To investigate the effects of pre-bend and varying backward swept geometrical design on the aeroelastic loads of flexible wind turbine blades, a rotation matrix was utilized to accurately describe the airflow velocity, blade displacement, orientation and vibration velocity at blade elements, which were introduced to modify the blade element momentum model and ultimately form an aeroelastic simulation model. Based on the DTU 10 MW blade, three blades with different backward swept geometries were designed, and static, dynamic, and aeroelastic coupling analyses were conducted on each blade variant. The study explores the effects of bend-twist coupling deformations on the blade angle of attack and the influence of increased aerodynamic force arms due to pre-bend and backward swept geometric design on root torque. The study reveals that the bend-twist coupling effects of the blades are significantly influenced by the blade's geometric shape. The backward swept blade shows the best performance in reducing flap-wise root fatigue loads by up to -11.14%, albeit significantly increasing the torsional root fatigue loads. Conversely, the pre-bend blade demonstrates a decrease in both flapwise and torsional root fatigue loads.

To achieve the lightweight design of the front nacelle base for wind turbines, a structural design process driven by topology optimization and shape optimization is proposed. Within the scope of topology optimization, a comparative analysis was conducted to assess the differences and similarities in the results obtained under various conditions, including compliance-weighted optimization for multiple load cases, minimization of the maximum compliance, and topology optimization under the influence of a single bending moment. This examination aimed to elucidate the strengths and weaknesses of each type of topology optimization model, and to identify their respective applicability ranges. Inspired by topology optimization, the geometric model is reconstructed, and shape optimization is employed to address localized strength deficiencies, reinforcing the structure through optimized design. The ultimate optimized structure achieves an 8.85% weight reduction by optimizing the available space （eliminating the base plate and bearing seat）, while fully meeting the strength design requirements. These results validate the feasibility of the proposed topology optimization method in the conceptual design of the front nacelle base.

An experimental method based on digital image correlation （DIC） for load distribution of Spiralock thread pairs is proposed, and the method is applied to study the load distribution of standard threaded pairs and Spiralock thread pairs. A 2D finite element model is developed based on the geometrical characteristics of the Spiralock thread pair and the accuracy is validated by the experimental results. Finally, the influence of thread wedge angle, pitch and modulus of elasticity on the load distribution of the Spiralock thread pair is analyzed. The results show that the DIC experimental method can identify the load distribution characteristics of Spiralock thread pair and verify the effect of Spiralock thread pair from the experimental perspective. The maximum error between the standard threaded pair experimental load ratio values and the analytical method is only 4.82%. The maximum error between the results of the experiment load distribution of the Spiralock thread pair and the results of the finite element model is 5.23%. The most uniform loading is achieved with a wedge angle of 30°. The axis load has little effect on the load carrying ratio. Load uniformity is inversely proportional to the pitch and directly proportional to the elastic modulus ratio of the bolt to the nut.

Offshore wind turbine suction pile foundation has been widely used. As known as suction bucket foundations, they are large concrete or steel barrel structures, closed at the top, open at the bottom, and equipped with a pumping/drainage port at the top. Firstly, the validity of the model is verified by the finite element software ABAQUS based on the data of actual test piles; then the finite element model of suction pile barrel foundation with pile diameter of 4-9 m in sandy soil is established to analyze the bending moments and displacement responses of the pile body under different horizontal loads; finally, on the basis of the hyperbolic p-y curve model, the initial foundation reaction modulus and ultimate soil reaction force calculation method are corrected by considering the effects of soil depth and pile diameter size, and a suitable p-y curve model is proposed. The results show that the suction pile barrel foundation in sandy soil mainly shows rotation mode under horizontal load, and accompanied by a little bit of translation, the value of the pile bending moment increases and then decreases along the soil depth direction, and the change of the horizontal load size basically has no effect on the location of the maximum value of the pile bending moment, the API method p-y curve overestimates the initial foundation reaction modulus and underestimates the ultimate soil reaction force, which is not suitable for its calculation and analysis, while the agreement between the modified p-y curve model and the results of the numerical modeling is better.

