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• 2026 Volume 47 Issue 12
  Published: 15 November 2026

    

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  COORDINATED OPTIMIZATION MODEL OF SYNCHRONOUS CONDENSER AND ENERGY STORAGE FOR NEW ENERGY STATIONS CONSIDERING TRANSIENT VOLTAGE SUPPORT

  Pan Yan, Zhang Chenxi, Zhao Wei, Li Xing, Xu Peng, Li Fuqiang

  2026, 47(12): 1-8. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1012

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  In order to effectively solve the problems that the randomness of new energy output may lead to the decrease of transient stability level and power supply reliability of power grid, a coordinated optimization model of synchronous condenser and energy storage for new energy stations considering transient voltage support is proposed in this paper. Firstly, by analyzing the reactive voltage support characteristics of the synchronous condenser and energy storage, as well as the dynamic characteristics of the synchronous condenser during near-field short circuits, a new energy station reactive power and voltage support characteristic model that coordinates synchronous condenser and energy storage is constructed; Then, with the objective of minimizing the total coordinated operation cost, a coordinated optimization model of synchronous condenser and energy storage for new energy station considering transient voltage support is established; Finally, through the example simulations, it is verified that the coordinated optimization model of synchronous condenser and energy storage proposed in this paper can improve the operational economy of new energy stations and the transient voltage regulation level of the power grid.
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  RISK ASSESSMENT OF ELECTROCHEMICAL ENERGY STORAGE POWER STATIONS BASED ON COMBINED WEIGHTING-CLOUD MODEL

  Zeng Xing, Wei Bin, Lai Yilin, Chen Hao, Zhao Yi

  2026, 47(12): 9-13. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1112

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  In response to the frequent occurrence of safety incidents at energy storage stations and the limitations of existing safety assessment methods-such as arbitrary weight assignment, inadequate handling of uncertainty, and limited adaptability to diverse scenarios-this paper proposes a safety risk assessment method for energy storage stations based on a combined weighting cloud model. First, by comprehensively considering the overall operation of the energy storage station, an assessment index system for safety risks is constructed to address the deficiencies of existing systems, namely the lack of holistic operational representation and insufficient applicability. Second, an improved Analytic Hierarchy Process （AHP） is employed to determine subjective weights, eliminating the consistency verification issue inherent in traditional AHP; this is combined with the CRITIC method to calculate objective weights for each index, and a game-theory-based approach is used to optimize the weight combination. Then, a safety risk assessment model based on the cloud model is developed using cloud theory, fully leveraging its strengths in converting uncertainties between qualitative concepts and quantitative data, thereby effectively resolving the assessment challenge posed by the coexistence of randomness and fuzziness in the risks of energy storage stations. Finally, taking a lithium iron phosphate energy storage station as a case study, the effectiveness of the proposed method is validated, providing valuable reference and a scientific criterion for the safe and stable operation of energy storage stations.
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  PARAMETER IDENTIFICATION OF BATTERY FRACTIONAL ORDER MODEL BASED ON IMPROVED PELICAN OPTIMIZATION ALGORITHM

  Jin Xiaona, Liu Ranran, Song Weihua, Guo Wei

  2026, 47(12): 22-29. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1121

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  As a critical energy storage carrier in renewable energy systems, accurate battery state monitoring is essential, and the establishment of an accurate battery model is the basis for ensuring that the battery state can be accurately estimated. To achieve this, the paper proposes a model parameter identification method based on IPOA. Firstly, the battery model is established based on the fractional-order second-order RC circuit, the diversity of the initialized population is improved by introducing the Sine chaotic mapping strategy into POA. Secondly, the Lévy flight strategy is adopted to solve the problem that the algorithm is easy to fall into local optimum, and the adaptive nonlinear attenuation factor is designed to dynamically adjust the search weight, thus the IPOA is proposed to realize the identification of battery model parameters. Finally, simulation verifications are carried out under different working conditions, and the simulation results show that compared with the original pelican algorithm and the adaptive genetic algorithm, the proposed algorithm not only has higher accuracy and can accurately identify the model parameters, but also has a better estimation effect when applied to SOC estimation.
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  STATE-OF-HEALTH ESTIMATION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN-GRA-ANFIS

  Zhang Youbing, Wu Yixuan, Gu Jipeng, Zhu Lidan, Zhang Zhiming, Dai Lingjun

  2026, 47(12): 30-43. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1123

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  Accurate and reliable estimation of the state of health （SOH） of lithium-ion batteries （LIBs）can help improve the safety and stable operation of battery-powered equipment. A lithium-ion battery SOH estimation method based on complete ensemble empirical mode decomposition with adaptive noise-grey relational analysis-adaptive neural fuzzy inference system （CEEMDAN-GRA-ANFIS） is proposed to address the current issues of inability to directly measure SOH in lithium-ion batteries, difficulty in extracting health features, and insufficient estimation methods. The method is validated using publicly available lithium-ion battery degradation test data from NASA Ames Research Center and the Advanced Lifecycle Engineering Center at the University of Maryland. Firstly, the battery charge/discharge curves are processed and CEEMDAN is used to decompose the battery degradation data into signals to extract health-related features. Then use GRA to perform correlation analysis on the feature components to select input variables for the model. Finally, the selected input-output data with obvious relevant characteristics is put into ANFIS for battery SOH estimation. The experimental results show that this method can accurately estimate the SOH of lithium-ion batteries, including both single cell and multi cell cases. The R² of the estimation results is always greater than 99%, further proving that the proposed estimation model has good generalization and robustness.
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  BATTERY STATE OF HEALTH ESTIMATION METHOD BASED ON MULTI-MODEL FUSION

  Gao Bingmei, Bao Caijilahu, Wang Xin, Ma Zhiqiang, Wang Hongbin, Zhang Jiaqi

  2026, 47(12): 44-55. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1128

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  The capacity of batteries gradually decreases during long-term operation, and an accurate estimation of battery state of health is crucial to ensuring the safe operation of new energy storage power stations. Aiming at the problems of model uncertainty due to the difficulty of health feature selection and the loss of key information during feature extraction due to data noise, a battery state of health estimation method based on multi-model fusion is proposed. Firstly, the uncertainty of feature selection is reduced by analyzing the battery charging and discharging data and extracting key health features; Secondly, the features are extracted in depth using a noise-reducing autoencoder and an attention mechanism to convert the low-level features into key features and reduce the interference of noise; Finally, extreme gradient boosting modeling is used to capture the complex patterns and nonlinear dependencies in the data. Comparison, ablation, and generalization experiments are conducted based on publicly available datasets. The experimental results demonstrate that the proposed model reduces the average MAE by 38% and RMSE by 42% compared with the suboptimal model, indicating high accuracy and good generalization performance in battery SOH estimation tasks.
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  CAPACITY PREDICTION METHOD OF LITHIUM-ION BATTERY BASED ON RECURRENCE PLOTS AND CONVOLUTIONAL NEURAL NETWORK

  Li Zhiming, Xia Xiangyang, Luo Yichong, Bai Wenqi, Peng Cheng

  2026, 47(12): 56-65. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1152

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  This paper proposes a capacity prediction method for lithium-ion batteries based on Recurrence Plot （RP） and Convolutional Neural Network （CNN）. Firstly, the differential voltage （dV/dQ） curves of the batteries are converted into recurrence plots, effectively capturing the complex dynamic characteristics of capacity degradation during practical operation. Subsequently, a CNN is employed to extract multi-level features, achieving efficient capacity regression prediction. Finally, comprehensive validation is conducted using the public lithium-ion battery aging dataset from Tongji University and a lithium-ion battery aging experimental platform. The results indicate that the error metrics （MAE, RMSE, and MAPE） of the proposed method are all maintained at low levels. This verifies the effectiveness and robustness of the proposed capacity prediction method.
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  FUZZY DECOUPLING TEMPERATURE CONTROL OF PEMFC THERMAL MANAGEMENT SYSTEM OPTIMIZED BY TLBO-DE

