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  • Yang Dinghua, Zhou Le, Zhang Xianfeng, Ma Lu, Shen Xin, Du Zhaohui
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    In this paper, based on the free vortex wake model and the geometrically exact beam model, the aeroelastic characteristics of wind turbines under yaw conditions are studied, and the combined effects of platform yaw motion and blade deformation on wind turbine performance are discussed. The results show that the yaw motion of the floating platform may cause the fluctuation in blade normal inflow, which affects wind turbine performance. Under yaw condition, the average output power of the rotor is significantly higher than that under the fixed condition, while the flexible deformation of the blade will reduce the load of the rotor. The yaw motion of the floating platform causes the uneven distribution of the load on the rotor plane and the fluctuation of the pitch and yaw moments. Under yaw conditions, blade deformation is affected by the combined effects of gravity and floating platform motion. Blade flapwise deformation will reduce the normal inflow velocity of the blade, while torsional deformation will reduce the blade angle of attack, resulting in the decrease of wind turbine load.
  • Yang Yi, Liu Shi, Tao Tao, Yin Jianfei, Zhu Zhicheng, Li Deshun
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    To address the vibration sensitivity of semi-submersible floating offshore wind turbines (FOWTs) in marine environments, this study proposes a tuned mass damper (TMD) design method based on multi-degree-of-freedom (DOF) coupling effects to achieve precise low-frequency vibration control. Using the International Energy Agency (IEA) 15 MW semi-submersible FOWT model, numerical simulations and multi-DOF free decay tests were conducted. The dominant frequencies of key modes under multi-DOF coupling effects were identified through time-domain and frequency-domain analyses. This study evaluates the vibration control effects of TMDs with optimized parameters at different installation locations on the coupled platform and tower-top vibrations. The results indicate that platform-mounted TMDs significantly outperform nacelle-mounted TMDs in suppressing low-frequency vibrations, reducing tower-top longitudinal displacement and platform pitch angle amplitudes by 30.6% and 46.2%, respectively. Furthermore, the dual installation of TMDs on both the platform and nacelle demonstrates potential in enhancing overall vibration control performance.
  • Zhang Zhenhua, Zeng Linjun, Huang Weiying, Xu Jiani, Qing Wei, Li Huanting
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    To improve the utilization rate of renewable energy sources and reduce the carbon emission level of integrated energy system (IES), an optimal scheduling strategy of IES is proposed based on dynamic carbon emission factor and wind power prediction. Firstly, an IES architecture is established, along with a carbon emission measurement method that considers dynamic emission factors. Secondly, a combined wind power interval prediction model based on CNN-BiGRU-Attention is proposed in view of the safe and stable operation problems caused by the volatility and intermittency of wind power generation. The effectiveness of this model is verified by comparing the proposed model with a variety of traditional prediction models for wind power prediction. Finally, taking the minimization of the sum of energy purchase cost, carbon trading cost and wind curtailment cost as the low-carbon economic operation goal, the proposed scheduling strategy is verified to be able to effectively reduce carbon emissions while lowering the system operation cost by setting up several operation scenarios.
  • Su Yi, Tian De, Wang Yong, Meng Huiwen
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    Aiming at the problem of vortex-induced interference between blade and tower during wind turbine shutdown, a two-dimensional single-degree-of-freedom airfoil-cylinder coupled vortex-induced vibration simulation model is established based on computational fluid dynamics (CFD) method and nested grid technology. The influence of the airfoil wake on the vortex-induced characteristics of the cylinder under two working conditions of fixed and free vibration of upstream airfoil are analyzed. The simulation results show that the combination of CFD and nested grid technology can effectively overcome the problems of grid distortion and negative grid. The amplitude ratio peak error of single cylinder between simulation results and experiments is reduced by 75%, and the calculation accuracy is higher. The wake of the fixed upstream airfoil significantly changes the vortex-induced vibration characteristics of the downstream cylinder. The frequency-locked interval of the downstream cylinder lags behind and the root mean square ClRMS of the lift coefficient in the interval increases, resulting in a peak amplitude ratio of 70%-92% higher than that of a single cylinder, and increases with the spacing ratio. When the upstream airfoil vibrates freely, the disturbance of the downstream cylinder to its wake vortex shedding causes the maximum amplitude ratio of the airfoil to be 14%-30% lower than that of the single airfoil. Due to the impact of only the upstream part of the vortex, the amplitude ratio peak in the frequency locking range of the downstream cylinder is 27%-33% higher than that of the fixed upstream airfoil, and the “P+S” type vortex shedding mode is formed at high reduced speed. In addition, the interference effect between the airfoil and the cylinder under the two working conditions decreases with the increase of the spacing ratio.
  • Xu Jun, Wang Dan, Yu Quanfu, Zhang Futai, He Guangling, Wu Qiang
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    This study presents a reliability analysis method for determining the lateral drift angle limit of hybrid tower wind turbines. The uncertainties inherent in the design of hybrid tower wind turbines are thoroughly considered, and Sobol sampling is employed to generate samples of random variables. An integrated coupled nonlinear dynamic model of the hybrid tower wind turbine system is developed to conduct nonlinear dynamic response analyses, enabling the extraction of key physical quantities such as tower stress and lateral drift. The harmonic transform method is then utilized to reconstruct the probability distributions of stress extremes in the tower and lateral drift extremes at the tower top. Based on these results, a structural reliability analysis is performed with stress as the performance indicator, and the lateral drift limit is determined using an inverse reliability analysis approach. The lateral drift angle limit is further calculated by incorporating the total height of the tower structure. Numerical analysis results demonstrate that the lateral drift contribution from the concrete section is smaller than that from the steel section. For hybrid towers with varying height ratios between the concrete and steel sections, it is recommended to calculate the lateral drift limits for each section separately and combine them to determine the overall lateral drift limit, which is then normalized by the total tower height to derive the final lateral drift angle limit.
  • Yang Rui, Wen Liang, Zeng Xueren, Fang Liang, Bao Guangchao, Tian Nan
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    This study investigates the impact of typhoon-induced severe convective weather on the structural performance of blades, focusing on blade 1 at a 180° phase angle and blade 2 at a 60° phase angle of the NREL 5 MW wind turbine. The research considers normal wind conditions, typhoon conditions, and wind-rain coupled conditions with varying rainfall intensities. The study analyzes surface pressure, displacement, and equivalent stress at different blade sections, through numerical simulations based on WRF and ANSYS platforms. The results show that the aerodynamic characteristics of both blades are similar and stable under normal wind conditions. Under typhoon and wind-rain coupled conditions, significant differences in aerodynamic loads appear between blade 1 and blade 2, particularly at high rainfall intensities, where the aerodynamic pressure on blade 1 becomes more complex. Across all wind conditions, blade 2 exhibits greater variations in axial and circumferential displacement than blade 1, mostly in the negative direction, indicating that it experiences more complex force interactions. Additionally, blade 2 consistently has higher total displacement and equivalent stress than blade 1, especially under extreme wind conditions, posing greater structural safety challenges. These findings highlight the significant impact of typhoon-induced severe convective weather on wind turbine blade structural performance. Therefore, in wind turbine design and operation, it is crucial to account for extreme weather effects on blade stability and reliability to ensure safe and stable turbine operation.
  • Xiong Xiaofeng, Jia Shihui, Chi Xiaoni, Li Gaoxi
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    To address the problem of low wind speed prediction accuracy caused by the inherent randomness and volatility of wind speed, this study proposes a hybrid prediction method based on multivariate variational mode decomposition (MVMD) with parameters optimized by an improved sparrow search algorithm (ISSA) and the Timemixer model. First, data variables are reduced in dimensionality using the maximum information coefficient (MIC) and the random forest algorithm. Then, the ISSA, which incorporates Logistic-Tent chaotic mapping and a golden sine strategy, is utilized to automatically optimize the number of modes and the penalty coefficient in MVMD. The optimized MVMD decomposes the data into multiple intrinsic mode functions (IMFs), and each IMF is individually predicted using the TimeMixer model. Finally, the IMF predictions are aggregated to produce the final wind speed forecast. Experimental results on two datasets demonstrate that, compared with other prediction models, the proposed model achieves higher prediction accuracy and exhibits good performance in wind speed forecasting.
  • Pan Yiyu, Sun Jingwei, Deng Yong, Chen Qingwei, Zhang Zhaohuan, Chen Yan
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    To calculate the hydrodynamic loads on floating offshore wind turbine (FOWT) platforms under coupled wind-current-wave interactions and investigate how wind and currents influence wave spectra, wave elevations, and hydrodynamic load characteristics, this study establishes a coupled wind-current-wave interaction model. The model systematically explores the evolution trends of wave spectra, wave surfaces, and hydrodynamic loads under increasing wind speeds and current velocities. Maximum hydrodynamic loads and load ranges are calculated, with the Random Forest algorithm employed to evaluate the correlation between marine environmental factors and hydrodynamic load characteristics. Key findings include: Increasing wind speed amplifies wave spectral peaks and intensifies wave oscillations; Stronger currents suppress wave spectral peaks and dampen wave oscillations; Both elevated wind speeds and current velocities increase hydrodynamic load magnitudes, and are important factors affecting the maximum and range of the hydrodynamic loads. This research provides a data-driven framework for coupled environmental load analysis, offering insights for FOWT platform design and survivability assessments in complex marine conditions.
  • Wei Haitao, Cao Liyuan, Huang Xinyi, Li Chunxiang
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    To address the issue of low computational efficiency in simulating non-stationary wind fields with time-varying coherence functions for transmission tower-line systems, an efficient time-frequency interpolation-based stochastic wave spectral representation method (TFI-SWSRM) is proposed. First, the wavenumber-frequency joint power spectrum (WFJPS) that incorporates the time-varying coherence function is constructed at non-uniformly distributed time-frequency interpolation nodes. Second, the proper orthogonal decomposition (POD) method is adopted to decompose the WFJPS into independent components. Subsequently, non-stationary wind speed time series are generated by integrating the time-frequency interpolation (TFI) technique with the fast Fourier transform (FFT) algorithm. Finally, the effectiveness of the proposed TFI-SWSRM is verified by simulating both non-stationary homogeneous and non-stationary non-homogeneous wind fields with time-varying coherence functions in transmission tower-line systems. A comparative analysis of computational cost between the proposed TFI-SWSRM and conventional simulation methods demonstrates that the proposed approach achieves substantially higher computational simulation efficiency.
  • Wang Yajun, Zhang Xu, Zheng Wenjin, Fang Shibiao
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    To address the frequent collision accidents among pedestrians, vehicles, and heavy equipment at renewable energy construction sites (wind farms/solar power stations), this study develops a high-precision real-time traffic monitoring system by integrating machine vision and deep learning technologies within a multi-modal monitoring framework to enhance dynamic risk perception. An improved YOLO v8 model is employed for object detection, combined with binocular stereo vision and Kalman filtering algorithms for three-dimensional positioning and trajectory prediction. A prototype system is deployed at the Lingbi County wind power project, with its performance validated through real-time video streams. Experimental results demonstrate that the system achieves pedestrian detection accuracy of 96.2% and vehicle recognition accuracy of 98.5% under construction scenarios, while simultaneously tracking transport vehicle dynamic parameters (distance error ±3.0 m, speed error ±1.2 km/h), and improves response speed by 40% compared to conventional monitoring approaches. This study provides a universal solution for intelligent safety management at renewable energy construction sites, facilitating the deep integration of smart construction sites and intelligent transportation systems.