In order to solve the problems of performance degradation and poor stability of the ultrasonic wind measurement system in the presence of strong impulse noise, a three-element ultrasonic wind vector measurement method using the quadratic fractional low-order covariance algorithm is proposed. The three-element ultrasonic array structure consists of three transceiver-integrated ultrasonic transducers. Based on the system structure and combined with the quadratic fractional low-order covariance algorithm, the propagation time of the ultrasonic wave is obtained, then the wind vector estimation value is obtained. The practicability of the proposed method is verified by the actual wind measurement system and comparative simulation. In the actual measurement, the maximum error of wind speed and wind direction angle is 2.2% and 2.4° respectively, and the measurement accuracy basically meets the requirements of ultrasonic wind direction measurement.

Under the action of horizontal loads, a rigid rotational deformation mode occurs in the monopile foundation of offshore wind turbines. The vertical frictional resistance around the pile forms a resisting moment on the pile body, while the soil at the pile tip exerts a resisting force on the pile tip section. This paper first derives the formula for calculating the resisting moment on the pile body based on the radial distribution function of soil pressure, establishes a model of vertical stress variation during the rotation process of the pile tip section, and deduces the discrete numerical solution and simplified formula solution of the resisting moment at the pile tip. Secondly, based on the linear elastic-plastic frictional resistance-displacement model, a formula for calculating the horizontal shear force at the pile tip is established. Then, a simplified analysis model is established according to the deformation mode of rigid piles under horizontal loads. The reliability of the proposed analysis model is verified through comparative studies with experimental results. Finally, the variation pattern of the proportion of three additional resistances in horizontal bearing capacity with pile diameter, aspect ratio, and soil friction angle is analyzed. The results reveal that the proportion of additional resistance to horizontal bearing capacity decreases gradually with the increase of slenderness ratio, but shows no significant changes with the increase of diameter. As the internal friction angle increases, the proportion of pile shaft resisting moment in horizontal bearing capacity gradually increases.

The composite bucket foundation is a commonly used type of foundation in the development of offshore wind power. Accurately evaluating its natural vibration period is crucial for the successful implementation of construction techniques such as air-floating towing, sinking, and floating. This paper derives a formula for calculating the natural vibration period of the composite cylinder foundation, taking into account the mechanism of air-floating, structural dynamics theory, and the coupling effect of the air cushion and water plug inside the bucket. The validity and accuracy of this calculation method are confirmed by comparing it to experimental and calculated values from existing literature. The results demonstrate that by considering the coupled mass characteristics of the air cushion and water plug, along with the air buoyancy decreasing coefficient and a reasonable range for the additional mass coefficient, it is possible to accurately predict the natural vibration period of the composite bucket foundation. The added mass coefficient decreases as the draft increases, and the charge and exhaust modes are more effective than the top ballast mode for adjusting the natural period of the structure by changing its draft.

This study investigates the impact of the L/D ratio on the VIM characteristics of floating wind turbine foundations. Numerical simulations were conducted using an improved delayed detached eddy simulation （IDDES） model, integrated with dynamic mesh technology. The effect is studied through the vortex-induced motion of floating cylindrical structures. The analysis includes motion amplitude, frequency characteristics, motion trajectories, and wake structures. The findings reveal that the H/D ratio significantly influences sway motion, followed by heave motion, with negligible impact on yaw motion. An increase in the H/D ratio expands the lock-in region, intensifying vortex-induced resonance responses. No VIM is observed at an H/D ratio of 0.5; at this ratio, the natural frequency increases linearly with reduced velocity, and the motion trajectory is irregular. VIM occurs when the H/D ratio is greater than or equal to 1.0, and the motion trajectory evolves into a regular figure-eight shape as the H/D ratio increases. These results underscore the importance of the H/D ratio in influencing vortex-induced resonance in floating cylindrical structures.

This study proposes an interpretable model, PEARNN, which integrates a self-attention mechanism and positional encoding for predicting the remaining useful life of bearings. The model combines the advantages of self-attention mechanism and positional encoding to enhance its understanding of sequential data through patch masking and periodic positional encoding. Experiments conducted on the XJTU-SY and IEEE PHM 2012 datasets validate the effectiveness of the model and visualize the attention regions of the signals. The results demonstrate that the proposed PEARNN model performs excellently in bearing fault prediction tasks, exhibiting good generalization and robustness. Additionally, the paper illustrates how the model identifies critical parts of the input signal through visualized attention mechanisms to improve prediction credibility and interpretability. PEARNN not only enhances the accuracy of fault prediction but also strengthens the interpretability of the model.