  Chen Junhong, Song Jiale, He Pu, Wang Mingyang, Zhang Jinwei, Tao Wenquan

  2026, 47(12): 66-76. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1033

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  This paper proposes a temperature control strategy based on fuzzy decoupling to address the strong nonlinearity and strong coupling issues in the PEMFC thermal management system. Compared with traditional feedforward decoupling control, fuzzy decoupling control reduces the integral of absolute error （IAE） of the coolant inlet and outlet temperature difference by 23.09%, and reduces the IAE of the coolant inlet temperature by 14.53%. Then, a hybrid heuristic algorithm TLBO-DE is proposed, which combines differential evolution （DE） in the learning stage of teaching and learning based optimization （TLBO）, thus improving the convergence speed and global optimization ability of a single algorithm. Finally, in order to further improve the temperature control effect, this paper adopts the TLBO-DE algorithm to optimize the membership function and fuzzy rules of the fuzzy decoupling control, making it more suitable for the control scenario in this paper. The optimized fuzzy decoupling control can reduce the IAE of the coolant inlet and outlet temperature difference by 36.56% and the IAE of the coolant inlet temperature by 25.59%. The significant improvement in temperature control effect verifies the effectiveness of the optimization method.
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  MULTI-OBJECTIVE OPERATIONAL OPTIMIZATION MODEL FOR ELECTRO-HYDROGEN-METHANOL COUPLING SYSTEMS CONSIDERING HYBRID ELECTROLYZERS

  Fan Yijun, Du Yida, Xierzhati Yibulayimu, Zu Wenjing, Li Huixuan, Tan Zhongfu

  2026, 47(12): 77-90. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1086

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  This paper proposes a multi-objective operational optimization model for an electro-hydrogen-methanol coupling system featuring PEM-AWE mixed electrolytic cells and energy bidirectional conversion functionality. By combining the high responsiveness of PEM with the low-cost advantage of AWE, and through the coordination of the electrical energy subsystem, hydrogen energy subsystem, and methanol energy subsystem—as well as the bidirectional energy conversion among these subsystems—the system’s regulatory capacity and economic efficiency are effectively enhanced. Furthermore, the validity of the model is verified through case studies, providing operational strategies that balance economic efficiency, renewable energy integration, and system stability for the development of new power systems dominated by a high proportion of renewable energy.
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  PEMFC DEGRADATION HYBRID PREDICTION BASED ON IMPROVED WHALE OPTIMIZATION ALGORITHM OPTIMIZED GRU-INFORMER

  Su Hongyu, Gao Fengyang, Zha Pengtang, Yue Wenhan, Han Yunfei

  2026, 47(12): 91-100. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1118

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  To improve the accuracy and operating condition adaptability of proton exchange membrane fuel cell （PEMFC） degradation prediction models, a hybrid method combining a gated recurrent unit （GRU）-Informer neural network with an improved whale optimization algorithm （IWOA） is proposed. First, IWOA is used to optimize the key hyperparameters of the GRU-Informer model, yielding the best parameter sets under different operating conditions. Second, the GRU layer extracts short-term dynamic features from the input sequences, while the Informer layer captures global and long-term dependencies. Finally, five models are compared under both steady and dynamic conditions. The results demonstrate that the proposed method achieves the highest prediction accuracy across various scenarios and training set proportions, with a clear advantage in small-sample cases. Therefore, the method offers strong adaptability, stability, and generalization capability, effectively improving PEMFC performance degradation prediction.
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  RESEARCH ON ISLANDING DETECTION OF GRID-CONNECTED SOLAR PHOTOVOLTAIC POWER GENERATION SYSTEM BASED ON IMPROVED ACTIVE FREQUENCY OFFSET

  Yao Yuan, Liang Defei, Fu Hao, Guo Qianqian, Gao Kang

  2026, 47(12): 101-109. https://doi.org/10.19912/j.0254-0096.tynxb.2024-0971

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  Propose an islanding detection method for grid-connected solar photovoltaic power generation systems. In depth exploration of the mechanism of islanding, islanding detection of solar photovoltaic power generation grid connected systems is divided into two situations： off grid and non-off-gridstate. In response to the off grid state, Hall sensors are introduced to collect voltage signals from photovoltaic grid connection points, and isolation technology and filters are used to overcome interference effects, accurately reflecting the system's operating status and achieving islanding detection under off-grid conditions; Based on the active frequency offset method, an improvement is made for the non-off-gridstate by introducing positive feedback disturbance to enhance frequency deviation. To improve its applicability, the initial chopping frequency is adjusted according to the load characteristics, and the distribution of blind zone boundaries is changed to achieve islanding detection in the non-off-gridstate. Thus, effective detection of islanding in grid-connected solar photovoltaic power generation systems can be achieved. The experimental results show that in the off-grid state, the voltage signal obtained by the proposed method at the photovoltaic grid connection point is consistent with the actual voltage signal. In the non-off-gridstate, the voltage frequency offset can be effectively detected, and the islanding detection time is all within the maximum duration of the islanding. The detection speed is fast, the false detection rate is below 0.05%, the missed detection rate is 0, and the detection blind zone is small, which can achieve effective islanding detection.
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  RESEARCH ON PHOTOVOLTAIC MULTI-PEAK MPPT BASED ON IFTTA-INC HYBRID ALGORITHM

  Wang Qi, Zhang Jinhua, Zhao Minzheng, Wang Xiaoliang

  2026, 47(12): 110-120. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1030

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  For photovoltaic arrays under complex shading conditions, the power-voltage （P-U） characteristic curve exhibits multiple peaks, making it difficult to sustain maximum power point output. To address this problem, a novel hybrid algorithm, termed the improved football team training algorithm with incremental conductance （IFTTA-INC）, is proposed. In the initial stage, adaptive oscillating inertia weight factors and adaptive bilateral factors are incorporated into the football team training algorithm （FTTA） to enhance global search capability and avoid local optima. In the subsequent stage, the incremental conductance （INC） method is employed for local search, effectively suppressing frequent oscillations and improving tracking accuracy. Simulation experiments are conducted in MATLAB/Simulink and compared against the particle swarm optimization （PSO）, grey wolf optimizer （GWO）, improved football team training algorithm （IFTTA）, and INC methods. The results demonstrate that the proposed algorithm not only achieves faster tracking speed and higher accuracy but also exhibits superior robustness, thereby enabling efficient maximum power point tracking.
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  HYDRODYNAMIC RESPONSE OF OFF SHORE STAGGERED ARRAY FLOATING PHOTOVOLTAIC STRUCTURES

  Wu Zhaoqi, Zhang Weihua

  2026, 47(12): 121-129. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1074

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  This paper proposes a novel floating structure configuration employing prefabricated hexagonal floating units as fundamental components, interconnected by elastic connectors in a staggered arrangement pattern. To quantitatively evaluate the engineering applicability of this innovative structure, hydrodynamic response analysis is conducted using ANSYS Workbench software. The study systematically investigates the influence mechanisms of three critical factors on the motion responses of the multi-body floating system： the geometric configuration of floating units, rotational stiffness of connectors, and wave incidence angles. The results demonstrate that hexagonal floating arrays significantly reduce translational displacement amplitudes compared to other configurations, effectively minimizing peak mooring tensions. Optimizing the design of elastic connector can effectively suppress relative rotational displacement between adjacent floating units while balancing peak bending moments in the connectors, thereby synergistically enhancing wave resistance and motion stability. Further analysis reveals that the balanced moment of inertia and geometric characteristics of hexagonal floating arrays enable superior motion response performance across all wave incidence angles.
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  INVESTIGATION ON AERODYNAMIC INSTABILITY CHARACTERISTICS OF LARGE-ASPECT-RATIO SINGLE-AXIS PHOTOVOLTAIC SUPPORT