  • Lin Wenlong, Gu Xiaoqiang, Shi Zhenhao, Xiao Jiandong, Lin Yifeng, Hu Jing
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    To account for the influence of bucket-soil contact conditions on its bearing capacity, this study introduces the bucket-soil interface strength to reflect the contact characteristics between the bucket and soil. Through a series of three-dimensional finite element analyses, the impact of the skirt length, soil undrained shear strength and bucket-soil interface strength on the bearing capacity of the bucket foundation under combined vertical (V), horizontal (H), and moment (M) loads in normally consolidated soft clay is investigated. The normalized V-H-M failure envelope is used to describe the bearing capacity of the bucket foundation under V-H-M combined loads. The findings indicate that the bucket-soil interface strength can significantly influence the bearing capacity of the bucket foundation. Through a large number of finite element analyses, the formulas for the uni-directional ultimate bearing capacity of the bucket foundation considering the bucket-soil interface strength, as well as a normalized V-H-M bearing capacity envelope expression, are established. By comparing the finite element analysis results with the strength envelope expressions from this study and existing literature, the validity of the strength envelope proposed in this research has been verified. The results further indicate that the envelope can reasonably reflect the effect of the bucket-soil contact characteristics on the bearing capacity of bucket foundations under coupled V-H-M loading.
  • Zhang Ningning, Xiao Pengcheng, Tian Linlin, Zhao Ning, Zhao Jun, Zhao Zhangle
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    This paper proposes a novel power generation assessment method based on a three-dimensional wake model and a new equivalent wind speed calculation model, aimed at overcoming the insufficient accuracy of traditional methods caused by the inadequate consideration of wind shear and wake effects. The method is validated using measured power generation data from two typical wind farms under different wind conditions. The results indicate that for the Horns Rev wind farm with a conventional layout configuration, the novel power generation assessment method demonstrates significantly improved evaluation accuracy compared to conventional approaches. The error margins relative to measured data range from 2.43% to 7.68%, slightly outperforming the error range of 1.65% to 11.89% observed in large eddy simulation (LES) results. In the case of the closely-packed Lillgrund wind farm, the new assessment method exhibits superior comprehensive evaluation performance under two wind direction conditions when compared to traditional methods, while maintaining overall accuracy comparable to LES numerical simulation results. Overall, the new assessment method comprehensively considers the coupling effects of wind shear and wake interactions on the complex flow field within the wind farm, thereby enabling more accurate prediction of the actual power generation of the turbines.
  • Guo Xiuxin, Xu Chengshun, Ling Weiyu, Liu Kaiyuan, Jia Kemin
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    Offshore wind turbine structures are continuously subjected to long-term wind and wave horizontal loads, as well as seismic forces during their service life. To explore the seismic response behavior of these structures under the combined effects of wind, wave, and seismic loads, a large-scale physical model test was conducted. The results indicate that under long-term cyclic loading from wind and waves, the pile-soil interaction effect is pronounced within a range of one pile diameter (1D) around the pile, while the interaction can be neglected beyond five pile diameters (5D). Under the combined influence of seismic and wind-wave loads, the soil acceleration around the shallow piles is significantly amplified compared to when only seismic loads are applied. The effect of wind and wave loads may extend to a depth of up to four times the pile diameter. Additionally, under the joint action of seismic and wind-wave loads, the accelerations, displacements, and bending moments at the pile top and tower top are notably higher than those observed with seismic loads alone, with the structure exhibiting an overall tilt. The coupling effect observed in the wind turbine’s single-pile foundation under the combined horizontal and seismic loading has a considerable impact on the safety and serviceability of the turbine. This effect should be carefully considered in the seismic design of wind turbine foundations.
  • Xin Ziyu, Jiang Meng, Shi Wei, Ren Yajun, Zhang Yu, Li Xin
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    This study conducts an in-depth analysis of the dynamic response characteristics of a novel Triple-Spar floating wind turbine platform, which integrates the advantages of both semi-submersible and Spar-type platforms, under the environmental conditions of the floating wind farm in Wanning, Hainan, China. Using the commercial software ANSYS AQWA, a numerical model of the floating wind turbine was developed to systematically investigate the effects of combined wind, wave, and current forces on the mooring system and platform motion response under both uniform and shear flow conditions. The results indicate that, under the environmental conditions of the South China Sea wind farm, the Triple-Spar platform demonstrates excellent motion performance in the heave direction. However, the presence of shear flow induces significant variations in the heave and pitch responses, posing new challenges to platform stability and safety. This study provides valuable theoretical insights and data support for the design and operation of offshore wind farms in China.
  • Yue Minnan, Su Huanhuan, Liu Kunpeng, Fan Li, Li Chun, Yang Mei
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    To investigate the influence of different aperture patterns on the protective performance of fractal aperture proteective devices for offshore wind turbines, in this paper, the collision process between a 5000 t ship and a 4 MW single-column three-pile foundation offshore wind turbine at a speed of 2 m/s is simulated by using LS-DYNA software based on nonlinear dynamics theory. Through the analysis of indicators such as kinetic energy dissipation, contact force, system damage, and tower top dynamic response, the following conclusions are drawn: the traditional triangular aperture protective device exhibites the lowest kinetic energy dissipation rate, while the rectangular aperture device demonstrates the highest rate; under the protection of the three devices, the tower structure incurred a certain degree of plastic deformation, with the triangular aperture causing the least plastic strain and the rectangular aperture causing the highest; concerning tower top dynamic response, the triangular and circular aperture devices showe similar overall values, but the maximum response of the triangular aperture is slightly lower than that of the circular aperture, while the dynamic response of the rectangular aperture is comparatively more pronounced. The fractal protective device with a triangular aperture pattern delivers the best comprehensive protective performance, offering a higher level of safety assurance.
  • Sun Yuyuan, Wang Xiaodong, Fu Deyi, Liu Yingming, Yang Bin
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    In order to realize the rapid and convenient quantification of wind turbine load under voltage disturbance condition, this paper proposes a time series load prediction method for key parts of wind turbine driven by operation data based on TCN-iTransformer. Firstly, the correlation analysis of different measured quantities of load and power grid data and wind turbine operation data of key parts of the wind turbine is carried out to extract the key characteristic quantities. The outstanding local time series feature extraction ability of the temporal convolution network is used to capture the short-term dependencies in the time series data, and the iTransformer algorithm is used to model the nonlinear complex interaction between variables, so as to achieve accurate prediction of the load of key parts of the wind turbine. The analysis of the results based on the measured data shows that the prediction accuracy of the TCN-iTransformer model is significantly higher than that of the Transformer, iTransformer and other models.
  • Li Lianbing, Luo Wei, Cheng Qing, Su Wenyong, Chen Yece, Lu Zhihui
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    To further enhance the accuracy of wind power prediction, a prediction model based on rolling decomposition and CLA-XGBoost is proposed. Firstly, the wind power data is preprocessed, and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to perform rolling decomposition on the wind power sequence, decomposing it into multiple modal variables. Secondly, each modal variable is inputted into the spatiotemporal parallel model (CLA) and the XGBoost model for training. Finally, the prediction results of the two models are weighted and averaged based on the reciprocal of the mean squared error (MSE) during the training process. The comparative analysis results indicate that this model can overcome the disadvantage of information leakage in test data in traditional decomposition methods, and on this basis, it can significantly improve prediction accuracy and robustness, providing more precise prediction information for the safe and stable operation of high-proportion renewable energy power systems.
  • Zhu Binglei, Wang Anran, Zhao Zhengyang, Luo Ting, Wang Jiangjie, Zhang Kai
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    To improve the accuracy and stability of wind farm power forecasting, a wind farm prediction model based on a long short-term memory neural network (LSTM) optimized by an improved osprey optimization algorithm (IOOA) is proposed. A combination of bidirectional long short-term memory network (BiLSTM) and convolutional neural network (CNN) is used to leverage the advantages of both models to capture the complex nonlinear spatiotemporal relationships presented by wind farm power data, mine the hidden patterns and features, and introduce an attention mechanism to adjust the weight of the captured information. To address the issues of slow convergence and susceptibility to local optima in the osprey optimization algorithm (OOA), a method using the Levy flight strategy and Logistic chaotic mapping is proposed to enhance the OOA's search capability, after which the improved osprey optimization algorithm (IOOA) is used to optimize the model parameters. The results show that, compared to the better-performing CNN-BiLSTM-Attention prediction model, the proposed IOOA-CNN-BiLSTM-Attention model reduces the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 55.287%, 54.352%, and 32.283%, respectively, while R2 improves to 96.635%, demonstrating better stability and forecasting accuracy.
  • Liu Gongpeng, Gao Yifeng, Ai Congfang
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    To assess the restraint capabilities of the mooring system on the OC-4 DeepCwind wind turbine platform, a simulation-based investigation was carried out to analyze the motion response of the OC-4 wind turbine system when subjected to the combined effects of wind and waves. The study focused on exploring the impact of various heavy blocks, floating blocks, and mid-section cable configurations on the platform's dynamic response and mooring loads. Using the original catenary mooring as a benchmark, the roles of different influencing factors within the wind turbine system were comparatively analyzed through calculations utilizing the FAST and AQWA's wind-wave-elastic coupling framework (F2A). The findings reveal that incorporating heavy blocks into the mooring system can diminish the platform's low-frequency response, effectively curbing the amplitude of platform motion in multiple directions. Although adding floating blocks to the mooring system can mitigate platform motion and mooring loads in certain directions, it significantly increases the amplitude of motion and mooring loads in the remaining directions. By strategically coordinating the placement of heavy and floating blocks, it is possible to simultaneously mitigate the platform's low-frequency response and high-frequency resonance phenomenon.
  • Yang Xinmeng, He Lun, Zhang Ruixing, Huang Zenghao, An Liqiang
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    Aiming to address the demands for rapid response prediction and parameter inversion of floating wind turbines, this study proposes a mesh-free solution method based on physics-informed neural network(PINN) for floating wind turbine dynamics. By embedding the six-degree-of-freedom dynamic equations of the floating wind turbines into the loss function and optimizing the spatial domain, the method achieves efficient prediction of dynamic responses and parameter inversion. The results demonstrate that, compared to traditional numerical methods, PINN significantly enhances computational efficiency while maintaining high accuracy, effectively overcoming error accumulation under large time-step conditions. Furthermore, the inverse PINN (IPINN) architecture achieves system parameter inversion with errors controlled within 1%, demonstrating precise identification capabilities.
  • Wu Hao, Zhang Sai, Gao Shangsong, Li Liuyang, Gao Yifan, He Fang
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    This study carried out physical model experiments to identify the characteristics of horizontal wave loads on jacket foundations and investigate the variation patterns of wave loads under different wave conditions. A reliable and well-fitted prediction model was obtained by training a support vector machine (SVM) based on the physical model experimental dataset. Combined with the SVM prediction model, a global sensitivity analysis was performed using the Sobol method with respect to water depth, wave period and incident wave height, further clarifying the influence of each parameter on the horizontal wave loads of jacket foundations. It was found that among the parameters, incident wave height exerts the most significant effect on horizontal wave loads.