Double-layer blade structure is one of the main methods to improve the starting performance of straight blade vertical axis wind turbines （H-VAWTs）. In this paper, the starting performance of H-VAWTs with double-layer blades under the coupled influence of solidity and moment of inertia is investigated using the dynamic mesh method, and the improvement effect of double-layer blade on the self-starting performance of H-VAWTs under different wind turbine structures is analyzed. The results show that the double-layer blade can effectively improve the startup performance of H-VAWTs, and the startup duration can be shortened by more than 30%; the peak torque of the outer-layer blade in the double-layer blade is not necessarily higher than that of the single-layer blade, but under the interaction of the inner and outer blades, the torque performance of the double-layer blade is significantly better than that of the single-layer blade. Under the conditions of different blade numbers, chord lengths and wind turbine diameters, the double-layer blade H-VAWTs has higher torque peaks at the start-up stage, and the torque peaks appear earlier than the single-layer blade, which results in higher start-up acceleration of the double-layer blade wind turbine, and the wind turbine rotational speed reaches the steady state earlier.

A 5 MW composite wind turbine blade was taken as the research subject. Typical load conditions such as gravity, centrifugal force, and aerodynamic force were fully taken into account. Based on the grid search method and combined with the classical laminated plate theory, we aimed to optimize the layering ratio and sequence of the composite laminated layer of the upper and lower beam caps of the main beam. By focusing on maximizing stress reduction and increasing bending stiffness, we were able to determine the optimal layering scheme for the beam cap. The results demonstrated a significant decrease in maximum stress and an increase in bending stiffness for the optimized blade main beam under the same load condition.

An anomaly detection method based on multi-feature fusion and t-distributed stochastic neighbor embedding （t-SNE） is proposed to identify the abnormal state of wind turbine towers based on monitored structural response signals. The method involves evaluating the time domain, frequency domain, and time-frequency domain statistical indicators of the monitored structure response signals to extract high-dimensional multi-feature vectors. The t-SNE algorithm is used for dimensionality reduction and fusion to obtain a visual representation of the data in a low-dimensional space. The K-means clustering algorithm is employed to analyze the data state, and a quantitative analysis of the anomaly index is then constructed to realize structural anomaly detection. The proposed method has been successfully applied to engineering applications of wind turbine towers during typhoon and earthquake periods, demonstrating its effectiveness in detecting abnormal structural responses caused by environmental changes.

This study examines a wind power fluctuation smoothing strategy for a hybrid energy storage system comprising supercapacitors and lithium batteries to achieve stable grid connection for wind power. An adaptive sliding average algorithm, which accounts for the upper limit of window length, is introduced to smooth real-time wind power. Subsequently, the computed hybrid power undergoes variational mode decomposition （VMD） to extract modal components, and composite entropy serves as the objective for the improved hunter-prey optimizer （IHPO） to optimize the predetermined VMD parameters. Secondly, the high and low-frequency cutoff points are established through the Hilbert transform, withhigh-frequency signals allocated to the supercapacitor and the low-frequency signal designated for the lithium batteries. A multilayer energy management strategy is proposed to achieve dynamic optimization and redistribution of hybrid energy storage power, taking into account the state of charge （SOC） during the charging and discharging processes of the energy storage devices. Finaly simulation resaalty confirm that the proposed strategy can effectively mitigate wind power fluctuations while ensuring that the state of charge （SOC） of both supercapacito, and lithium-ion batteries operates within acceptable ranges, resulting in a cumulative deviation reduction of 13.8% and 14.9%, respectively, compared to before pre-improvement.

A study on modal parameter identification of large jacket offshore wind turbine structures under operating conditions was carried out, and the robustness of the method under non-ideal white noise excitation conditions was investigated. Combined with hierarchical clustering algorithm, the covariance-driven stochastic subspace identification（COV-SSI） method was established to realize the automatic identification of modal parameters of wind turbine structures. The effectiveness and accuracy of the proposed identification method were firstly verified based on a numerical example of a three-story frame under stationary excitation. Then, based on scaled experiments of offshore wind turbine structure with a large jacket foundation, the accuracy and robustness of the modal identification method for offshore wind turbine under different wind and wave excitations were investigated. The results show that under rated wind speed, the method can effectively identify the first two natural frequencies and modal shapes of the structure with high accuracy.