  Cao Nankui, Niu Huawei, Li Hongxing, Jiang Dong, Yi Ziyan, Chen Zhengqing

  2026, 47(12): 130-141. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1090

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  Existing stability curves derived of single-axis tracker with aspect ratios λ≤10.9 require validation for the applicability of structures with higher aspect ratios. Therefore, the aerodynamic stability of a single-axis tracker with an aspect ratio of 20 is investigated through full aeroelastic model wind tunnel tests under varying tilt angles, column heights, turbulence intensities, and wind directions. The results demonstrate that wind-induced torsional aerodynamic instability occurs within the tilt angle range of -20°≤α₀≤ 20° after reaching specific critical wind speeds, representing a reduction in the instability range compared to existing studies. Aerodynamic stability under positive tilt angles is superior to that under negative tilt angles, with significant static wind-induced torsional angles observed at the structure ends prior to instability. When the column height increases to 2.5 m, the difference in aerodynamic stability between positive and negative tilt angles substantially diminish. At wind directions of β=60° and 90°, the unstable tilt angle range remain is identical to that at β=0°, consistent with existing research. However, at β=30°, the unstable tilt angle range expands to -30°≤α₀≤30°, and local maxima in the overturning force coefficient occurrs at α₀=±30°, exacerbating the non-uniform spanwise aerodynamic force distribution. Under turbulent flow conditions, the critical instability wind speed for the flat single-axis PV structure significantly decreases, and the unstable tilt angle range broadens to -30°≤α₀≤30°. Turbulent fluctuating excitations reduce the aerodynamic stability of the structure. Consequently, the unstable tilt angle range varies for structures with different aspect ratios under diverse turbulence intensities and wind directions, necessitating further investigation.
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  MECHANISM AND SUPPRESSION STRATEGY OF FORCED OSCILLATION TRIGGERED BY INTERHARMONICS IN LARGE-SCALE GRID-CONNECTED PHOTOVOLTAIC SYSTEMS

  Ge Jinming, Zhai Junlong, Li He, Wang Zhenhao, Wang Chaobin, Dong Yuwei

  2026, 47(12): 142-152. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1091

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  Maximum power point tracking （MPPT） control for large-scale photovoltaic power generation leads to a large amount of interharmonics output from PV inverters, which may trigger forced power oscillations of other photovoltaic power generation system at the confluence end. In this regard, the forced oscillation mechanism of the forced system triggered by interharmonics in PV power plants is analyzed and a suppression strategy combining phase-shift compensation and active damping is proposed. Firstly, a typical model of a large-scale PV power plant and a PV power generation unit is established, and the mechanism of MPPT control generating interharmonic disturbance sources is analyzed. Then, on the basis of analyzing the inherent modes of the forced system, the mechanism of the forced oscillations of the forced system triggered by the interharmonic perturbation source is elaborated using the equivalent perturbation equation. Subsequently, the effects of the interharmonic disturbance source amplitude and the forced system damping on the forced oscillation amplitude are considered, and a forced oscillation suppression strategy combining phase-shift compensation and active damping is proposed. Finally, the effectiveness of the suppression strategy proposed in this paper in reducing the oscillation amplitude is verified by simulation analysis.
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  SHORT-TERM PHOTOVOLTAIC POWER PREDICTION OF EXTREME WEATHER DRIVEN BY DATA-MODEL COOPERATION

  Chen Pengyu, Mei Jianwei, Tuo Mingjian

  2026, 47(12): 153-162. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1108

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  To address the problem that photovoltaic power prediction under extreme meteorological conditions is limited by sample scarcity and insufficient generalization ability, this paper designs a heterogeneous hybrid framework fusing the physically constrained time-series generative adversarial network （TimeGAN-PC） and the snow ablation-optimized long short-term memory neural network （SAO-LSTM）. Firstly, key influencing factors such as radiation index and air temperature are extracted based on the Pearson correlation coefficient method. Then, TimeGAN-PC is used to generate extreme meteorological data that satisfy the nighttime zero-power constraint, the photoelectric conversion efficiency boundary constraint, and the inclined-plane radiation conservation constraint. Finally, the snow ablation algorithm is adopted to optimize core hyperparameters of the LSTM such as the learning rate, so as to improve the prediction accuracy of the model. The experimental results under extreme meteorological conditions show that the proposed prediction model achieves better performance than traditional models.
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  SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON VMD AND DUAL ATTENTION MECHANISM

  He Hucheng, Cao Haoyu, Sun Kai, Luo Yu

  2026, 47(12): 163-172. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1110

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  To address the challenges of strong non-stationarity in PV power sequences and low accuracy of single-model prediction, this paper proposes a short-term PV power prediction method optimized by variational mode decomposition （VMD） and a dual attention mechanism. Firstly, VMD is employed to decompose the original power signal into multiple frequency mode sub-sequences, and the separate prediction models are constructed for each sub-sequence to effectively capture spatial features and temporal dependencies. Then, a convolutional neural network enhanced with a channel attention mechanism （CNN-SE） is designed to improve the recognition of key features. Meanwhile, a self-attention mechanism is integrated to further exploit long-term temporal dependencies within the long short-term memory （LSTM） network. The final output is obtained by weighted fusion of all sub-sequence predictions. Case studies based on real-world data from a PV power station in Australia, along with comparative analysis against other mainstream methods, demonstrate the effectiveness of the proposed approach in handling non-stationary data and improving prediction accuracy.
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  PREPARATION OF HIGH-EFFICIENCY WIDE-BANDGAP PEROVSKITE PHOTOVOLTAIC CELLS BY INHIBITING PHASE SEPARATION WITH BAC MODIFICATION LAYER

  Li Zhuoxin, Feng Xuzheng, Liu Xuewu, Zheng Liming, Li Xingxi, Cai Molang

  2026, 47(12): 173-179. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1131

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  Benzalkonium chloride is employed as a surface modifier to suppress photoinduced phase separation of the mixed-halide wide-bandgap perovskite materials. The long-chain alkyl groups of BAC can form a dense hydrophobic barrier on the surface of the perovskite film, blocking the penetration of environmental moisture to inhibit moisture-induced halide ion migration, thereby effectively enhancing the phase stability of perovskite and suppressing the occurrence of phase segregation. Device characterization results reveal that the BAC-modified wide-bandgap perovskite solar cells exhibite notable performance improvements, achieving a maximum VOC of 1.22 V and a PCE of up to 21.24%. Furthermore, after continuous illumination for 500 hours in an ambient atmosphere with a relative humidity of 50%, the perovskite photovoltaic cell retains 83% of its initial efficiency, demonstrating favorable moisture resistance and operational reliability.
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  RESEARCH ON RELIABILITY OF PHOTOVOLTAIC MODULE FOR DESERT ENVIRONMENTS

  Han Xiaoliang, Hou Peng, Diao Yunchao, Qin Xiao, Zhang Siliang, Ma Di

  2026, 47(12): 180-186. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1153

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  Focusing on the application characteristics of photovoltaic （PV） modules in desert environments, this study presents the current status of desert PV development in China. Drawing on the PV power generation project at the Ulanbuhe new energy base, weather-resistant materials are used to prepare photovoltaic modules （referred to as desert photovoltaic modules） for the desert environment, and a series of strict reliability tests are conducted on the modules, including light-induced degradation, thermally assisted light-induced degradation, potential-induced degradation, thermal cycling, humid heat testing, and mechanical stress sequence testing; targeted testing in desert environments such as UV sequence testing and dust testing; and power generation system stability tests such as wind tunnel testing. The test results indicate that the power degradation values of the desert PV modules are all lower than those specified in the IEC standards. From a testing perspective, this validates that desert PV modules can withstand desert environments and that the system is stable and reliable. An outdoor demonstration project was conducted at the Ulanbuhe new energy base to monitor the actual power generation of desert PV modules and conventional modules. The data shows that desert PV modules generate 4.89% more power than conventional PV modules, demonstrating from a practical perspective that desert PV modules are better suited to the desert environment and deliver higher power generation benefits.
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  NUMERICAL SIMULATION STUDY ON IMPROVEMENT OF SOLAR RADIATION FORECAST IN WESTERN INNER MONGOLIA REGION UNDER DUSTY CONDITIONS