  • Li Lanqing, Li Yan
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    A short-to medium-term wind power prediction model based on deep fuzzy neural network(DFNN) is proposed. Firstly, the fuzzy rules and the number of rules are determined by introducing an effectiveness function to construct an adaptive fuzzy C-means clustering algorithm, reducing the impact of manually setting the number of clusters on the clustering performance of traditional fuzzy C-means clustering algorithms; Secondly, an improved quantum genetic algorithm is adopted to optimize the consequent layer parameters of the deep fuzzy neural network. This algorithm increases the diversity of gene collapse results and global optimization ability by controlling the numerical range of adaptive dynamic rotation angle during the initial iteration; Finally, the proposed model was applied to short-term and medium-term wind power prediction, with wind speed, wind direction, and environmental temperature as inputs and predicted power as outputs. The four models were tested, and the results showed that the proposed model had high accuracy in short-term wind power prediction during the selected time periods in spring and summer, as well as on the medium-term time scale, verifying the feasibility of the method.
  • Wang Jinke, Du Jing, Han Jia, Wang Shuang
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    To meet the comprehensive operating requirements of wind turbine bearings, this study develops a nonlinear spring-global finite element coupling model to analyze the dynamic load distribution. A raceway-oil groove sub-model is employed to accurately characterize the effect of case hardening depth on the stress field of oil grooves. Furthermore, a method for determining the critical threshold of case hardening depth for oil grooves is proposed. The results indicate that the equivalent stress of the oil groove in wind turbine pitch bearings decreases exponentially with increasing case hardening depth, while the fatigue damage exhibits a two-stage pattern of steep decline followed by gradual stabilization. Considering the high reliability and manufacturing economy of wind power equipment, the minimum case hardening depth for the oil groove is determined to be no less than 3.08 mm.
  • Zhou Dong, Luo Yuxiao, Heng Junlin, Dai Kaoshan, Liu Yangzhao, Qiu Keyi
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    To evaluate the time-dependent reliability evolution mechanism of flange joints in innovative truss-type wind turbine support structures under fatigue loads, this paper establishes a fatigue failure probability prediction model for high-strength bolts based on multi-physics coupled simulation. The effect of different bolt failure numbers on the ultimate bearing capacity of flange joints is analyzed. Considering the wind speed-direction joint distribution, the time-varying fatigue damage characteristics of high-strength bolts during service are quantified, and the evolutionary trend of structural failure probability for flange joints is predicted. The results show that the fatigue damage of connecting bolts in truss-type wind turbine flange joints accumulates continuously with service time, and the failure probability increases significantly after 15 years of service, leading to an obvious reduction in the overall bearing capacity of the joints. Combined with field measured wind data, a fatigue damage evaluation method suitable for truss-type wind turbine support structures is proposed, which reveals the influence law of cumulative bolt fatigue damage on the flange joint reliability and its time-dependent evolution of flange joints.
  • Liu Shujun, Yu Xiaoqing, Du Xiaoze, Wu Jiangbo
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    This paper compares and analyzes the performance of single models, stacked hybrid deep learning networks, and parallel hybrid deep learning networks for ultrashortterm wind power forecasting. Hybrid forecasting models based on Long ShortTerm Memory (LSTM), Temporal Convolutional Networks (TCN), and Recurrent Neural Networks (RNN) are developed, including stacked architectures, hybrid parallel architectures, and dualparallel architectures. Experimental results show that parallelarchitecture deep learning networks for wind power forecasting significantly outperform stackedarchitecture models in terms of prediction accuracy and computational efficiency. Under the same hyperparameter configuration, the parallelarchitecture PATCNLSTM model achieves the best performance among nine models. Compared with the corresponding stackedarchitecture model, it reduces the mean absolute error (MAE) by 10.89%, the root mean square error (RMSE) by 5.96%, increases the coefficient of determination (R2) by 0.35%, and shortens the training time by 31.99%. Compared with single models, the PATCNLSTM model reduces MAE by 27.76% on average, RMSE by 35.17% on average, and increases R2 by 1.20% on average. Furthermore, a comparative analysis of the prediction performance and efficiency of hybrid parallel and dualparallel architecture models is conducted using TPE Bayesian optimization. The results indicate that among the compared models developed in this study, TPEPATCNLSTM achieves the best prediction accuracy and training efficiency.
  • Fan Qinglai, Fan Ge, Yin Jiaqi, Li Xiang
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    The mechanical response of pile-bucket hybrid foundations subjected to horizontal loading is systematically investigated using three-dimensional nonlinear finite element analyses. Firstly, the existing centrifuge model test is numerically simulated, and the calculated results are in good agreement with the relevant experimental data. Then the effects of bucket size, soil strength parameters and horizontal load eccentricity on the horizontal bearing capacity of pile-bucket hybrid foundations are analyzed, revealing the failure mode and load-sharing mechanism of pile-bucket hybrid foundations in sand. The results indicate that the failure mode of the pile-bucket hybrid foundation is mainly rotational instability. The horizontal bearing capacity of the foundation increases with the increase of the bucket diameter. The trend of increasing horizontal bearing capacity with the increase of bucket height becomes less obvious when the bucket height exceeds 1.5 times the pile diameter. In the pile-bucket hybrid foundation, the bucket component primarily bears the horizontal force, while the pile component mainly withstands the bending moment. It is more advantageous to exert the horizontal resistance capacity of the bucket component in denser sand. Through data fitting of numerical simulation results, a lateral bearing capacity prediction formula is proposed that incorporates bucket dimensions, sand strength parameters, and load eccentricity.
  • Xu Shan, Gong Shuguang, Liu Qiliang, Xie Guilan, Liang Zhiwei
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    Bladeless wind turbines convert harvested wind energy into electricity through load damping. To enhance the energy harvesting efficiency of the system, an angular-velocity-proportional damping model is proposed, based on the nonlinear characteristics of vortex-induced vibration (VIV). This model includes two control parameters: the preset maximum damping ratio and the model exponent. The governing equations of the energy harvesting system with nonlinear damping and the efficiency calculation formula are derived. A numerical study is performed to analyze the influence of the control parameters on the VIV characteristics of the energy harvesting cylinder and the system's energy harvesting efficiency. The results indicate that under the design wind speed, when the model exponent is greater than zero, the lateral oscillation amplitude of the energy harvesting cylinder gradually increases while the oscillation frequency decreases, but remains close to the system’s natural frequency. All cases exhibit a distinct "single-peak" characteristic. For different combinations of control parameters, the energy harvesting cylinder demonstrates a "dual-frequency" resonance in its VIV response, with trajectories forming "figure-eight" patterns. With a preset maximum damping ratio of 0.03 and model exponent values of 0.5, 1.0, and 2.5, or with a preset maximum damping ratio of 0.04 and exponent of 2.5, a significant improvement in the system’s energy harvesting efficiency is observed. Particularly, when the preset maximum damping ratio is 0.03 and the model exponent is 2.5, the system’s energy harvesting efficiency increases by 43.91% compared to the constant damping model.
  • Yang Kun, Liu Yanbin, Li Xuying, Ling Jinbo, Bai Ang, Yan Wenxin
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    In addressing the issues of high energy consumption and wear in cylindrical roller bearings of wind power gearboxes due to excessive friction torque, a novel design featuring flexible cables to support the bearing is proposed. First, a bearing dynamic simulation model is established by integrating Hertz contact theory with elastohydrodynamic lubrication theory. Subsequently, the Masjedi wear model and single-point observation method are utilized to establish criteria for evaluating flexible rope cable wear and to investigate the impact of rope cable groove depth and width on bearing friction and wear performance. Finally, the NSGA-Ⅱalgorithm is employed to optimize the geometric parameters of the rope cable groove, and the optimal solution is determined using the entropy weight method. The research indicates that optimal friction and wear performance of the bearing is achieved when the groove depth is 1.98 mm and the groove width is 1.60 mm. The optimized bearing exhibit lower friction torque compared to conventional circular pocket hole bearings under radial loads of 1000 N and rotational speeds ranging from 1000 to 5000 r/min, with reductions ranging from 28.34% to 41.83% and an average reduction of 35.40%.
  • Jia Zhaolin, Zhao Qixiang, Lian Jijian, Yang Defeng, Zhang Zheng, Wang Suli
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    This study presents a comprehensive analysis of fluidized stabilized soil (FSS) formulations integrating industrial by-products—ground granulated blast furnace slag (GGBS) and fly ash (FA)—as partial cementitious replacements. Experimental evaluations employed unconfined compressive strength (UCS) testing and scour resistance assays to quantify the influence of mix design parameters and curing duration on UCS, scour rate, critical shear stress ($ \tau_{c} $), and critical flow velocity (VC). Findings reveal that GGBS substitution significantly enhances both mechanical strength and marine erosion resistance, with a 1∶1 GGBS-to-cement blend achieving $ \tau_{c} $=17.18 Pa and VC=2.84 m/s after 24-hour curing, representing 67.87% and 43.31% improvements in VC and $ \tau_{c} $ respectively compared tocontrol specimens (C20). Conversely, FA contents exceeding 4% by mass demonstrated detrimental effects on both strength development and scour resistance. Empirical exponential correlations were derived between UCS,$ \tau_{c} $, and VC, forming the basis for a proposed predictive framework enabling rapid assessment of FSS scour resistance under hydraulic loading conditions.
  • Pan Chao, Lou Rui, Wang Fei, Tian Baotao
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    This study selects an actual case of a suction bucket jacket in a wind farm in the Dalian sea area,monitor key parameters such as the bucket pressure and velocity during the penetration process,and compares and analyzes them with the calculated values of DNV calculation to obtain the relationship and characteristics of resistance, pressure,and velocity during the penetration process in silty clay. The research results indicate that the DNV calculation of self-weight penetration depth into the soil is relatively accurate. The change of actual driving suction force inside the bucket shows three stages: increase,stability,and increase,with the stability stage accounting for the main part. The actual driving suction force during the initial stage of suction penetration is greater than the calculated value,and then continues to be lower than the calculated value. Continuous dynamic suction penetration can reduce the resistance to a certain extent. The suction bucket jacket should be continuously penetrated as much as possible during the actual operation The actual driving suction force of the final penetration is less than the calculated value,about 76.0% of the calculated value in silty clay. The suction penetration velocity shows a characteristic of rapid increase at first,followed by a decrease to a smaller stable velocity for continuous penetration.
  • Yan Wenshuai, Liu Xing, Ren Nianxin, Li Yanwei, Chen Chaohe
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    To achieve the efficient utilization of marine spatial resources, biological resources, and renewable energy, a novel integrated structure system that combines floating fish reefs with marine aquaculture platforms for wave energy extraction is proposed. Based on ANSYS-AQWA software, a coupled time-domain analysis model of this integrated wave energy structure system has been established, accounting for both multi-body hydrodynamic coupling effects and mechanical coupling effects.The research examines the dynamic behavior of the structural system in standard sea conditions and assesses its safety performance in extreme environments. The findings indicate that the integrated marine aquaculture platform, featuring floating fish reefs, can effectively reduce the amplitudes of motion responses while providing substantial energy supply and contributing to marine environmental remediation. Furthermore, under extreme conditions, the weighted anchoring system significantly mitigates the peak values of longitudinal motion responses and the maximum stresses on the anchor chains of the aquaculture platform.