The fatigue calculation and analysis method is studied for the mooring system of large floating wind turbine. The dynamic tension of the floating wind turbine mooring system is time-varyingand asymmetric about the time axis. The effect of the average cyclic stress is ignored in the current fatigue analysis of floating wind turbine mooring lines. Only the effect of the asymmetric peak of the mooring tension is considered to calculate the fatigue damage. The Goodman equation is introduced to correct the asymmetric peak value of mooring line stress by considering the average cyclic stress value of the dynamic tension. The critical fatigue cycle number is determined based on the corrected peak value of different stresses to calculate the fatigue damage. Based on the Goodman equation correction method, the fatigue damage and fatigue life of 10 MW wind turbine were calculated. The results show that the cycle-averaged stress value of the mooring line has an important influence on the fatigue damage,and the Goodman equation should be introduced to correct the asymmetric stress peaks in the fatigue analysis,the fatigue performance of the 10 MW wind turbine mooring system meets the safety requirements.

Grid-forming wind turbine generator exhibits excellent performance in weak grids, providing frequency and voltage support to the grid. However, the integration of a high proportion of power electronic devices may lead to grid harmonic pollution, while failures of renewable energy clusters to ride through, faults can cause transient impacts on the grid, worsening power quality. To address these issues, this paper proposes the utilization of a Unified Power Quality Conditioner （UPQC） based on nine-switch converter to achieve harmonic mitigation and source-grid fault isolation, significantly reducing the risk of converter current exceeding limits. To suppress circulating currents generated by parallel operation of multiple turbines and enhance active support during grid-forming wind turbine cluster fault ride-through, the mechanism of virtual synchronous machine power coupling and transient power angle stability are analyzed. Traditional strategies are improved based on reactive power loop supplementary voltage compensation and adaptive active power control. Simulink simulation results under various operating conditions demonstrate that the proposed topology and control strategies exhibit superior dynamic performance, achieving the desired control effects.

The fluctuation and intermittency of wind power influenced by meteorological environmental conditions are important reasons leading to wind power ramp. A wind farm in Inner Mongolia is taken as the study area. The data of wind speeds on the height of wind turbine hub and actual power data are used to identify the ramp events in the study area. The data time interval is 15 minutes and period is from 19:00 on September 11, 2019 to 23:45 on December 31, 2020. The characteristics of the ramp events are analyzed from the aspects of ramp heights, ramp speeds, times of duration, seasonal and daily variations, and so on. Further more, five risk levels of the ramp events including extremely low, low, medium, high and extremely high are classified, combining with the risk assessment of power system operation. The maximum ramp heights and wind speed changes at different risk levels are determined in order to provide effective technical support for predicting ramp events. The results show that： 1）The average ramp speed of the upward ramp events is significantly higher than that of the downward ramp events, the ramp speeds are inversely proportional to duration times; 2） the ramp heights and ramp speeds have significant seasonal variations, the average of those in spring and summer are higher than in autumn and winter; 3） the occurrence possibility of high risk upward ramp events is significantly higher than that of high risk downward ramp events, there is a possibility of extremely high risk downward ramp events, the maximum ramp height of upward ramp events is close to that of downward ramp events under different risk levelss, but the maximum wind speed variation of downward events is greater.

Aiming at the issues of nonlinearity, aliasing of characteristic information, and low diagnostic accuracy of vibration signals in the wind turbine gearbox, by integrating time-domain characteristic analysis and multi-sensor information fusion technology, an early fault identification approach based on non-threshold recurrence plot （NRP） and deep learning was proposed. First, the time-domain indices is used as the feature parameter, and the feature-level and data-level fusion technology is used to construct the dataset. Secondly, NRP converts one-dimensional data into two-dimensional colored visual feature maps. Then, an AlexNet-SENet network structure is built, and the improved AlexNet embedded with SENet's attention mechanism makes it adaptive to focus on the key structural and nodal feature. Finally, taking the vibration data set collected by the planetary gearbox dynamics test platform of Huazhong University of Science and Technology as an example, the results show that the diagnosis accuracy of this method is 99%, which can extract fault characteristics more effectively and has higher classification and diagnosis accuracy.