  Han Li’na, Xu Li’na, Wang Zhinan, Zhao Jiaqi, Hu Ya’nan

  2026, 47(12): 187-197. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1081

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  In spring, the Inner Mongolia region frequently experiences sand and dust weather, characterized by high intensity and extensive coverage, which significantly weakens solar radiation reaching the surface. This study utilizes the WRF-Solar model to optimize and improve solar radiation forecasting under dust conditions in western Inner Mongolia, taking into account the sensitivity of the WRF-Solar parameterization schemes and the impact of aerosols on solar radiation. Preliminary conclusions are as follows： 1） During and after dust events, significant differences are observed among sensitivity experiments of the model, indicating varying sensitivities among different parameterization scheme combinations, which is more conducive to optimizing the model's forecasting performance; 2） Under the influence of moderate and strong dust events, the Lin microphysics scheme and the Betts-Miller-Janjic cumulus convection scheme demonstrate relatively good simulation performance, while the RRTMG longwave and shortwave radiation schemes show superior results in simulating strong dust events. Additionally, the model's simulation performance for strong dust events is found to be better than that for moderate dust events. Under the significant influence of severe and extreme dust events, the Kain-Fritsch cumulus convection scheme can improve solar radiation simulation to some extent; 3） After comprehensive consideration, an optimal combination of parameterization schemes is selected, namely the RRTMG longwave radiation scheme, RRTMG shortwave radiation scheme, Betts-Miller-Janjic cumulus convection scheme, and the aerosol-aware Thompson microphysics scheme; 4） After accounting for the impact of aerosols on solar radiation, the WRF-Solar model exhibits notably improved performance.
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  A BENCHMARK MODEL FOR SOLAR IRRADIANCE FORECASTING： ATMOSPHERIC TRANSPARENCY FACTOR PERSISTENT MODEL

  Xu Ximeng, Ma Chao, Deng Zexing, Liu Run

  2026, 47(12): 198-204. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1126

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  To address the issue of inadequate and inaccurate physical information guidance in solar irradiance forecast models, an atmospheric transparency factor persistent model （ATF-P） is proposed. After accurately modeling the solar irradiance propagation process, the deterministic and stochastic components in the solar irradiance time series are decoupled, and the steady atmospheric transparency factor is extracted as the key variable to construct a persistent forecast model. A case study using solar irradiance data from a location in Sichuan province shows that the root mean square error （RMSE） metric of the proposed ATF-P model is 55.59 W/m². Compared with other models, it has stronger consistency with physical laws, higher forecast accuracy, and simpler modeling costs, making it suitable for promotion and application as a new benchmark model.
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  MULTI-TIMESCALE LONG-TERM SOLAR IRRADIATION PREDICTION METHOD FOR PHOTOVOLTAIC INSURANCE

  Du Mingcong, Li Xinli, Yang Guotian, Gu Guogang, Lin Bin

  2026, 47(12): 205-216. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1137

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  This paper proposes a long-term solar irradiation prediction method based on adaptive signal decomposition and intelligent joint prediction. Firstly, a recursive decomposition strategy is designed to adaptively decompose the original sequence into a series of stationary sub-sequences at different timescales, i.e., intrinsic mode functions （IMFs）, so as to reduce the difficulty of prediction. According to the characteristics of each decomposed sub-sequence, the seasonal autoregressive integrated moving average （SARIMA） model and the bidirectional long short-term memory （BiLSTM） network are adopted for prediction respectively. In particular, three multi-head attention modules capturing monthly, seasonal and annual features are connected to the output layer of BiLSTM to extract multi-timescale characteristics. Finally, the final prediction result is reconstructed by integrating the prediction outputs of all sub-sequences. The proposed method is validated using actual solar irradiation data from a certain area in Inner Mongolia. Experimental results show that the proposed method achieves the best performance in terms of evaluation metrics including mean squared error （MSE） and coefficient of determination （R²）. Specifically, the R² value reaches 97.92%, representing improvements of 1.38 and 5.70 percentage points over the STL-EMD-VMD fixed decomposition strategy and the single-timescale attention-based BiLSTM model, respectively. Compared with the Informer algorithm, the proposed method yields a 2.23 percentage point improvement. In addition, the proposed joint prediction method outperforms the multi-timescale BiLSTM prediction model alone by 1.46 percentage points.
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  ENERGY CAPACITY CONFIGURATION OF WIND-SOLAR-STORAGE MICROGRID CONSIDERING POWER INTERACTION

  Zhang Fan, Lin Zeyuan, Han Benshuai, Wang Zongyao, Hu Shaoyi

  2026, 47(12): 217-224. https://doi.org/10.19912/j.0254-0096.tynxb.2024-0742

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  In the grid-connected operation mode, the microgrid needs to exchange power with the main grid in both directions to balance its own power demand. When the output power of the wind and solar power generation system fluctuates rapidly, the power exchange between the microgrid and the main grid becomes unstable, resulting in frequent adjustments of power exchange. If there is a lag between the capacity of energy storage equipment and this response, it will lead to instability in the internal voltage and frequency of the microgrid. Therefore, a comprehensive energy capacity allocation method for wind, solar, and storage microgrids is proposed. Power interaction constraints are specifically considered. By establishing a capacity allocation model that includes a cost function and a reliability function, and introducing power interaction as a key constraint condition into the model, it is ensured that when the output power of the wind and solar power generation system fluctuates, the power exchange between the microgrid and the main grid can remain stable, avoiding excessive power fluctuations that impact system stability. In order to solve this complex multi-objective function problem, an improved dung beetle algorithm is introduced to achieve global search and optimization by simulating the foraging behavior of dung beetles in nature. This algorithm can find an energy capacity allocation scheme that optimizes both the cost function and reliability function under the constraint condition of power interaction. The results show that the microgrid system configured using the method proposed in this study is superior to traditional methods in terms of operating costs and load loss probability. This method not only reduces operating costs, but also significantly reduces load loss events and improves power supply reliability. This achievement fully demonstrates the superiority and innovation of the method proposed in this study in energy capacity allocation for microgrids.
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  LOW-VOLTAGE RIDE-THROUGH IMPROVEMENT STRATEGY FOR INVERTER-INTERFACED DISTRIBUTED GENERATION CONSIDERING SECONDARY VOLTAGE DIPS

  Dai Zhihui, Zhang Yu, Xu Dongrui, Han Tao, Yu Haiping

  2026, 47(12): 225-232. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1013

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  To address the issue of adjacent-line inverter-interfaced distributed generation（IIDG） disconnection caused by secondary voltage dips at the point of common coupling（PCC） after front-accelerated auto-reclosing operates on a permanent fault, an improved low-voltage ride-through（LVRT） strategy applicable to the distribution network with reclosers is proposed. Based on the LVRT specifications of IIDG, the impact of auto-reclosing on LVRT is analyzed, and the successful LVRT process is divided into three stages （voltage dip, voltage recovery, and power restoration）. Prior to auto-reclosing actuation, the adjacent-line IIDGs terminate the ongoing LVRT process in advance by identifying these stages. When secondary voltage dips occur due to front-accelerated auto-reclosing on a permanent fault, the adjacent line IIDGs can initiate a new LVRT cycle to prevent grid disconnection. Simulation results verify that the proposed strategy avoids IIDG disconnection during secondary voltage dips without altering the original fault recovery scheme, exhibiting high practical value.
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  MIES TWO-LAYER OPTIMAL SCHEDULING CONSIDERING SOURCE-LOAD UNCERTAINTY