  • Wang Zhenpeng, Lyu Changqi, Chen Min, Zhang Yaqun, Wang Wensheng, Sheng Songwei
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    A systematic study investigates the impact of abrupt seabed topography near reef areas on the hydrodynamic performance of oscillating buoy wave energy converters, using physical model experiments and computational fluid dynamics (CFD). In the wave flume experiments, a reef-flat model with a sloping seabed is constructed, with a flat seabed as the control group. The effects of incident wave height, period, and device deployment position on performance are analyzed. A numerical wave flume is developed, and its accuracy is validated through comparison with experimental data. The results indicate that, in the long-wave region, abrupt seabed topography significantly enhances the capture width ratio compared to flat seabed conditions.
  • Zhang An, Li Yanni, Tao Ji, Cao Feifei, Shi Hongda
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    Most existing studies on wave energy converters (WECs) have failed to account for structural damage induced by wave run-up on the buoy surface. To address this issue, the modeling test is conducted on a segmented annular buoy to quantify the influences of wave period and draft on wave run-up characteristics. The experimental results demonstrate that wave run-up is significantly amplified when the wave period approaches the natural period of the segmented annular buoy. Wave run-up also exhibits a monotonic increase with growing draft, where the magnitude of the increase is strongly dependent on the wave period. Furthermore, analysis of wave field distribution around the buoy under specific conditions demonstrates that the heave motion of the segmented annular buoy predominantly affects the wave field on the wave-ward side, while its effect on the leeward is restricted to a narrow range.
  • Li Zhiming, Xia Xiangyang, Zhao Xiaoyue, Lu Qifu, Xia Tian, Cai Yukuan
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    Aiming at the problems of insufficient visualization of the aging process of lithium-ion batteries and difficulty in accurately estimating remaining useful life, this paper proposes a lithium-ion battery remaining useful life prediction method based on phase plane two-dimensional sample entropy (PP-2DSE). The method first visualizes the aging process by constructing the battery phase plane (PP) and transforms battery monitoring data into a two-dimensional time series; secondly, calculates the two-dimensional sample entropy (2DSE) value, thereby quantifying the battery aging process into an observable indicator; and finally, through the validation of the actual operation data of an energy storage plant in Hunan Province and the NASA public dataset, the results show that, with gradual battery aging, the peak value in the specific interval of the PP-2DSE curve appears earlier and its amplitude increases, and the ratio of the peak value to its corresponding time is used as a health factor to achieve accurate prediction of the remaining battery life. The method shows good effectiveness and robustness in both qualitative and quantitative analyses and provides a new solution for lithium-ion battery remaining useful life prediction, which can provide accurate information for safe scheduling and control of energy storage plants by power departments under different working conditions.
  • Zhang Xiuqi, Li Hui, Lai Wei, Liao Qinglong, Li Yongfu
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    In response to the issue of one-sided assessment results caused by the disconnection between optimization scheduling objectives and evaluation indicators in the current comprehensive evaluation system for energy storage, this study focuses on compressed air energy storage (CAES), a key flexible resource in the new power system. It proposes a comprehensive quantitative evaluation method for the grid-connected benefits of CAES based on multi-dimensional indicators and multi-time scale coupled collaborative optimization scheduling. First, a multi-dimensional evaluation indicator system is constructed, which includes economic, low-carbon, and stability indicators. Next, these indicators are separately set as objectives for day-ahead, intraday, and real-time scheduling, establishing a collaborative optimization scheduling model that couples multi-dimensional indicators with multi-time scales. Furthermore, a quantitative analysis model for each evaluation indicator of CAES is developed based on scenario comparison methods, and a comprehensive evaluation model is constructed using the Analytic Hierarchy Process, entropy weight method, and Technique for Order Preference by Similarity to Ideal Solution. Finally, a comprehensive evaluation study is conducted on the grid-connected operational benefits of CAES under different operating scenarios. The results indicate that, compared to traditional comprehensive evaluation methods that focus solely on economic optimization scheduling based on day-ahead considerations, the proposed evaluation method achieves a higher comprehensive evaluation score and effectively addresses the issue of excessively high scores in the economic benefit dimension found in traditional evaluation methods, achieving a balanced consideration of multi-dimensional evaluation indicators.
  • Han Zhonghe, Deng Xiaoyu, Li Chen, Li Hengfan
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    This study focuses on a triple-tube phase change thermal storage unit and first analyzes the evolution of liquid fraction and average temperature during the heat release process for single-tube and double-tube configurations at 30%-80% filling ratios. It compares differences in total heat released, solidification time, and specific heat release rate. Second, the study selects 30% and 80% PCM filling ratios to investigate the evolution of liquid fraction and vorticity contours during heat release in single-tube and double-tube configurations, aiming to elucidate their heat transfer mechanisms. Furthermore, using the CRITIC method and VIKOR method, it evaluates the heat release performance of different configurations at various filling ratios, identifying the optimal model as the single-tube configuration with 80% PCM filling ratio. Finally, it examines the impact of PCM radial position on heat release performance for the optimal model. The results indicate that increasing PCM filling ratio enhances total heat released but reduces specific heat release rate. When the filling ratio exceeds 60%, the single-tube configuration not only benefits from larger heat transfer area and superior thermal conductivity but also accelerates vortex generation and diffusion within the PCM, thereby strengthening convective heat transfer and significantly outperforming the double-tube configuration. Under constant outer tube diameter and PCM filling ratio, radially positioning PCM closer to the outer wall increases heat transfer area and improves thermal efficiency. The closer the PCM is to the outer wall, the shorter the solidification time and the better the heat release performance.
  • Yu Ping, Wang Hao, Cao Jie
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    To more accurately predict the remaining useful life (RUL) of lithium-ion batteries and address the prediction inaccuracy caused by the capacity recovery phenomenon, this paper proposes a hybrid prediction model that integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, a Stacked Sparse Autoencoder (SSAE), an improved Temporal Convolutional Network (LATCN), and an improved Transformer (SWFormer). The model first decomposes the data using the CEEMDAN algorithm, and then employs the stacked sparse autoencoder to extract sparse features from the decomposed modal components. Subsequently, an improved dual-branch network model, LATCN-SWFormer, is constructed for life prediction, and a novel feature fusion attention mechanism is applied to fuse the features. Finally, a single Kolmogorov-Arnold Network (KAN) layer serves as an enhancement network for the final prediction of battery remaining useful life. The CALCE dataset and the NASA generalization experimental dataset are used for validation. Experimental results show that compared with XGBoost, BiTCN, and iTransformer methods, the proposed model reduces the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 73.6%, 68.6%, and 66.2%, respectively. In the capacity recovery stage of CS2_36 and B0006 batteries, the model achieves zero-error prediction, fully demonstrating its significant advantages in handling nonlinear degradation characteristics and capacity recovery interference. Meanwhile, during the rapid capacity decay stage, the model exhibits stronger fitting ability and achieves substantial error reduction on multiple sub-datasets, reflecting good generalization ability and robustness.
  • Yu Honghao, Sun Yutian, Zou Jibin, Jin Huiyong
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    Aiming at the problem of excessive winding loss in high-speed permanent magnet energy storage electric machine when rectifier loads are taken into account, an 8-pole 48-slot high-speed permanent magnet energy storage electric machine was taken as the research object. A mathematical calculation model of winding loss was constructed, and researches on the number of winding layers, parallel branches and transposed winding technology were carried out. The results show that increasing the number of winding layers and parallel branches of the electric machine can reduce the proportion of eddy current loss in the total loss and decrease the winding loss. By comparing and analyzing the in-slot transposition mode and the end twist transposition method of the winding, the effectiveness of the end twist transposition method in suppressing the AC loss of the winding was verified. For the common operating conditions of the electric machine, the electric machine performance and efficiency of different schemes were compared. The simulation results show that the proposed optimization method can reduce the winding loss and improve the efficiency of the high-speed permanent magnet energy storage electric machine considering the rectifier load, and the accuracy of the simulation analysis has been verified by experiments.
  • Zhang Jianpo, Chen Xiaoxuan, Jia Jiaoxin
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    With the rapid development of direct current transmission projects and new energy, the frequency regulation and voltage regulation capabilities of the direct current receiving-end power grids have been continuously weakened. The variable speed pumped storage unit (Variable Speed Pumped Storage Unit, VSPS), as one of the grid frequency regulation and peak shaving technologies, has received extensive attention and application. To address the frequency fluctuations and suppression issues at the direct current receiving end, this paper first elaborates on the frequency regulation mechanism and frequency response model of the VSPS; then, it designs the frequency response link of the VSPS based on active disturbance rejection control, establishes the state equation of active disturbance rejection control, determines the control parameters, and obtains the frequency correction amount of the receiving-end power grid; at the same time, an adaptive tuning strategy for the droop-inertia coefficient is added to the active/frequency coupling link to enhance the frequency fluctuation suppression effect of the power grid. Finally, the simulation results based on the PSCAD/EMTDC platform show that the adaptive control strategy can fully exert the frequency regulation capability of the VSPS, achieve effective support for the grid frequency, and has certain engineering application reference significance.
  • Bian Yangzhen, Yang Jian, Liu Weijie, Ding Yi, Zhong Quanming, Zhang Lin
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    Taking the hydrogen fuel cell for underwater vehicle enclosed space as the research object, COMSOL was used to study the effects of different serpentine channel types and design parameters on the performance of PEMFC. Study shows that the number of channels and the number of channel cells in the serpentine flow channel are important parameters affecting the fuel cell flow field performance in enclosed space, and the current density and power output of the fuel cell can be significantly improved by optimizing these parameters. The multi-channel serpentine flow channel can extend the reactant residence time and enhance the water management efficiency by adjusting the number of channels and reasonable symmetry design to achieve higher current density and power density, and meanwhile its characteristics of accelerating the water removal and enhancing the oxygen transport further enhance the overall fuel cell performance.
  • Li Jiawen, Su Tongze, Chu Jun, Tan Jinzhu
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    Based on the finite element method, a three-dimensional single-channel numerical model of a PEMFC was developed using COMSOL Multiphysics multiphysics simulation software, with a focus on investigating the deformation behavior of the gas diffusion layer (GDL) under bolt torque and its subsequent impact on the electrochemical performance of the PEMFC. The simulation results indicate that when the bolt torque reaches 1 N·m, the GDL begins to intrude into the flow channel, and the intrusion depth increases significantly with rising bolt torque, leading to a reduction in the effective cross-sectional area of the flow channel, increased gas transport resistance, and an elevated risk of flooding. Furthermore, bolt torque induces a pronounced non-uniform distribution of GDL porosity between the regions under the flow-field ribs and those beneath the flow channels, which in turn gives rise to spatially inhomogeneous distributions of oxygen concentration, water content, and current density. This ultimately reduces the mass transfer efficiency of the PEMFC and may even cause permanent damage to the cell performance. The study further reveals that as the bolt torque increases, the maximum power density of the fuel cell exhibits a trend of first increasing and then decreasing; when the GDL intrusion effect is taken into account, the peak power density of the fuel cell is achieved at a bolt torque of 2.5 N·m.