To obtain the failure mechanism of the segmented blade bolt connection, failure models of various composite materials are first established and compared through experiments and simulations to obtain the most suitable failure theory. Then, on this basis, the bolt connection structure model is established and numerical simulation is carried out. The results show that： Compared with the Hashin and Tsai-Wu failure criteria, the LaRC failure criterion is more applicable to characterizing composite materials failure in bolt connections of segmented blade bolt connections; During the process of bolt loading, the composite material is more prone to failure, but it does not lead to the failure of the overall structure. The bolt failure that occurs in the subsequent process is the main cause of the failure of the connection structure.

For identificating rotor imbalance in wind turbine under varying wind directions, a method based on expert system and convolutional neural networks is proposed. Firstly, based on the response of rotor imbalances, an expert system for detection of rotor imbalance is proposed. It includes the identification, isolation and location of the rotor imbalance. Secondly, to address the impact of varying wind conditions on identification accuracy, a identification method based on convolutional neural network with training interference is proposed. The wind condition interference problem is reduced to a domain adaptive problem in the neural network for the identification of rotor imbalance. A variable probability Dropout method and a small Mini-batch are combined for training to simulate the uncertainty of wind. Finally, the cross-validation data set is established to verify and test the proposed method. The average accuracy is more than 98%, which proves the effectiveness of the proposed method.

Accurate wind power forecasting is crucial for the reliability of grid scheduling decisions. To address the variability in wind power output, a wind power online forecasting method based on dynamic deep learning is proposed. Initially, a benchmark model utilizing bidirectional Long Short-Term Memory（LSTM） networks and bidirectional Gated Recurrent Units（GRU） is developed, with parameters and weights tailored to the training dataset. Subsequently, a fast Hoeffding drift detection method is used for wind power state monitoring, and the deep learning model is dynamically updated based on the detection results. Finally, a Random Forest regression model is integrated to correct the predicted power errors, enabling rolling online forecasting through sequential time windows. Validation results indicate that the proposed algorithm improves the root mean square error（RMSE） by 5.68%, the mean absolute error（MAE） by 18.56%, and the coefficient of determination（R²） by 2.06% compared to the Transformer, demonstrating good predictive performance and further enhancing the accuracy of wind power forecasting.

Numerical analysis has been conducted to examine the bearing characteristics of bucket foundations with various embedment ratios under vertical, sliding, and overturning loads. Comparative analyses between the results obtained from the Hansen formula, the caisson deep foundation formula, earth pressure-based calculation methods, and numerical simulations have elucidated the applicability ranges of these methodologies. The findings demonstrate that the vertical bearing capacity calculations using the Hansen formula align closely with the numerical analysis results, surpassing those obtained through the caisson deep foundation formula. Additionally, it was determined that the anti-slide and anti-overturning bearing capacity calculations based on earth pressure theory and the caisson deep foundation formula respectively are appropriate only for bucket foundations with embedment ratios ranging from 0.25 to 1.

This paper takes the NREL 5MW semi-submersible floating wind turbine as the research object. The nonlinear model was built through mechanisms analysiss and dynamic analysis. The linear parameter-varying （LPV） model under different working conditions was established by the online parameter identification method. The gap metric theory was used to simplify the number of linear models, and the LPV model was compared with the FAST model to verify its accuracy. A multi-model predictive control pitch controller based on fuzzy variable weighting （Fuzzy-MPC） was proposed. The Kalman filter state observer was introduced for state estimation, with to reducing power fluctuations and tower displacement as the objective function. To enhance the controller's tracking ability under different working conditions, the fuzzy control was employed to optimize the weight coefficients of the MPC objective function online, achieving adaptive control. The effectiveness of the designed controller in smoothing power, reducing tower displacement and stabilizing unit attitude is verified by comparison with FAST and fixed weight MPC controllers.