  Ma Yun, Zhang Bo, Wu Xiaojie

  2026, 47(12): 233-242. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1083

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  In the context of high-proportion renewable energy integration and deepened energy coupling, to improve the energy utilization reliability and stability of the mine integrated energy system （MIES） and overcome the impact of wind-solar output and load prediction errors on the scheduling plan, a two-layer optimal scheduling scheme for MIES considering source-load uncertainty is proposed. Firstly, the energy supply structure and equipment models of MIES are established, covering electricity, heat, cold and other energy forms as well as mine-derived energy devices. Secondly, based on multi-scenario technology and fuzzy mathematics theory, the day-ahead and intra-day uncertainties of renewable energy output and load demand are modeled. Latin hypercube sampling is used to generate wind-solar-load scenarios, and clustering reduction is applied to obtain typical scenarios. Fuzzy chance-constrained programming is utilized to relax the power balance constraints of intra-day scheduling into the constraints under a certain confidence level. Finally, in the uncertain environment, day-ahead scheduling is conducted aiming to minimize the mine operation cost and carbon emission cost, and intra-day flexible adjustments are made based on the day-ahead plan. Taking a coal mine in Hulunbuir, Inner Mongolia as a case study, the results show that the proposed method can balance the risk and return of day-ahead and intra-day scheduling plans, reduce operation and carbon emission costs, and ensure the safety and reliability of energy supply. This method is of great significance for promoting the optimal operation and sustainable development of MIES.
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  RESEARCH ON OPTIMAL DISPATCH STRATEGY OF RURAL MULTI-MICROGRID SYSTEM CONSIDERING ECONOMIC AUTONOMOUS OPERATION

  He Dong, Zou Yuxin, Lan Zheng, Zeng Jinhui

  2026, 47(12): 243-252. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1084

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  In order to improve the operation economy and autonomy of rural microgrid, an optimal scheduling strategy of multi-microgrid system is proposed, which considers economy, environmental costs and microgrid autonomy . Firstly, the rural multi-microgrid system structure and distributed power output model composed of three different types of rural microgrids are established, based on which the rural multi-microgrid system optimal scheduling model is constructed. Secondly, improved bald eagle search algorithm （IBES） based on Bernoulli chaotic mapping, Levy flight, nonlinear weight factor and adaptive t-distribution mutation strategy is proposed to enhance the efficiency and accuracy of the optimal scheduling model solution. Finally, MATLAB simulation software is used to build the rural multi-microgrid system model, and the proposed optimal scheduling method of rural multi-microgrid system is verified by comparative analysis. The results show that compared with the traditional bald eagle search algorithm （BES）, IBES improves the system economy and autonomy by 15.75% and 9.51%, respectively. In addition, the optimized scheduling strategy proposed in this study improves the autonomy by about 19.00% compared with the conventional strategy without considering autonomy. Therefore, the proposed optimal scheduling model and the IBES proposed in this paper can rationally distribute rural distributed generation and effectively improve the economic efficiency of the system and the autonomy of microgrid, which is of great significance for the development of new rural power distribution systems.
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  PRE-SYNCHRONIZATION CONTROL PI PARAMETER TUNING AND APPLICATION SCHEME ORIENTED TOWARD POWER-FREQUENCY FLUCTUATION SUPPRESSION

  Ma Jiajun, Yao Ruizhe, Li Yongkang, Wang Ning, Zhao Zhixuan, Wang Xiaoyi

  2026, 47(12): 253-263. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1085

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  To address the challenges associated with PI parameter tuning and the complexity of engineering implementation in pre-synchronization control of virtual synchronous generators （VSG） with virtual power integration, this paper proposes a PI parameter tuning and application scheme based on a bi-level optimization model, with a focus on suppressing coupled power-frequency fluctuations. To achieve a balance between power-frequency stability and synchronization speed, a simulation-algorithm bi-level optimization framework is established, where an improved particle swarm optimization algorithm is utilized to optimize the PI parameters of the pre-synchronization controller. Furthermore, to enhance the practical applicability of the control strategy, an ‘offline optimization-online lookup’ approach is designed for PI parameter deployment. Simulation results show that the proposed scheme reduces the maximum frequency fluctuation of the inverter output during pre-synchronization by 26.5% and the maximum power fluctuation by 52.1%, thereby verifying the effectiveness of the proposed method.
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  LOW-CARBON ECONOMIC DISPATCH OF INTEGRATED ENERGY SYSTEM CONSIDERING AA-CAES AND BIOMASS GAS

  Li Xiaodong, Li Yuwen, Ji Chengchen, Li Guoxiang, He Jiayang, Kong Jian

  2026, 47(12): 264-273. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1096

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  To improve energy utilization efficiency and accelerate the low-carbon transformation of China’s energy structure, a low-carbon economic model for integrated energy systems incorporating advanced adiabatic compressed air energy storage and biomass gas was constructed. First, an advanced adiabatic compressed air energy storage and combined heat and power coupling collaborative heating model was established to achieve heat-power decoupling. Second, a biomass gas multi-energy coupling model considering fermentation temperature factors was constructed, which improves biomass energy utilization while producing natural gas in a more economical and efficient manner. Meanwhile, entropy weight-based information gap decision theory was introduced to objectively describe the uncertainties on both source and load sides. A low-carbon economic dispatch model for integrated energy systems considering stepped carbon trading mechanism and source-load uncertainties was established. The multiple scenarios are established for comparative analysis to validate that the proposed model can enhance economic benefits.
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  LOW-CARBON ECONOMIC DISPATCH OF INTEGRATED ENERGY SYSTEM CONSIDERING WIND POWER-PHOTOVOLTAIC SYNTHESIS OF GREEN AMMONIA AND AMMONIA -BLENDED GAS

  Cao Hui, Yang Peihong, Zhang Xiaoming, Zhang Xin, Zhang Jihong, Fan Lulu

  2026, 47(12): 274-285. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1116

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  To fully tap the renewable energy consumption capacity and carbon reduction potential of the integrated energy system（IES）, the IES low-carbon economic dispatching method of synthesizing green ammonia from wind and solar power and blending ammonia in gas is proposed. First, establish an IES scheduling framework integrating power-to-ammonia （P2A）, power-to-gas （P2G）, and combined heat and power （CHP） with dynamic ammonia blending ratios, and mathematically formulate the operational models of P2A, P2G, and the combustion of ammonia-blended fuel in CHP units. Second, with the goal of minimizing the total operating cost of the system and carbon emissions, considering the output constraints of each unit, the IES low-carbon economic scheduling model is established, and the uniformly distributed Pareto frontier solution is obtained by using the normalized normal constraint （NNC） method. Then, the optimal solution is selected using the technique for order preference by similarity to ideal solution （TOPSIS） method. Finally, the feasibility of the method proposed in this paper is verified by setting five application scenarios as examples. Results demonstrate that the proposed P2A-P2G-CHP integrated framework with dynamic ammonia blending ratios reduces total operating costs by 26.02% and carbon emissions by 10.78% compared to conventional IES, providing an effective pathway for low-carbon economic dispatch.
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  ANALYSIS OF VOLTAGE SUPPORT MECHANISM AND INFLUENCING FACTORS IN HYBRID GRID-FOLLOWING/GRID-FORMING RENEWABLE ENERGY POWER PLANTS