  • Li Pei, Wang Weiqing, Li Xiaozhu, Zhou Ming, Zhang Menglin
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    To further improve the accommodation capacity of renewable energy and promote the utilization of hydrogen energy, this paper proposes an optimal configuration method for multi-scenario adaptable hydrogen energy storage systems. Firstly, considering the volatile characteristics of wind and photovoltaic power output, ambiguity sets for wind and solar power are established by adopting Bayesian generalized regression and nonlinear interaction models based on the distributionally robust optimization theory of Bayesian inference, and the wind-solar power output under typical operating scenarios is quantitatively calculated. Secondly, a system framework that meets the multi-scenario power regulation requirements is constructed. On this basis, a capacity optimization configuration model of hydrogen energy storage is developed with the objective of minimizing the total operational and investment cost of the system. Finally, the effectiveness and feasibility of the proposed method are verified through a regional practical case study. The results demonstrate that the wind-photovoltaic output data obtained via the Bayesian method improve the rationality of hydrogen energy storage configuration schemes. The proposed system framework can effectively satisfy multi-dimensional power regulation demands and reduce the total system cost, which fully validates the outstanding application advantages of hydrogen energy in new power systems.
  • Zhang Zitong, Zhang Xuexia, Chen Weirong
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    In consideration of the characteristics of the thermal management system for proton exchange membrane fuel cell (PEMFC), including nonlinearity, strong coupling, and significant time delay, this study proposes a BP neural network PID control method (BP-PID) optimized by the bald eagle search algorithm (BES). This approach addresses the problenis of the conventional BP-PID method, such as slow learning rate and susceptibility to local optima, thereby enabling rapid adjustment of the fuel cell under varying operating conditions and reducing temperature fluctuations. A comparative analysis of four control methods—fuzzy PID, BP-PID, PSO-BP-PID, and BES-BP-PID—reveals the superior performance of the proposed BES-BP-PID method. Specifically, compared to BP-PID, BES-BP-PID reduces overshoot by 15.6% and decreases settling time by 29 seconds. Furthermore, when benchmarked against PSO-BP-PID, it achieves a lower root mean square error (RMSE) and smoother temperature transients.
  • Deng Wenqing, Yang Bo, Zhang Ting, Hong Qinglong, Huang Shenmin, Deng Fanfeng
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    In this study, based on Fourier transform infrared spectroscopy (FTIR) and cavity ring-down spectroscopy (CRDS), the performance of analytical methods for trace ammonia in hydrogen fuel was comprehensively and systematically investigated. The results showed that the two methods exhibited good linearity relationship was the concentration range of (0.05~1.0) μmol/mol, with correlation coefficient R2>0.999. The accuracy of the FTIR and CRDS methods was between -3% and 4%, and the repeatability (RSD) was<6%, and their detection limits were 2.7 nmol/mol and 0.29 nmol/mol, respectively. The analytical methods used for hydrogen fuel need to meet the requirements of GB/T 43361—2023, the method applicability of the methods was evaluated for the first time in this study according to GB/T 43361—2023, and the results showed that the above spectroscopic methods can meet the measurement requirements for trace ammonia in hydrogen fuel, and can be used for rapid and accurate detection of trace ammonia content in hydrogen used in fuel cells.
  • Wei Huili, Chang Guofeng, Xu Sichuan
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    In this work, staggered three-dimensional (3D) partially narrowed structures are added to improve the poor performance of conventional parallel straight flow field in proton exchange membrane fuel cells (PEMFCs). Numerical simulations using a 3D non-isothermal CFD model are employed to investigate the mass transfer mechanisms of this novel flow channel configuration. Results show that, compared with the parallel straight flow channels, the 3D partially narrowed flow channels increase the vertical oxygen concentration gradient between the channel and the gas diffusion layer (GDL), and generate high-speed convective mass transfer within channels and between adjacent channels, thereby effectively enhancing the oxygen concentration in the GDL and PEMFC performance. When the narrowed section height decreases, PEMFC performance initially improves and then significantly decreases. The optimal performance is achieved when the narrowed section height is 0.3 mm. Compared with the parallel flow channels, at this configuration, the output current density and system net power of PEMFC at 0.54 V increase by 18.14% and 17.61%, respectively.
  • Yao Ye, Ren Xiaoli, Song Jixiang, Deng Ziwei, Yu Zhenzhen, Shao Shuqin
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    This paper proposes a large-scale integrated HDPE floating block structure. Utilizing the Volume of Fluid (VOF) method, a numerical wave tank is constructed, and a hydrodynamic model of the floating structure is developed by coupling computational fluid dynamics (CFD) with rigid body dynamics. The study investigates the motion response and mooring force characteristics of the floating structure under inshore sea conditions. The results indicate that the maximum surge, maximum heave, X-direction velocity, and Z-direction velocity of the floating structure are positively correlated with wave height. The maximum surge and maximum heave of the floating structure increase with an increase in the wave period. Higher wave heights and shorter wave periods lead to greater fluctuations and the higher amplitudes of the mooring forces experienced by the floating structure.
  • Zhou Xuesong, Geng Shengyi, Ma Youjie, Chen Yunfei, Ma Licong, Li Shuang
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    Due to the influence of external conditions such as irradiance, the maximum power point of the photovoltaic system fluctuates frequently, and the traditional adjustment method is slow in tracking speed, poor anti-interference ability, and parameters that are difficult to adjust parameters, resulting in violent power fluctuations in the power of the photovoltaic power generation system. Therefore, This paper proposes an AC reinforcement learning dynamic optimization for active disturbance rejection power tracking control of photovoltaic systems. Firstly, variable step size P&O is used to achieve MPPT control, and then linear active disturbance rejection control(LADRC) is designed to achieve decoupling. Then, the AC reinforcement learning algorithm is combined to dynamically adjust the parameters of the linear tracking differentiator (LTD), so that the active power output of the photovoltaic power generation system can be quickly tracked to the maximum power point. Finally, the system model is built in the digital simulation platform, and through comparative analysis, it is verified that AC-LADRC control can significantly improve the response speed, achieve no overshoot-free control, and greatly improve the tracking accuracy, up to 99.8%, showing good tracking performance when the external environment changes dramatically.
  • Li Yuhong, Bi Guihong, Yang Nan, Wang Xiaoling, Chen Shiyu
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    To address issues in existing photovoltaic power forecasting, such as redundant historical data, strong correlations in consecutive time series, large training datasets, slow model convergence, and the significant randomness and nonlinearity of power outputs, this paper proposes a novel forecasting approach based on improved grey relational analysis, dual-modal hybrid decomposition, and a multi-branch deep learning model. First, meteorological variables positively correlated with PV power output are selected through correlation analysis. Their positive correlation coefficients are incorporated as weights into an enhanced grey relational analysis framework, combined with a logarithmic normalization strategy to improve the accuracy and discriminative capability of similar-day sample selection. Next, a dual-modal hybrid decomposition is performed on the PV power time series using empirical wavelet transform (EWT) and swarm decomposition (SWD), enabling multi-scale feature complementarity and generating more comprehensive input features. For model construction, a multi-branch input-parallel deep learning architecture, MB-T2V-KSA Net (Multi-Branch input-parallel Time2Vec-BiGRU-KAN-SA), is developed. This model integrates Time2Vec encoding to capture both linear and periodic temporal features, employs a Bidirectional Gated Recurrent Unit (BiGRU) to learn contextual dependencies, utilizes the Kolmogorov-Arnold Network (KAN) for high-order feature interaction, and applies a Self-Attention (SA) mechanism to adaptively fuse multi-source features. Together, these components significantly enhance the model’s ability in temporal modeling and feature integration.Experimental results demonstrate that the proposed method achieves high prediction accuracy and robustness regardles of weather type classification and diverse typical weather conditions, with particularly strong performance in adverse weather scenarios.
  • Gao Yu, Li Jie, Tian Haibo, Zhu Yixuan, Yang Haonan, Ma Chuang
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    To improve the cleaning efficiency of photovoltaic modules, this study investigates path planning for a photovoltaic cleaning robot and proposes a mobile robot path-planning method based on an improved sparrow search algorithm (HASSA). A Halton sequence is introduced to alleviate the non-uniform distribution of the initial population, and a dynamic factor is designed to adaptively adjust the numbers of discoverers and followers, thereby enhancing the optimization capability of the sparrow search algorithm. A path-planning model for the cleaning robot is established, and the fitness value function is reconstructed by considering practical operating conditions. The improved sparrow search algorithm is then applied to the photovoltaic cleaning path-planning problem, and simulation experiments are conducted to compare its performance with that of several other algorithms. The results show that the path obtained by HASSA achieves the best overall path performance. Finally, field experiments are carried out, and the results indicate that the proposed method performs better than conventional algorithms in photovoltaic cleaning path planning.
  • Zheng Xinyu, Li Yuan, Liang Yuling
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    To address the issue that traditional modeling and parameter identification methods fail to meet the requirements of high-precision characterization and prediction, this study proposes an Improved Red-billed Blue Magpie Optimization (IRBMO) algorithm for solar cell model parameter identification. Firstly, a novel population initialization strategy employing Sobol sequences integrated with opposition-based learning enhances population diversity while effectively mitigating premature convergence. Secondly, the implementation of a sine-cosine oscillation modulation and scaling control mechanism and a nonlinear convergence factor update strategy with stochastic components optimizes search trajectories, achieving superior balance between global exploration and local exploitation capabilities. In summary, experimental results demonstrate that the IRBMO algorithm outperforms other state-of-the-art algorithms in photovoltaic parameter estimation accuracy, achieving a root mean square current error of 9.8602E-04 for solar cell models. Under varying solar irradiance conditions, the IRBMO algorithm demonstrates a close agreement between the identified results and the measured curves, enabling accurate and effective identification of solar cell model parameters.
  • Yang Mengxue, Dai Zhiqiang, Zhu Yanyan, Liu Yongsheng
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    A photovoltaic power prediction method based on CNN-BiLSTM and an improved robust attention mechanism (RobustAttention) is proposed to address the issues of model susceptibility to disturbances and poor stability. First,adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose historical power data into several intrinsic mode functions (IMF),providing more stable data. Next,the IMFs and original data are input into the CNN-BiLSTM-RobustAttention model,where the local feature extraction ability of CNN and the long-term dependency correlation capturing ability of BiLSTM are combined to extract spatiotemporal features from the data. Finally,the features are input into the robust attention module to obtain the prediction results. Ablation experiments using historical power generation data from a photovoltaic power station in Jiangsu are conducted to validate the model’s prediction performance. The traditional attention mechanism is replaced with Robust Attention for comparison. The results show that the proposed method outperforms all models in terms of prediction performance across different time periods,with each component contributing to the improvement of model performance. RobustAttention can better capture anomalies in photovoltaic power generation data,further enhancing model stability and significantly improving prediction accuracy.
  • Li Changlong, Zhong Peijun, Ou Haonan, Su Sheng, Sun Jianjun, Su Huafeng
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    To meet the requirements of DC-side leakage current characteristic analysis and fault identification in photovoltaic grid-connected inverters, this paper first establishes an equivalent analysis model for DC-side leakage faults in non-isolated three-phase photovoltaic grid-connected inverters. Then, the formation mechanism and frequency-domain characteristics of leakage currents under DC-side faults are analyzed and the reasons for the formation of various frequency components are explained. After that, a multi-type fault identification method using the XGBoost algorithm is proposed based on the mechanistic and statistical characteristics of DC-side leakage currents.In the simulation cases, the proposed fault identification method based on mechanistic features and statistical features achieves an accuracy rate exceeding 99.5%, demonstrating significant superiority over traditional statistical feature-based approaches and conventional machine learning algorithms for fault detection. These results substantiate that incorporating mechanistic features can markedly enhance the identification accuracy of DC-side leakage fault types in grid-connected photovoltaic inverters.