To tackle the limitations of conventional wind power prediction methods in capturing non-stationary and nonlinear features of wind power sequences in the frequency domain and underutilization of spatial interrelations among wind turbines, we propose a novel network named DSTNet. This network integrates advanced signal decomposition technology for frequency enhancement while simultaneously incorporating both temporal and spatial information, enabling highly accurate ultra-short-term wind power forecasting. In terms of temporal information processing, discrete cosine transform is used to convert the wind power sequence from the time domain to the frequency domain, followed by frequency enhancement through a channel attention mechanism. To fully capture temporal dependencies, a decoder is employed to extract the intricate temporal features from the enhanced frequency signals. On the spatial front, a graph neural network model is employed, constructed based on the geographical layout of wind turbines within the farm. This GNN captures spatial features by modeling the relationships between each turbine node and its neighbors. Finally, the temporal and spatial features are fused to generate ultra-short-term wind power predictions. By seamlessly combining these temporal and spatial features, DSTNet enables superior ultra-short-term wind power forecasting, spanning prediction horizons of 10 minutes, 1 hour, and 4 hours. Our method is evaluated on the spatial dynamic wind power prediction dataset released by Baidu KDD CUP, and the results show that DSTNet outperforms all other methods in terms of both prediction accuracy and stability. Specifically, compared to the second-best method, the mean absolute error （MAE） is reduced by 29.75%, 19.11%, and 8.09%, respectively, and the mean square error （MSE） is reduced by 28.22%, 13.44%, and 6.96%, respectively. Furthermore, in terms of prediction stability, the coefficient of determination （R²） is increased by 1.78%, 1.68%, and 2.41%, respectively.

Reed straw was subjected to pretreatment using alkaline hydrogen peroxide under atmospheric pressure and low-temperature conditions. Single-factor experiments were conducted to investigate the effects of varying NaOH concentration, H₂O₂ addition amount, time and temperature. Response surface optimization experiments were subsequently performed, yilding the following optimal parameters： 1.38% NaOH, 1.67% H₂O₂, 2.17 hours, and 69 °C. Under these conditions, the lignin removal rate reached 83.83%, with cellulose and hemicellulose retention rates of 91.69% and 61.94%, respectively. The enzymatic hydrolysis of the pretreated residue achieved a 24-hour hydrolysis rate of 71.08%, significantly surpassing that of the untreated raw material under equivalent conditions, reaching enzymatic equilibrium. Analytical techniques, including Fourier-transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy, were employed to analyze both raw materials and pretreatment residues. The results demonstrated that the alkaline hydrogen peroxide pretreatment effectively removed lignin and hemicellulose, disrupted the structural integrity of the raw material, and enhanced the efficiency of enzymatic hydrolysis for sugar production. These findings offer valuable technical insights into the high value-added utilization of reed straw.

The principles and application methods of density functional theory （DFT） simulation are reviewed, the application process and analytical methods of DFT simulation are summarized in terms of pyrolysis gasification mechanism and tar removal of typical biomass components, and an outlook on the future development of DFT simulation in the field of biomass gasification is presented.

This paper takes Guangdong Sea Area as the study area, uses ERA5 reanalysis dataset to analyze the spatial and temporal distribution characteristics of wind and wave resources, evaluates the availability, variability and correlation as well as complementary synergies of the two, and assesses and analyzes the potential for joint development of wind and wave resources. The results show that wind and wave resources in Guangdong sea area are relatively abundant, with the maximum annual average power density of wind energy exceeding 800 W/m² and the annual average power density of waves mainly ranging from 3 to 12 kW/m; the correlation of wind and wave resources is overall high, and the complementarity of wind and wave resources in near-shore is low, with the complementarity index W_iWaS not exceeding 0.3, and the wind and wave complementarity is enhanced by the increase of the distance from the shore, and the complementary synergistic properties of wind and wave resources in the offshore region W_iWaS can be more than 0.6. Compared with the western part of Guangdong, the wind and wave resources in the eastern part of Guangdong and the southwestern part of Taiwan Strait are more complementary, and the synergistic effect of the two is more obvious, which makes them more suitable for the joint development of wind and wave energy.

In response to the increasing energy consumption demands of offshore buoy navigation lights, this paper proposes a submerged chain generator capable of simultaneously harnessing wave and tidal energy in the marine environment. The motion and output voltage models of the generator are established, and numerical simulations are conducted to study the effects of component structure and motion parameters on its power generation performance. The stiffness of the springs and the planar excitation array structure in the generator are reasonably determined and experimentally verified. The results show that, under the same wave-current coupled excitation, the motion magnitude of the array decreases with the increase in the stiffness of the springs. The output voltage also increases with the increase in the motion frequency and magnitude of the array. When the swing magnitude and frequency of the anchor chain are 142.70 mm and 0.85 Hz, respectively, the power generation device （array dimensions： length × width × height=50 mm×20 mm × 50 mm） achieves an average voltage magnitude of 5.02 V and a maximum no-load power density of 0.024 mW/cm³, enabling the collection and conversion of low-frequency wave and current energy and providing power for offshore buoy navigation lights.