  Sun Dawei, Yu Siqi, Deng Xiaoyang, Liu Sijia, Gao Benfeng, Zhang Siyu

  2026, 47(12): 286-298. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1122

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  In renewable energy stations, converting grid-following units to grid-forming control can effectively enhance voltage support capability of the system. However, the underlying mechanism of how grid-forming conversion impacts system voltage support remains unclear, and the operational principles of key influencing factors require further investigation. To address these issues, this study first analyzes the voltage support characteristics of grid-forming converters and the relationship between their equivalent impedance and control parameters. Second, typical case studies are established for both full and partial conversion scenarios of renewable energy stations. The analytical expressions for the multiple renewable energy station short-circuit ratio （MRSCR）, incorporating grid-forming converter equivalent impedance, are derived for each scenario to reveal the mechanism through which grid-forming conversion enhances the voltage support capability of grid-following stations. Subsequently, a correlation model between grid-forming converter equivalent impedance and MRSCR is developed to analyze the influence of grid-forming control parameters on system voltage support capability. Finally, validation is performed using the MATLAB/Simulink simulation platform. The results demonstrate that grid-forming conversion reconstructs the system node impedance matrix by introducing the equivalent impedance of grid-forming converters and new nodes, thereby increasing the MRSCR and voltage support capability of grid-following stations. In hybrid grid-following/grid-forming renewable energy stations, reducing the reactive-voltage droop coefficient and virtual impedance decreases the equivalent impedance of grid-forming converters, consequently increasing the MRSCR and enhancing the voltage support capability of grid-following stations. These findings provide theoretical support for the collaborative optimization of capacity configuration and control parameters in hybrid renewable energy stations.
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  TRANSIENT VOLTAGE STABILITY ASSESSMENT CONSIDERING SAMPLE IMBALANCE AND SPATIO-TEMPORAL FEATURE FUSION

  He Jiacheng, Zhang Xuexia

  2026, 47(12): 299-309. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1136

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  To improve the performance of transient voltage stability assessment models for power systems, this paper proposes a spatiotemporal feature modeling approach that integrates an improved graph attention network （GAT） with a Transformer. Considering the inherent sample imbalance that may lead to missed detection in assessment, a Wasserstein generative adversarial network with gradient penalty （WGAN-GP） is further introduced for data augmentation. The proposed model extracts the temporal evolutionary features of transient voltage via the Transformer module, and incorporates an impedance-aware edge prior into the graph attention branch, thereby enhancing the spatial representation's ability to capture the physical coupling strength of the power grid. Meanwhile, the WGAN-GP network generates high-quality unstable samples to alleviate the class imbalance issue. Simulation results on the modified New England 39-bus system demonstrate that the proposed model effectively reduces the missed detection of instability and improves assessment accuracy. Furthermore, the model exhibits strong robustness even under noise interference and extreme class imbalance conditions.
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  ISADE-BASED STACKELBERG GAME OPTIMIZATION STRATEGY FOR LOW-CARBON MULTI-ENERGY MICROGRID OPERATION

  Zhang Xin, Wang Changyuan, Li Wanming, Wang Lidi

  2026, 47(12): 310-319. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1139

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  To reduce carbon emissions and operational costs in microgrid operation,a low-carbon economic operation strategy for multi-energy microgrids integrating shared energy storage, carbon capture power plants, and power-to-gas collaborative operation is proposed in this study. A tiered carbon trading mechanism is introduced to control carbon emissions. The improved simulated annealing and differential evolution （ISADE） algorithm is employed for Stackelberg game iterative solving. This algorithm incorporates parameter adaptive adjustment,hybrid mutation strategies,and an elite retention strategy. The proposed model is evaluated through numerical simulations on the MATLAB platform. The results demonstrate that the model achieves 39% faster convergence speed and higher solution accuracy compared to conventional methods, while effectively avoiding convergence to local optima. A 79% reduction in carbon emissions, a 17% decrease in microgrid operational costs, and a 10% reduction in user costs are achieved, ultimately maximizing benefits for both microgrid operators and end-users.
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  STUDY ON PERFORMANCE OF FLOATING WIND TURBINE CONSIDERING EFFECT OF TOWER DEFORMATION

  Ma Lu, Zhou Le, Zhang Xianfeng, Guo Hao, Shen Xin, Du Zhaohui

  2026, 47(12): 320-327. https://doi.org/10.19912/j.0254-0096.tynxb.2025-0128

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  Based on the lift-line free vortex wake model and Euler beam model, the influence of tower deformation on the performance of the floating wind turbine is studied in this paper. The results show that the tower deformation will lead to the load decrease of the blade and wind turbine cause a delay in loads response under the floating platform movement condition. Moreover, the deformation of the tower will cause the unbalanced distribution of the load at the rotor plane, which leads to the significant fluctuation of the tilt and yaw moments of the wind turbine. under the surge condition, significant vibration will occur in the axial direction of the tower, and the additional speed introduced by the vibration will reduce the normal inflow velocity of the blades and thus reduce the loads of the wind turbine.
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  RESEARCH ON WIND POWER PREDICTION METHODS BASED ON WAA-ICEEMDAN AND TENT-ESN

  Yang Ningning, Zhang Tian, Wu Chaojun, Zhang Chen, Dong Minjie, Ma Zhirui

  2026, 47(12): 328-336. https://doi.org/10.19912/j.0254-0096.tynxb.2025-0919

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  Aiming at the problems of non-stationarity, obvious seasonal fluctuation and poor real-time performance in short-term wind power forecasting, this paper proposes a wind power prediction model combining WAA-ICEEMDAN and echo state network improved by Tent chaotic map. The weighted average operator is adopted to optimize ICEEMDAN to restrain mode mixing, and the Tent map replaces the activation function of ESN to strengthen the capture of abrupt time-series features. Simulations are performed on full-year four-season measured data from a wind farm, with comparative experiments against tanh-ESN, VMD-ESN, Transformer, Informer and Autoformer. The results show that the proposed model achieves the optimal RMSE, MAE and MAPE under all seasonal conditions, and its prediction errors are barely affected by seasons. With an inference time of approximately 3.2 s, the model balances prediction accuracy, seasonal robustness and low computational latency, and is applicable to short-term real-time wind power forecasting of wind farms.
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  STUDY ON DYNAMIC INTERFERENCE OF DOCBLE SEMI-SUBMERGED FLOATING OFFSHORE WIND TURBINES CONSIDERING HYDRODYNAMIC INTERACTION EFFECTS

  Wu Bang, Hua Xugang, Wang Wenxi

  2026, 47(12): 337-347. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1011

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  This paper conducts a numerical simulation study on the hydrodynamic interactions problem of double semi-submersible floating bodies under the action of wind and waves. Specifically, two 5 MW semi-submersible wind turbines aligned in tandem in the same wind farm are investigated. The research employs time-domain numerical simulations to assess the influence of turbulent wind and second-order Stokes wave effects on the hydrodynamic interactions and their consequent impact on the structural dynamic response of the downstream turbine. The analysis considers varying wave lengths （L=60-120 m） and center-to-center spacing distances （D=160-300 m）. The findings reveal that, for a fixed center-to-center distance, both the peak and mean heave motions initially increase and subsequently decrease as the wavelength increases, attaining a maximum at approximately 90 m. In contrast, the maximum values and standard deviations of pitch and roll motions exhibit an increasing trend with wavelength, whereas their mean values decline. Regarding hydrodynamic forces, the maximum diffraction and Froude-Krylov （FK） forces both display a non-monotonic behavior, with the diffraction force peaking at a wavelength of 90 m and the FK force reaching its maximum at 80 m. When the wavelength is held constant, increasing the center-to-center distance from 160 m results in maximum differences exceeding 6% in all statistical measures of heave motion, with standard deviation differences surpassing 11%. Conversely, variations in pitch and roll statistics remain within 5%. The diffraction force shows an initial increase followed by minor fluctuations, while the FK force’s maximum value remains essentially constant as spacing increases. These results underscore the necessity of accounting for hydrodynamic interactions among floating structures when analyzing dynamic interference effects in semi-submersible wind turbine arrays. Comparative analysis with a single turbine under identical environmental conditions indicates that hydrodynamic interference effects on the dynamic response of the downstream turbine become negligible when the center-to-center spacing reaches 400 m.
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  EFFECTS OF WIND FARM WAKES ON SAND AND DUST TRANSPORT IN DESERT-GOBI-WASTELAND AREAS