  • Zhu Xianhui, Liu Jin, Ma Yating, Qian Wenli, Jia Yisong
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    A parameter identification model for silicon-based photovoltaic module based on the Golden Search Algorithm was constructed to address the issues of low accuracy and susceptibility to local optima in traditional algorithms. The optimization strategy that combines particle swarm optimization algorithm and sine cosine algorithm enhances the global search capability of the algorithm, while introducing transformation operators to dynamically balance the early global search and later local development capabilities of the algorithm. While ensuring computational efficiency, the proposed method improves the accuracy of the photovoltaic module parameter identification model and reduces the risk of falling into local optima. Compared with particle swarm optimization algorithm, differential evolution algorithm, war strategy algorithm, and aquila Optimizer, the results showed that the root mean square error of the identification results in this paper was less than 3%, and the average relative error was less than 1.4%, demonstrating the effectiveness and accuracy of the proposed model.
  • Wei Xiudong, Li Haotian, Niu Fucheng, Yu Qiang
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    To address the measurement of the overall mirror shape of the heliostat and the alignment angles of sub-mirrors in tower solar power stations, a heliostat mirror shape measurement method based on fringe reflection is proposed. This method utilizes four cameras to measure the heliostat composed of four reflective mirrors, reducing the size of the projection screen. The measurement principle of this method is described in detail, a measurement system is constructed, and the measurement uncertainty of the system is analyzed. Experimental results show that the normal deviation values of the measured heliostat in the X and Y directions are 1.004 mrad and 1.311 mrad, respectively, with a measurement uncertainty of 0.52 mrad.
  • Chang Zehui, Xu Wenfu, Li Xinliang, Liu Xuedong
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    Aiming at the receiver overheating of the compound parabolic concentrator (CPC) for solar greenhouse heating during nonoperating periods, this study proposes a novel compound parabolic concentrator photovoltaic-thermal device, with PV modules installed inside the CPC cavity. By intercepting incident solar radiation at different incident angles, these PV modules reduce the amount of radiation concentrated on the receiver and thus mitigate the overheating risk. First, TracePro is used to simulate rays’ propagation and focusing paths of the device at different incident angles, and quantify the changing trends of its optical characteristic parameters. Based on these results, an experimental platform was established to evaluate the anti-overheating performance of the compound parabolic concentrator photovoltaic-thermal device and to compare its overheating prevention effect with that of a standard CPC. The results show that, optical parameters of the device decrease initially and rise subsequently. Compared with the standard CPC, the maximum reductions in the light acceptance rate and concentration efficiency of the device are 26.4% and 25.3%, respectively. The maximum energy flux density on the receiver surface is 9276.1 W/m2, which is 25.6% lower than that of the standard CPC. Under clear-sky conditions, the receiver fin temperature reaches a peak at noon. The maximum temperatures of fins R1 to R6 are 102.2, 92.2, 91.7, 92.7, 98.6, and 100.3 ℃, respectively. These values are 15.7, 21.1, 23.4, 13.8, 19.9, and 14.6 ℃ lower than those of the corresponding fins in the standard CPC. In addition, the maximum output power of PV module Ⅰ and PV module Ⅱ are 4.8 and 3.8 W, respectively. Economic analysis further indicates that the static investment payback period of a solar greenhouse heating system integrated with this device is approximately 2 years.
  • Yu Lei, Chen Jifeng, Yang Song, Peter Lund, Wang Jun
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    This paper presents a novel conceptual design of solar concentrator, the fixed reflector parabolic concentrator, which consists of a fixed reflector with an asymmetric parabolic profile and a movable receiver. Compared with traditional linear concentrators (parabolic trough and linear Fresnel), it offers the advantages of fewer moving parts, lower construction cost and higher land utilization rate. Firstly, the relationship between the deviation angle and the concentration characteristics was studied through optical simulation and experiments. Then, the distribution characteristics of the deviation angle were investigated by establishing a mathematical model. The results show that for different geographical locations, when the concentrator is placed in the east-west axis direction and the parabolic inclination angle is set to the local latitude, the focal spot is the smallest and the overall concentration ratio ranges from 8 to 22. This paper provides an initial validation of the feasibility of the novel linear solar concentrator for solar concentration under varying temporal and positional conditions.
  • Zhou Zhi, Zhang Siyuan, Yang Genben, Xu Jianwei
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    This study employs mathematical modeling and numerical simulation to quantitatively evaluate the kinematic characteristics and operational patterns of heliostats utilizing two distinct tracking modes: azimuth-elevation and elevation-roll. The simulation results and comparative analysis demonstrate that heliostats employing the pitch-roll tracking mechanism impose more stringent requirements on the actuator’s range of motion compared to those utilizing the azimuth-elevation mode. This leads to increased design complexity and higher manufacturing costs. However, by introducing a tilt angle to the primary axis of the pitch-roll tracking heliostat, the actuator’s range-of-motion requirements can be partially mitigated, thereby reducing mechanical constraints.
  • Lyu Xinqi, Zhou Hui, Hu Jinhua, Liu Shenghao, Yan Xingbo, Wang Xuanhua
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    To address the calculation error caused by existing shading and blocking efficiency algorithms that typically assume the heliostat mirror as an ideal single reflective surface, without considering the composite structure of multiple sub-mirrors and the gaps between them, this paper proposes a novel computational approach based on traditional planar projection methods. Utilizing the characteristics of the scan-line algorithm, this method can flexibly calculate the shading and blocking efficiency for any polygonal heliostats, including those formed by combined polygons, thereby avoiding some shortcomings of conventional planar projection techniques. When applied to composite mirror surfaces, this method yields more precise results compared to treating the heliostat as a single, continuous reflective surface. Furthermore, the output from this computational model has been validated through field visits, where the measured shading and blocking efficiency of several actual heliostats was found to have a relative error of less than 1% when compared to the algorithm’s predictions.
  • Meng Jia, Hao Ling, Liu Yongxin
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    In this paper, we propose a simulation method for a regenerative solar transcritical Rankine cycle power generation system with a Fresnel collector, and analyse the system configuration and thermal efficiency influencing factors. The results show that when the with area is in the range of 30-50 m2, the performance of the system without regeneration is better. In the range of 50-70 m2, the internal regeneration system performs better. When the irradiation intensity was increased from 400 W/m2 to 1000 W/m2, the output work, thermal efficiency and exergy efficiency of the internal reheat system were improved by 3.8 kW, 5.58 and 5.6 percentage points, respectively. The system output power as well as the thermal and efficiencies decreased with the increase of solar incidence angle and the increase of CO2 mass flow rate. The optimum performance of the internal regenerative system is achieved at a solar incidence angle of 0° and a CO2 mass flow rate of 0.08 kg/s.
  • Mo Jingyue, Shen Yanbo, Ke Huabing, Yuan Bin, Ali Mamtimin, Liu Junjian
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    By integrating the global horizontal irradiance(GHI) forecast products from the China Meteorological Administration’s Wind and Solar Power Prediction System (CMA-WSP), aerosol forecast products from the atmospheric chemical/dust model (CMA-CUACE-DUST), and ground-based observations from 14 radiation observation stations in North China, an energy meteorology artificial intelligence forecasting system (AI-ENG) was developed for aerosol-high-impact weather. This system integrates a total of 8 sub-models and stacked generalization models. Employing a sliding monthly window strategy to partition the training and testing datasets, and the performance of AI-ENG in forecasting GHI was systematically evaluated over six months in the springs of 2023 and 2024. The results show that: 1) During the study period, dust weather events were frequent in North China with relatively high concentrations. CMA-WSP tended to overestimate radiation under strong dust conditions, and the forecast errors of CMA-CUACE-DUST were positively correlated with PM10 peak concentrations. 2) Incorporating aerosol forecast products, AI-ENG significantly improved the accuracy of GHI forecasts in North China. extreme gradient boosting decision tree(XGB) and multi-layer perceptron(MLP) models performed the best in the comprehensive evaluation, with all 7 metrics showing improvement. 3) In case studies of 72-hour day-ahead forecasts during different-intensity dust events in March 2024, AI-ENG reduced forecast errors by 17%-20%, particularly excelling in plain areas and along primary dust transport corridors. However, persistent biases remained in topographically complex regions. These results demonstrate that the AI-ENG system effectively supports short-term solar energy forecasting under dust weather conditions.
  • Wang Zhiwen, Yang Dazhi, Liu Bai, Qiu Haizhi, Shao Zhuhang
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    This study proposes a PV resource rating method based on principal component analysis (PCA) and clustering algorithms. The methodology involves acquiring solar irradiance and auxiliary meteorological variables for Heilongjiang Province from 2016 to 2020, derived from Himawari-8 satellite data with a spatial resolution of 4 km. Subsequently, nine temporal features are extracted for the five variables using time series analysis, and PCA is applied to reduce the dimensionality of the extracted features. Finally, a spatial distribution map of the PV climate at a power plant scale for Heilongjiang Province is generated through clustering analysis. This map categorizes the total solar resource of Heilongjiang Province into three levels: general, relatively abundant, and very abundant, providing significant scientific support for subsequent refined PV resource assessments.
  • Sun Xian, Lei Yangna, Wei Na, Hu Lin, Cheng Lu, He Xiaoai
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    In this study, the applicability and accuracy of the estimation model of daily global solar radiation in China are systematically evaluated. Based on the measured data of 121 radiation observation stations in China and combined with meteorological factors such as sunshine duration and daily temperature range, five representative models for estimating daily global solar radiation were selected, including nonlinear model (model Ⅰ), percentage of sunshine model (model Ⅱ), linear model (model Ⅲ), B&C model (model Ⅳ) and comprehensive model (model Ⅴ), and their applicability nationwide was tested. Through the evaluation of indicators such as the mean absolute percentage error (EMAE), mean absolute percentage error (EMAPE), root mean square error (ERMSE), and normalized root mean square error (ENRMSE), it is found that Model I and model Ⅲ have the best simulation performance, while model Ⅳ and model V have larger errors. Further analysis indicates that Model I and model Ⅲ have better stability in different climate regions and are suitable for daily global solar radiation estimation nationwide. The research results provide a scientific basis for the assessment of solar radiation resources and the development of solar energy in China.
  • Huang Lizhe, Li Jiadong, Du Chuang, Jia Yimeng, Sun Gang, Zhang Dongwei
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    To address the instability of standalone solar heating systems and the imbalance between heating and cooling demands in ground source heat pump (GSHP) systems, a solar collector-ground source heat pump system with seasonal thermal energy storage is proposed to stabilize building heating demand. The proposed solar-assisted seasonal thermal storage GSHP system was modeled using the TRNSYS simulation software and compared with a conventional standalone GSHP system. The impacts of soil temperature variation, heat pump performance, system electricity consumption, and equipment cost were systematically investigated. The results indicate that after five years of operation, the soil temperature in the standalone GSHP system decreases significantly, leading to a reduction in the system coefficient of performance (COP) from 3.75 to 3.51, with an annual electricity consumption of 83965 kW·h. In contrast, the soil temperature in the solar-assisted GSHP system remains nearly stable, and the system COP is maintained at approximately 3.91, with a reduced annual electricity consumption of 77428 kW·h. Although the initial equipment cost of the solar-assisted GSHP system increases by 145100 yuan, the annual operating cost is reduced by 19200 yuan, resulting in a payback period of 7.55 years. Overall, the results demonstrate that the solar-assisted ground source heat pump system with seasonal thermal energy storage is well suited for building heating applications in cold regions.