The design and working principle of a new megawatt-class wave energy converter （WEC） is introduced briefly, which consists of a semi-submersible floating body, a wave energy capture system, an energy conversion system, and a mooring system. The unstable wave energy is converted into stable electric energy by using a sharp eagle wave-absorbing floater and a hydraulic energy conversion system. Experiments on the energy capture efficiency of the megawatt-class WEC were carried out. Results show that the megawatt-class WEC can achieve highly efficient capture of wave energy. The energy capture efficiency of the megawatt-class WEC is mostly between 30% and 50%, and the maximum reaches 56.17%. The sharp eagle wave absorber has better absorbing properties in heading waves than in oblique waves. The energy capture efficiency in heading waves is 1.6 times that in oblique waves. The five wave absorbers on the same side of the megawatt-class WEC should be arranged to directly facing the predominant wave direction.

To effectively address the issues of soil temperature decreasing annually in ground source heat pump （GSHP） systems and excessive soil temperature in summer caused by solar-assisted ground source heat pump （SAGSHP） systems in solar greenhouses, this paper proposes a cross-seasonal partitioned thermal storage mode for SAGSHP systems. The buried pipes are divided into four zones, and TRNSYS-18 simulation is used to model different seasonal operation modes, leading to the optimal partitioned thermal storage strategy. Results show that the optimal mode involves connecting half of the solar thermal storage during the spring transition and cooling seasons, and the entire area during the autumn transition and heating seasons. Additionally, during the autumn transition season, half of the zone provide heating or cooling, while the entire area is used for heating or cooling during other periods. In this optimal mode, soil temperature remains stable over ten years, the Coefficient of Performance（C_OP） decrease in summer is minimized, and the system's operational stability is higher than traditional SAGSHP systems. Over ten years, the System Coefficient of Performance （S_COP） is on average 57% higher than GSHP systems and 5.4% higher than that of traditional SAGSHP systems. In the tenth year, the S_COP is 70.6% higher than GSHP systems and 10.4% higher than that of traditional SAGSHP systems. This study provides a reliable strategy for the efficient and stable operation of greenhouse GSHP systems.

The traditional parameter identification methods for lithium-ion batteries will lead to problems such as low accuracy of identification results and poor adaptability to working conditions, and sudden error and even distortion of output voltage curve due to sudden parameter changes in low SOC region. In this paper, the second-order RC equivalent circuit model is reconstructed according to the different time-scale characteristics of lithium-ion battery dynamics, and it is decomposed into fast dynamic （FD） part and slow dynamic （SD） part. The FD part and SD part of the model are distinguished by the improved IFRLS and the adaptive Kalman filter algorithm （AKF） to avoid the mutual interference between the model parameters. Finally, the proposed method is compared with the traditional parameter identification methods under various working conditions, and the results prove the effectiveness and accuracy of the proposed parameter identification method, which has certain engineering reference value for the practical application of energy storage system.

Aiming at the problem of poor consistency between individual cells, low sorting efficiency and failure to meet the use requirements during the recycling of retired lithium batteries, the study first tested the capacity, internal resistance, self-discharge characteristics, cycle life characteristics and charge and discharge thermal behavior of waste lithium batteries to analyze the current status of the battery performance; then the principal component analysis （PCA） method was used to screen the sorting factors of seven performance parameters, including charging capacity, discharge capacity, coulomb efficiency, charging energy, discharge energy, open circuit voltage and internal resistance; finally, the K-means algorithm was optimized by preprocessing the data, optimizing the initial point selection method and improving the distance formula to achieve clustering of the battery. The results show that high-rate discharge will accelerate battery aging, and the battery discharge capacity will decrease significantly under lower temperature conditions. Through PCA algorithm analysis, the battery discharge capacity, open circuit voltage and internal resistance can be selected as sorting factors, fully reflecting battery performance. The improved K-means algorithm was used to cluster and sort 300 groups of data, and it was found that when K=4, the clustering effect was optimal.