  Li Deshun, Xie Chi, Zang Zhizhao, Dong Yanbin, Yan Wei

  2026, 47(12): 348-356. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1036

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  Focusing on the persistent sand-dust weather in Northwest China, this study investigates the impact of wind farm wakes on dust transport using a coupled sand and dust numerical model （WRF-WFP-DUST）. The model integrates a mesoscale meteorological model with a wind farm parameterization scheme. The research takes the typical flat-terrain wind farm cluster in Alxa League, Inner Mongolia Autonomous Region, as the study object and simulates a regional sandstorm event in March 2021.The findings reveal： A significant wind speed attenuation effect occurs within the wind farm cluster area. The degree of attenuation exhibits distinct variation characteristics depending on the number of wind farms arranged in tandem. The maximum wind speed attenuation （38.5%） occurred at the third wind farm within the cluster. Sand-dust concentrations display different characteristics in the horizontal and vertical directions. Downstream of the wind farms, along the wake direction, the dust distribution exhibits distinct symmetry. When the number of tandem wind farms reaches three, a substantial influence on sand and dust transport occurs at the hub height plane, where the concentration decreases by 12.2%. The hindering effect is slightly less pronounced at the upper tip height plane and the lower tip height plane, with concentration reductions of 10.9% and 7.4%, respectively.
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  ULTRA-SHORT-TERM WIND SPEED FORECASTING BASED ON SPATIO-TEMPORAL FEATURE FUSION OF HYBRID DEEP LEARNING MODEL

  Liu Zeqi, Wang Weiqing, Li Xiaozhu, Zheng Jirong

  2026, 47(12): 357-369. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1037

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  This paper proposes a hybrid deep learning model integrating convolutional neural network （CNN）, bidirectional gated recurrent unit （BiGRU）, and attention mechanism for ultra-short-term wind speed forecasting. Firstly, the sliding window algorithm is adopted to reorganize the wind speed time series into multiple small-sample datasets, and a CNN is used to extract spatial features from the samples. Secondly, a BiGRU is adopted to extract temporal features from the preprocessed data. Finally, an attention mechanism is introduced to fuse and screen the spatial and temporal features of each time step, so as to realize accurate wind speed prediction. This paper conducts comparative experiments on datasets with different prediction steps and input feature combinations, as well as ablation experiments with different network layers and model structures. Furthermore, the proposed model is compared with multiple models of varying complexity to verify its effectiveness. The results demonstrate that the proposed data processing method and prediction model can achieve high-precision ultra-short-term wind speed forecasting, with low model complexity, excellent robustness and superior generalization performance.
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  DYNAMIC STALL CHARACTERISTICS AND FLOW MECHANISM OF WIND TURBINE AIRFOIL UNDER COUPLING OF WIND, WAVE AND CURRENT

  Hu Chuanxin, Zhou Tao, Huang Wen, Zhao Lin, Wu Kaiming

  2026, 47(12): 370-378. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1087

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  This study employs overset grid technology to numerically simulate the pitch-plunge dynamic stall of the DU93-W-210 airfoil under wind-wave-current coupling. Three conditions are analyzed： pure wind, non-synchronous frequencies （fa≠fw）, and synchronous frequencies （fa=fw）. Results demonstrate the simulation's stability and effectiveness. The coupled input excites additional low-frequency components and increases aerodynamic energy input. Dynamic stall angles decrease progressively： 22.9° （pure wind）>21.7° （non-synchronous）>11.1° （synchronous）. Non-synchronous waves induce early flow separation at high angles of attack, while synchronous coupling causes significantly premature stall. Fluctuating pressure on the airfoil upper surface exhibits wave-like changes, characterized spatially by forward-propagating wave crests with decreasing peaks followed by backward propagation with increasing peaks. Temporally, the sequence of minimum pressure occurrence differs： non-synchronous precedes pure wind, and synchronous precedes non-synchronous. This timing aligns with stall angle variation, indicating that separation bubble evolution （expansion/contraction, trailing-edge vortex drift） governs the stall process.
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  MOTION RESPONSE CHARACTERISTICS OF MODEL TURBINE WITH THREE ROTATIONAL DEGREES OF FREEDOM

  Zhang Buen, Zou Ying, Ye Ye, Xiao Duanlong, Zhao Zhenzhou, Liu Huiwen

  2026, 47(12): 379-385. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1088

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  In this paper, a test scheme of wind turbine coupled with irregular, multi-degree-of-freedom rotating motion is proposed. Through physical model tests, the motion response characteristics of wind turbine under different yaw angles are studied under turbulent conditions. The results show that the rolling motion and pitching motion of wind turbine are low frequency, while the yawing motion is high frequency. In the wind turbine incoming flow, the low-frequency turbulent structure is more likely to cause the floating wind turbine pitching motion, while the high-frequency turbulent structure is more likely to cause the floating wind turbine rolling motion. When the yaw angle of the wind turbine is 0°, the rolling motion is the main advantage among the three rotating motion components of the floating wind turbine, while when the yaw angle of the wind turbine is 30°, the pitching motion is the main dominant rotating motion component. With the increase of the yaw angle, the oscillation amplitude of the roll motion of the wind turbine tends to decrease, while that of the pitch motion tends to increase.
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  COUPLED WIND-WAVE-CURRENT DYNAMIC ANALYSIS OF OFFSHORE WIND TURBINE BLADE HOISTING AND MATING

  Xia Hongjian, Cheng Honglong, Zhang Qifu, Hong Haowu, Li Deyuan

  2026, 47(12): 386-395. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1102

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  To investigate the vibration characteristics during the hoisting of large offshore wind turbine blades, a "super element" method is employed to discretize the ultra-long flexible blades and the wind turbine tower, establishing a multibody dynamics model of the hoisting system. Based on the loading characteristics during blade hoisting, the aerodynamic loads on the hoisting structure are established by combining blade element theory and an unsteady aerodynamic model. The wave-current loads on the tower are modeled using Morison’s equation. A coupled aero-hydro-elastic model of offshore blade hoisting is then developed through co-simulation. Using the DTU large wind turbine as a case study, the vibration characteristics under both steady and turbulent wind conditions during blade hoisting are analyzed. Furthermore, the external environmental conditions required for successful blade mating are quantitatively evaluated using the out-crossing rate.
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  RESEARCH ADVANCES IN SYSTEM IDENTIFICATION METHODS FOR DYNAMICS OF FLOATING WIND TURBINE FOUNDATIONS

  Han Xu, Song Yaxin, Zhang Chen, Wang Qunyi, Shi Wei, Li Xin

  2026, 47(12): 396-408. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1113

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  This paper presents a comprehensive review of the state-of-the-art in the field of structural dynamic system identification, with a focus on floating offshore wind turbine （FOWT） systems. It also draws on relevant research on fixed offshore wind turbines and other floating marine structures.The review focuses on the system identification of FOWT foundation and mooring systems. It thoroughly evaluates the multi-source influences from the uncertainties of structural and environmental monitoring as well as dynamics modelling on identification results. The recent advances in probabilistic identification methods are described in depth, followed by summarizing the relevant theoretical challenges in complex dynamic system identification for large-scale floating wind turbines. This review aims to provide solid technical support and theoretical references for the steady industrial development of horizontal- and vertical-axis FOWTs in China, while facilitating breakthroughs in relevant technologies and their practical applications.
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  STUDY ON GRID-CONNECTED VOLTAGE INTENSITY AND CONVERTER POWER CAPACITY CONFIGURATION OF GRID-FORMING DOUBLY-FED WIND TURBINES