  • Liu Shuai, Wang Dengjia, Liu Yanfeng, Gong Jinghu
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    To address the energy demands of plateau border defense military zones, a modular photovoltaic-diesel-storage energy system is proposed. Based on the characteristics of natural resource distribution in border regions, the system’s operating principles, structural integration, and operational strategies are optimized. System optimization and simulation studies are conducted using HOMER software, focusing on indicators such as economic performance and solar energy share. The results demonstrate that the optimized modular photovoltaic-diesel-storage energy system achieves a 33% to 58% improvement in construction and installation efficiency, an 11.7% increase in annual energy utilization, a reduction in net present cost to 226000 yuan/a, and a decrease in levelized cost of energy to 187 yuan/(kW·h). These findings highlight the system’s excellent energy efficiency and economic advantages.
  • Li Jinping, Zhou Jingqiu, Huang Juanjuan, Kang Jian, Vojislav Novakovic
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    To investigate the impact of the differences in the coupling of heat pumps with different collectors in cold and arid regions on the operational performance of the system, evacuated tube collectors, large flat plate collectors, and PV/T collector and air source heat pumps were integrated for mathematical modeling and performance simulation, and their energy economy during the heating season was compared. The results show that the PV/T heat pump heating system has a better comprehensive effect, with an average solar fraction of 58.11% and an energy efficiency ratio of 4.85 during the heating season. Compared with evacuated tube collectors and large flat plate collectors, the solar fraction has increased by 22.4% and 46.7% respectively, and the energy efficiency ratio has increased by 31.4% and 60.6% respectively. And the PV/T heat pump heating system not only produces heat but also generates electricity, during the heating season, the system maintains an average daily power generation efficiency of 13.9%, with a return on investment period of only 5.5 years.
  • Dang Chaoliang, Zhai Jiahao, Jiang Zehao, Han Sipeng, Song Weizhang
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    To enhance the power quality and conversion efficiency of the grid interface of the new energy power generation system and its energy storage unit, this paper proposes two model predictive control strategies (MPCOVS) based on the optimal selection of voltage vectors for the VIENNA rectifier, aiming at the problems of fluctuating switching frequency, difficulty in selecting the weighting coefficient for the neutral point potential regulation, and large grid-side current ripple in the traditional finite set model predictive control (FCS-MPC). Firstly, based on the sector division method, the search and optimization scale of the candidate vector set is reduced from 27 to 3, and the vector set is optimally selected by the pre-judgment of the upper and lower DC busbars to achieve the neutral point potential balance control without weighting coefficients. Secondly, two constant-frequency control methods using the synthesis of two vectors and three vectors are utilized to improve the grid-side current ripple, achieving smooth switching of the switch states and effectively enhancing the output performance of the grid-side current through duty cycle optimization. Finally, the proposed methods are verified through simulation and experimental analysis in multiple dimensions such as static characteristics, transient response, and neutral point potential balance control. The results show that the proposed methods can effectively improve the quality of the grid-connected current, reduce the computational complexity and switching losses, and have good steady-state and dynamic performance.
  • Dong Fugui, Liu Jinyi, Wang Peijun
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    To address the impacts of renewable energy output uncertainty on the economic operation of multi-micro energy grids (MMEGs), this study proposes a collaborative optimization method for shared energy storage in MMEGs by integrating bi-level programming with an enhanced cooperative game theory. Based on a time-of-use electricity price mechanism, a bi-level collaborative planning framework for electro-thermal hybrid energy storage systems in MMEGs is established: The upper-level model optimizes energy storage capacity allocation by minimizing investment and operational costs, while the lower-level model coordinates electricity-heat energy dispatch across MMEGs to refine charging/discharging strategies. To overcome the limitations of traditional cost allocation methods, a tri-dimensional contribution degree encompassing economic, environmental, and energy efficiency attributes is innovatively developed, establishing an allocation mechanism based on an improved Shapley value method. The disruption propensity index is employed to evaluate the satisfaction of individual microgrids, thereby verifying the fairness of the cost-sharing scheme. Simulation results demonstrate that the proposed approach reduces the total system operational cost by 4.6% and decreases carbon emission intensity by 3.04% compared to independent energy storage configurations. The allocation scheme derived from the improved Shapley value method yields defection propensity indices below 0.8 for all microgrids, confirming the equity and effectiveness of the cost distribution.
  • Luo Zhaoxu, Yu Kang, Cao Yunzhou, Cheng Yinan
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    A model predictive control (MPC) based ESS control scheme is proposed to stabilize the DC microgrid bus voltage fluctuations caused by random fluctuations in photovoltaic output. This method can accurately generate ESS output power reference based on changes in bus voltage, and use recursive least squares method with forgetting factor for online parameter identification and rolling optimization of model parameters to reduce the impact of photovoltaic fluctuations. At the same time, considering the SOC of ESS as an additional constraint condition, the principles for selecting weight coefficients in the objective function are obtained using the Lyapunov method, thus achieving a good balance between the fluctuation range of bus voltage and the health of ESS. The outstanding advantage of this method is that the ESS output power reference instruction can be directly calculated and generated based on local measurement data, without the need for external communication, reducing system costs and improving reliability. The simulation and experimental results have verified the correctness and effectiveness of the method.
  • Liu Tianze, Zhu Yongqiang, Zhou Jiaxin, Fan Yingqi
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    This study provides a new definition of complementarity and proposes a novel complementarity evaluation method based on quantitative deviation. Instantaneous complementarity and interval complementarity are defined to characterize, respectively, the real-time and overall complementary states of two variables. For interval complementarity, pure complementary intervals and true complementary intervals are further distinguished to highlight the complementary characteristics of the interval. The method establishes two quantitative indexes, namely the relative deviation ratio and the overall deviation reduction rate, examines their numerical relationships and value ranges, and verifies the advantages and effectiveness of these indexes compared with existing indexes. Finally, the proposed method is applied to multiple scenarios in wind-solar hybrid systems. Specifically, the influence of wind-solar installed-capacity ratios on quantitative deviation complementarity is analyzed, and the optimal installed-capacity configuration is determined accordingly. The results demonstrate improved power supply reliability and enhanced supply-demand coordination. The study also discusses the effects of different transmission scheduling curve formulation methods adopted by power dispatching departments on the quantitative deviation complementarity of the system. Furthermore, wind and solar power outputs are optimized to enhance quantitative deviation complementarity, and the effectiveness of this optimization in reducing flexibility requirements is analyzed and validated.
  • Dai Wenzhi, Cui Tianyu
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    To realize the efficient and economical operation of a combined cooling, heating and power (CCHP) system, an optimal scheduling mathematical model is established with the minimum sum of operationmaintenance cost and environmental cost as the objective. Aiming at the problems of weak global search capability and low optimization efficiency in the traditional dung beetle optimizer (DBO), an improved dung beetle optimizer is proposed. Chaotic initialization is introduced to enhance population diversity, goldensine strategy and nonlinear control factors are adopted to improve particle search performance, and a mutation strategy is utilized to prevent the algorithm from falling into local optima. Unimodal and multimodal functions are selected to test the performance of the improved dung beetle optimizer. Comparative results with other algorithms show that the improved algorithm achieves better convergence speed and convergence precision. Rank tests are conducted on the simulation results, which verify the statistical significance of the proposed algorithm. When applied to the CCHP system, the algorithm can improve the operational and environmental benefits of the microgrid.
  • Dai Zhihui, Jiang Ying, Shi Chen, Shi Junyang, Yang Yishi
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    This paper proposes a fault line selection and location method based on Variational Mode Decomposition (VMD) optimized by the Beluga Whale Optimization (BWO) algorithm. By constructing an objective function based on the quality factor, the BWO algorithm adaptively optimizes the VMD parameters, thereby enhancing the extraction capability of transient fault features. A transient energy criterion is established using the Intrinsic Mode Function (IMF) components of differential currents to accurately identify fault lines and fault types. Furthermore, the Teager Energy Operator (TEO) is employed to extract traveling-wave characteristics for high-precision fault location. A six-terminal ring-type flexible DC distribution network model is developed in PSCAD/EMTDC. Simulation results demonstrate that the proposed method achieves high accuracy and exhibits strong adaptability and robustness in fault line selection and location.
  • Qu Keqing, Su Shijie, Mao Ling, Zhao Jinbin, Huang Yuchen, Gao Chang
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    To address the instability issue of grid-connected inverters caused by the negative-resistance characteristic introduced by phase-locked loops (PLLs) in low-frequency range under high grid impedance conditions in weak grids, this paper proposes a single-phase PLL-free control method based on an improved second-order generalized integrator (ISOGI) and conservative power theory (CPT).The proposed method employs the unbiased integration in CPT combined with ISOGI for phase tracking, effectively replacing conventional PLLs. Impedance modeling analysis demonstrates that this approach significantly extends the grid impedance adaptability range. Compared with PLL-based control methods, it exhibits enhanced stability and robustness under extremely weak grid conditions with high-order harmonics and DC components in grid voltage. Finally, experimental results validate the correctness of the theoretical analysis.
  • Li Peng, Yu Bohan, Sun Shumin, Cheng Yan, Yu Peng, Wang Jiahao
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    With the increasing penetration of distributed renewable energy, the operational complexity of distribution systems intensifies, and the declining reactive power regulation capability exacerbates voltage quality issues. To address this challenge, this study proposes an intraday optimal voltage/reactive power control method coordinating novel energy storage systems with traditional reactive power resources in high-penetration distributed renewable energy grids. First, the response characteristics and regulation capacities of novel energy storage systems and various reactive power resources in such systems are analyzed, establishing a multi-time-scale coordination framework. Subsequently, a dual timescale intraday optimal reactive voltage control model is developed and solved using a second-order cone relaxation approach. Verification through the modified IEEE 33-bus test case demonstrates that the proposed method effectively harnesses the adjustment capabilities of multiple reactive resources. It maintains control precision while preserving adequate dynamic reactive power reserves, finally enhancing the grid's reactive power optimization capability.
  • Yu Yang, Pang Qiwen, Niu Yunjing, Zhang Qianhui, Sun Zishuo, Wang Boxiao
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    Currently, the impact load identification methods for microgrids with heterogeneous energy storage insufficiently focus on energy storage parameters, resulting in ineffective responses of energy storage systems to complete impact loads. To address this issue, this paper proposes an IBKA-ISDT algorithm-based impact load identification method for microgrids with heterogeneous energy storage. Firstly, multidimensional mutational indicators for impact loads are established, including power peak, power change rate, peak duration, and electrical energy, to meet the requirements of heterogeneous energy storage microgrids. Secondly, an improved swing door trending (ISDT) algorithm with multiple door-width coefficients is proposed, where door-width coefficient optimization is implemented through an improved black-winged kite algorithm (IBKA). Subsequently, a modified fitness function is employed to achieve multi-power feature extraction, accomplishing impact load identification and combination. Finally, actual operational data from a heterogeneous energy storage microgrid are used to validate the proposed method. Results demonstrate that this method effectively identifies impact loads with high accuracy and exhibits significant advantages.
  • Li Jianlin, Geng Ziyu, Li Yaxin, Jiang Xiaoxia, Han Yuchen
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    To address carbon trading issues in power grid dispatch, a comprehensive optimization framework is proposed that integrates carbon allowance allocation, trading coordination, renewable energy consumption, and regulatory verification. An intelligent monitoring system combined with a blockchain-based evidence storage mechanism is introduced to enable reliable data interaction and unified management across multiple processes, thereby improving the effectiveness of carbon trading decision-making for grid enterprises.Carbon emission accounting models are established for both the generation side and the user side. A comparative analysis is conducted on generation-side methods, including the emission factor method, material balance method, and direct measurement method, as well as user-side approaches such as average, time-of-use, regional, and dynamic carbon emission factor methods. This analysis clarifies the technical pathway for accurate carbon emission quantification. Furthermore, a multidimensional low-carbon evaluation system is constructed, incorporating indicators such as the contribution of clean power generation, the allocation ratio of flexible loads, and the proportion of green consumption, to comprehensively assess the low-carbon performance of the power grid.
  • Dai Zeyu, Zhang Xuedan
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    To address the issue of significant variability in the performance of a phase change thermal storage parallel solar assisted heat pump heating system under varying meteorological conditions, a cluster analysis method is proposed to classify the meteorological data. Two primary indicators, solar irradiation and outdoor temperature, along with five secondary indicators, were selected. Using k-means cluster analysis, 183 meteorological days in Harbin during the heating season were classified into five categories, and the heterogeneity of the clustering results was examined based on the Q-test using stratified analysis. Furthermore, typical days were selected for each category to analyze the impact of air source heat pump operation time on total system input power, and the optimal turn-on time for the air source heat pump was determined for each typical day with the lowest total system input power as the optimization objective, so as to obtain the optimal operation strategy of the system for the whole heating season.
  • Ma Yanfeng, Wang Shuyan, Wang Zijian, Zhao Shuqiang, Xu Weikuo, Han Shanshan
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    Aiming at the problems of poor generalization, and insufficient temporal feature extraction capability existing in the identification of noisy sub-synchronous oscillations (SSO) by traditional neural networks, a CNN-SE-Attention-ITCN hybrid identification model combining convolutional neural network(CNN)with self-attention mechanism and improved temporal convolutional network(ITCN)is proposed. Firstly, the mode number K and penalty factor α of variational mode decomposition(VMD)were optimized using TTAO algorithm, and several modes of SSO were decomposed. Then, effective modal components were selected according to Pearson correlation coefficient(PCC), and time domain features were extracted. Finally, the trained CNN-SE-Attention-ITCN model was used to identify SSO parameters. The model is proved to have good identification accuracy and robustness through the testing of noisy ideal signal, simulated signal and real signal.
  • Zhang Zhenghao, Wu Kexin, Chen Peng, Xue Qiang, Wang Shuohe
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    With the continuous development of new power systems integrated with photovoltaics, the types of power quality disturbances (PQDs) have become more complex. In response to the increasing proportion of composite disturbances, which makes it difficult for traditional methods to accurately classify complex PQDs under the guidance of human experience, a complex PQDs classification method based on image fusion and multimodal feature-driven approach is proposed. In terms of data processing, two-dimensional image encoding methods—recurrence plot (RP) and continuous wavelet transform (CWT)—are introduced to convert one-dimensional PQDs signals into two-dimensional images. RP-CWT feature images are generated through horizontal concatenation to achieve feature enhancement. At the model level, an ensemble model for multimodal fusion with parallel optimization of re-parameterized large kernel network (RepLKNet) and bidirectional gated recurrent unit (BiGRU) is established. This model captures high-dimensional features in the time, frequency, and spatial dimensions, enabling rapid and accurate identification of disturbance features in signals. Finally, tests based on simulation data and a PQDs experimental platform show that the proposed method can accurately classify complex PQDs.
  • Luo Xi, Huang Wenyuan, Han Mingyue, Zheng Yanning
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    A solar-biomass integrated energy system can be used to address the low-carbon energy supply needs of agricultural greenhouse buildings. However, because the energy load patterns of greenhouse buildings are shaped by crop growth requirements and therefore have certain distinctive characteristics, conventional energy system optimization design methods are difficult to apply directly. Taking a Phalaenopsis greenhouse building in the Guanzhong region of Shaanxi Province as an example, this paper analyzes the energy load characteristics of such agricultural greenhouse buildings based on the actual growth requirements of Phalaenopsis, and optimizes the design of a solar-biomass integrated energy system while considering flexible loads.The results indicate:the annual energy consumption patterns of Phalaenopsis greenhouses vary across three distinct phases: dormancy (dominated by rigid loads such as heating and ventilation), rapid growth, and flowering (featuring flexible loads from irrigation, supplemental lighting, and transport equipment charging, enabling load flexibility).Flexible load management reduces the annualized system cost by 8.42% and 3.01% under scenarios with and without time-of-use (TOU) electricity pricing, respectively. During typical days representing dormancy, rapid growth, and flowering phases, daily operational costs decrease by 5.89%, 12.93%, and 7.60% when TOU pricing is considered.Photovoltaic module price fluctuations significantly impact the annualized system cost, but flexible load control mitigates this effect to some extent.
  • Qiu Bin, Guo Shanshan, Wang Kai, Liu Hongzhi, Wang Raoning
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    Driven by the dual strategy of “double carbon” target and energy transformation, this paper proposes a synergistic optimization model of integrated energy system integrating photovoltaic power plant, stepped carbon-green certificate trading mechanism and load demand response, and puts forward an innovative solution to improve the economic and environmental benefits of the integrated energy system. First, on the basis of the traditional integrated energy system model, a model of the concentrating solar power(CSP) plant is constructed, and the stepped carbon-green certificate trading mechanism is introduced. Second, the demand response model of electric and thermal loads is constructed, and the system is analyzed in accordance with the energy quality characteristics, and the exergy efficiency model of the whole system is constructed. Finally, the model is solved using a hybrid optimization algorithm of Aquila Eagle and African Vulture. The results show that the optimization model proposed in this paper reduces the total system cost by 37.62%, reduces the carbon emission by 43.76%, and improves the energy efficiency by 14.72% compared with the traditional model.
  • Cao Feng, Tan Youqi, Zeng Jinhui, Liu Renshan
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    In solid-state transformers, the output voltage of the isolated DC/DC converter stage is susceptible to voltage fluctuations from the front-stage grid-connected three-phase converter or load transients, thereby threatening the stability of the DC bus voltage in the solid-state transformer. To address this issue, this paper proposes a voltage control strategy for solid-state transformers based on dual-integral sliding mode control. First, a dual closed-loop voltage-current control is implemented for the front-stage three-phase fully-controlled rectifier, and a unified phase-shift control model for the isolated bidirectional DC/DC converter is established based on Fourier series analysis. Subsequently, the model is validated under single-phase-shift control mode, and a controller is designed by integrating dual-integral sliding mode control theory. Simulation and experimental results demonstrate that, compared to traditional PI control and single-integral sliding mode control, the proposed method exhibits significant advantages in dynamic response speed, overshoot suppression capability, voltage stability under load transients, and substantially enhanced robustness.
  • Jiang Jianbo, Ma Ying, Zhao Enming, Li Peng, Liu Guangyu
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    The generation of electricity from renewable energy sources such as solar power is intermittent, this characteristic hinders its large-scale integration into the power grid. The integration of multiple distributed energy sources can realize complementary advantages and then smooth out power fluctuations. Based on this, this paper pro-poses a modular inverter consisting of a quad active bridge (QAB) converter, a cascaded H-bridge (CHB) inverter, and a multiple active bridge (MAB) converter, where the MAB is used to realize the electromagnetic energy transfer between the submodule converters. The main advantages of this topology include the following: each sub-module can be controlled independently, inter-phase power balance and intra-phase power balance can be achieved, and the MAB can reuse the high-frequency inverter of the QAB to reduce the number of switches. In addition, a collaborative control strategy based on virtual synchronous generator (VSG) control is designed to support the frequency of power grid. Finally, a hardware-in-the-loop semi-physical simulation platform is built to experimentally verify the effectiveness of the proposed modular inverter and cooperative control strategy.
  • Fu Xiaoyan, Zheng Le
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    In response to the system scheduling optimization problem caused by the intermittent and fluctuating output of renewable energy generation, an optimal scheduling algorithm of renewable energy power generation considering security and stability risk cost is proposed. Firstly, a multi-objective optimal scheduling model of renewable energy generation is established with the power generation cost, the stability cost, and the proportion of renewable energy consumption as optimization objectives. Secondly, based on the principles of deep reinforcement learning, an optimization solution framework for the scheduling model of renewable energy power generation system is proposed. Combined with the Markov Decision Process (MDP), the multi-objective optimization scheduling problem of renewable energy generation is transformed and described to establish a solution process for the model based on deep reinforcement learning. Then, an open-source platform for scheduling of renewable energy with deep reinforcement learning (SEDRL) is established using open-source software packages such as PandaPower, OpenAI Gym, and Stable-Baselines. According to the actual power grid parameters, the system optimization scheduling algorithm program is established, and different deep reinforcement learning algorithms are used for verification. Finally, a test example is established based on the IEEE 118-bus system for verification. The results show that the open-source platform has good scalability and is suitable for the generation of renewable energy generation scheduling strategies in different scenarios.
  • Li Shengqing, Wu Jinghang, Gao Zehua, Qiao Jingxiao
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    To enhance the economy and reliability of independent microgrids and obtain the optimal ratio of each configuration in the microgrid, research on the optimal capacity configuration of microgrids based on the improved Harris Hawks optimization algorithm is conducted. Taking the average annual comprehensive cost as the objective function, and comprehensively considering various constraints and the demand response theory of time-of-use electricity prices, the configuration model and operation strategy of the wind-solar-diesel-storage microgrid are constructed. Aiming at the problems that the Harris Hawks optimization algorithm is prone to local convergence and insufficient accuracy when solving models, an improved Harris Hawks optimization algorithm integrating ICMIC chaotic mapping, sine cosine algorithm, crisscross algorithm and Levy flight strategy is proposed. Through non-zero solution test function, the improved Harris Hawks optimization algorithm was tested and compared with the Harris Hawks optimization algorithm, the gray wolf optimization algorithm and the ant-lion optimization algorithm, verifying that the improved Harris Hawks optimization algorithm has better convergence performance. Finally, meteorological data and load data of a certain area were selected for case analysis. It was found that the annual comprehensive cost was reduced by 10.71%, 17.37% and 7.92% respectively compared with the other three algorithms, verifying the effectiveness of the improved Harris Hawks optimization algorithm.
  • Cheng Yu, Yan Yulu
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    Load aggregators in the day-ahead joint energy-reserve market face uncertainties from external market price volatility and the internal responsiveness of aggregated resources. Accounting for the risk preferences of price-taking aggregators, we optimize bidding strategies for both energy and reserve markets. To support fine-grained management of response behavior and the associated risks to energy-trading and reserve revenues undes dual sources of uncertainty (prices and resource responses), we integrate fuzzy chance constraint (FCC) with information gap decision theory (IGDT) to develop a risk-constrained hybrid FCC-IGDT bidding model. Simulation results show that the model meets requirements for both revenue robustness and risk sensitivity, enabling risk-management-driven co-optimization of day-ahead energy offers and reserve capacity bids. It also provides mechanisms for early warning and identification of hedging opportunities, thereby enhancing aggregators’ market competitiveness.