  Yan Xiangwu, Shi Peisheng, Jia Jiaoxin, Tian Shaoze, Zhou Fan

  2026, 47(12): 409-419. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1115

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  This paper investigates voltage ride-through, voltage impact mitigation during voltage sag and swell under large grid disturbances, and converter power capacity allocation for grid-forming doubly fed wind generators. Based on a virtual synchronous machine controlled grid-forming doubly fed wind turbine system, the internal relationships among grid-support strength, point of common coupling voltage sag depth, and the power capacity of the rotor-side and grid-side converters under droop control and constant-voltage control are analyzed. On this basis, a reference for converter power capacity allocation of wind turbine generators under constant-voltage control is provided. Furthermore, the causes of voltage impact under constant-voltage control are analyzed, and an improved constant-voltage control method is proposed. During grid faults, the reactive power control coefficient of the rotor-side converter is optimized to solve the overvoltage and overcurrent problems caused by conventional constant-voltage control. Simulation results show that the proposed method can effectively support grid voltage, reduce voltage impact, improve voltage and transient stability, and enhance the fault operation capability of grid-forming doubly fed wind turbine generators.
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  ANALYSIS OF HYDRODYNAMIC LOADS ON OFFSHORE WIND TURBINES UNDER WAVE-CURRENT INTERACTION CONDITIONS

  Zhang Yupeng, Chen Qingwei, Zhang Zhaohuan, Sun Jingwei, Yang Tianlang, Chen Yan

  2026, 47(12): 420-431. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1129

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  To investigate the hydrodynamic loads on a monopile offshore wind turbine tower under extreme wave-current conditions and analyze the influence of wave and current conditions on the wave field, this study examines the correlation between oceanographic factors （such as wave height, wave period, and current velocity） and hydrodynamic loads to predict the maximum hydrodynamic loads. A numerical wave tank model was established using the fluid simulation software Waves2Foam to measure and analyze wave height, wave velocity, and hydrodynamic loads on the tower under various extreme wave-current coupling conditions. The total hydrodynamic loads were precisely calculated by integrating surface stresses on the tower based on the Navier-Stokes equations. Additionally, the LightGBM algorithm was employed to predict the maximum hydrodynamic loads and evaluate the correlation between oceanographic factors and hydrodynamic loads. The results demonstrate that background currents slightly reduce extreme wave heights. When waves propagate in the same direction as the background current, the horizontal velocity of water particles increases significantly, thereby amplifying the hydrodynamic loads on the tower. The presence of the tower structure notably affects the wave field, increasing wave amplitude upstream of the tower while reducing background current velocity. Due to the strong nonlinearity of extreme wave-current interactions and complex three-dimensional flow disturbances induced by the tower, the stress integration method based on the Navier-Stokes equations captures these intense interactions more effectively than the Morison equation. Among the selected oceanographic factors, wave height exhibits the strongest correlation with the maximum hydrodynamic loads on the structure, establishing it as a critical parameter for predicting hydrodynamic loads.
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  WIND POWER INTERVAL PREDICTION BASED ON BICAMTCN AND FGFORMER

  Tian Xiaodong, Zhang Fei, Ren Xiaoying, Gao Lu, Li Jingtao

  2026, 47(12): 432-442. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1134

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  To improve prediction accuracy and address the limitations of point forecasts in capturing the uncertainty of wind power, this paper proposes a novel integrated forecasting model that combines a bidirectional convolutional attention mechanism-based temporal convolutional network （BiCAMTCN） with a Fourier-gated Transformer （Fgformer）. The BiCAMTCN module first performs hierarchical feature extraction on input wind power sequences. Its bidirectional architecture captures both forward and backward temporal dependencies, while its convolutional attention mechanism adaptively models interdependencies among multiple meteorological variables. The Fgformer module replaces the standard multi-head self-attention mechanism in Transformers with Fourier transform operations to enable efficient frequency-domain modeling. In addition, a residual gating mechanism is introduced to suppress redundant information instead of using plain residual connections. Finally, quantile regression is incorporated to produce interval forecasts at multiple confidence levels. Experimental results demonstrate that the proposed model performs well in both point forecasting and interval forecasting tasks.
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  INFLUENCE OF HORIZONTAL CYCLIC LOADING FREQUENCY ON BEARING CHARACTERISTICS OF SUCTION BUCKET FOUNDATION

  Zhang Yuan, Hao Limeng, Li Guangnian, Zhang Zhaode

  2026, 47(12): 443-451. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1141

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  To investigate the bearing characteristics of wide and shallow suction bucket foundation for offshore wind power in sandy soil foundation under horizontal cyclic loading, this study systematically investigates the failure mechanism and soil-structure interaction law of the bucket foundation under large-angle conditions with varying cyclic loading frequencies （0.1~0.9 Hz） through scaled model tests combined with ABAQUS finite element analysis. The model test combined with the finite element results show that：when the foundation is subjected to the horizontal ultimate load of large turning angle,it shows the failure mode dominated by rigid body rotation,the soil body in the passive zone produces significant shear deformation,the soil body in the active zone produces depression,the phenomenon of bucket-soil separation occurs under the foundation roof,the rotation point is transferred from the back side of the foundation to the front side of the foundation in the process of the foundation movement,and the change of the soil pressure of the roof shows the characteristics of asymmetric distribution. Under large-angle conditions, an increase in cyclic loading frequency leads to a significant increase in the amplitude of soil pressure change, which is expected to provide a reference for the anti-overturning design and long-term service performance evaluation of offshore wind power foundations.
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  ANALYSIS OF COMBUSTION CHARACTERISTICS AND SLAGGING MECHANISM OF Caragana korshinskii AND Sophora japonica BIOCHAR

  Bai Yujia, Li Xinnan, Niu Wenjing, Wang Dongbiao, Zhang Jing, Yang Yuanqing

  2026, 47(12): 452-460. https://doi.org/10.19912/j.0254-0096.tynxb.2025-1119

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  Biochars were prepared from Caragana korshinskii and Sophora japonica at pyrolysis temperatures of 240~480 ℃. Their combustion characteristics were evaluated by proximate analysis, ultimate analysis, calorific value determination, and thermogravimetric analysis, while ash composition and slagging rate were analyzed to assess slagging tendency. Scanning electron microscopy and X-ray diffraction were further employed to characterize the microstructure and mineral phase evolution of the ash. The results showed that the higher heating values of the two feedstocks increased overall after pyrolysis, reaching 27690 kJ/kg for Caragana korshinskii and 23907 kJ/kg for Sophora japonica. Caragana korshinski biochar produced at 400 ℃ exhibited the highest comprehensive combustion characteristic index of 1.864 × 10^-8 mg²/（K³·min²）, with a maximum combustion rate of 1.70 mg/min, corresponding to 3.02 and 1.98 times that of the raw Caragana, respectively. Its stable combustion index and volatile releasing index were 5.69×10^-6 mg/（K²·min） and 2.22×10^-4 mg/（K²·min）, respectively, both the highest among the tested samples. After high-temperature pyrolysis, the ignition temperature of Sophora japonica biochar increased to 327.9 ℃, 35.9% higher than that of the raw material, while its maximum combustion rate ranged from 1.01 to 1.70 mg/min, with SJ-480 reaching 1.70 mg/min. At air-blowing intensities of 0.1, 0.2, and 0.3 m/s, the slagging rates of both biochars were below the 9% reference value. The slagging rates of CK~400 were 0.65%~2.21%, while those of SJ-480 were 0.64%~2.41%. After ashing at 550 ℃, the ash of Caragana korshinski biochar pyrolyzed at 400-480 ℃ exhibited SiO₂ and MgSiO₃ phases together with a relatively dense sintered structure, which may affect the migration and aggregation of low-melting-point potassium-bearing species. In contrast, Sophora japonica biochar ash showed different mineral phase compositions and structural evolution characteristics, corresponding to its different slagging response.