Extreme scenarios are important factors that threaten the economic and stable operation of isolated island microgrids. Taking into account the economic cost of system operation and the constraints on stable operation under extreme scenarios, a method for optimizing the capacity allocation of isolated island microgrids is proposed. First, a two-stage isolated island microgrid energy storage capacity optimization and allocation mathematical model is proposed, with the upper and lower layers constructing the system capacity optimization and allocation model and the operation benefit optimization model, respectively, and the stable operation constraints under extreme scenarios are introduced into the lower operation benefit optimization model in the form of a penalty function. Secondly, in order to ensure the stable operation of the system under the condition of insufficient number of extreme scenario samples, CWGAN-GP is used to construct the extreme scenario generation method, and the extreme scenarios are generated in large quantities to ensure the economy and stability of the results of the optimal allocation of the capacity as much as possible. The proposed method is verified by using the scenario data of isolated island grid collected from a province of Southern Power Grid, and the results show that the optimized energy storage capacity can effectively ensure stable system operation. The power balance violation rate in various operation scenarios is 0, and the investment return ratio can reach 5.53.

In this paper, a multi-objective model is established to distribute active and reactive power among subsystems within an energy storage power station. The combination of energy storage subsystems participating in active power distribution is obtained by taking SOC as the priority criterion. Taking energy consumption cost, life loss cost, SOC balance and reactive power regulation cost as targets, IHAOAVOA algorithm is adopted to solve the active and reactive power output of each subsystem. The simulation is verified on Matlab platform and compared with other strategies. The results show that the proposed optimization strategy can reduce the number of subsystems during charging and discharging of energy storaging, reduce the operating cost of energy storage, and improve the state balance of state of charge in energy storage.

Aiming at the problems of wind and solar curtailment, reverse transmission and over-limit of feeder power caused by the access of distributed generation in high-penetration active distribution network, this paper proposes a bi-level optimal configuration method of energy storage system based on flexible interconnection of rotating power flow controller. Firstly, a bi-level framework is presented, where the objective is to minimize the payback period of RPFC and energy storage combined system in the outer planning layer to realize the optimal configuration of RPFC and energy storage location and capacity, and the objective is to minimize the annual operation cost in the inner operation layer with RPFC participating in the operation optimization as a flexible adjustment resource. Secondly, the hybrid optimization of genetic algorithm and Pulp solver is used to solve the optimal configuration model. Finally, the optimal configuration model is verified on the example of IEEE 33-node. The results verify the applicability of the proposed optimal configuration scheme of RPFC and energy storage combined system, which can effectively promote the renewable energy consumption, and improve flexibility and economy of system.

The effects of 10 different operational strategies on the thermal performance of the wall are explored by combining CFD simulation and experiment, and a long-term operation study is carried out based on the typical meteorological annual parameters in hot summer and cold winter regions. The results show that the optimal control scheme derived in this paper is when the upper and lower control temperature thresholds are 27 ℃ and 25 ℃, respectively; under this control scheme, the cumulative indoor heat gain of the embedded pipe-type movable phase change wall in summer is reduced by 10.6% and 10.8%, respectively, compared with the two types of static phase change walls, which provides good energy-saving benefits compared with the static phase change wall and the ordinary wall.

To address the difficulty of analytical modeling of the internal resistance and incremental capacity of lithium-ion batteries, this paper proposes a mechanism-based modeling method with battery current and temperature as inputs variables and the internal resistance and incremental capacity as outputs. Firstly, the method constructs a voltage mechanism model based on the Arrhenius equation, which can quantitatively describe the voltage under the influence of current, temperature and other variables, as well as a Bernardi thermal mechanism model under the influence of current, voltage and other variables. Secondly, the coupling effects between the above two models and between the variables are decoupled through well-designed experiments. Then, the analytical model for internal resistance and incremental capacity analytical model are further derived. The experimental results show that the proposed method requires minimal experimentalal effect and can accurately estimate the battery voltage, internal resistance and incremental capacity. The voltage error is lower than 0.3‰ and the internal resistance error is lower than 1.2%.

In order to improve the output stability of photovoltaic power plants and ensure power quality, it is necessary to optimise the allocation of energy storage capacity in photovoltaic power plants. This paper proposes an optimal allocation method of energy storage capacity based on Teaching-Learning-Based Optimization （TLBO） algorithm. Considering the impact of multiple factors on PV output, a two-layer energy storage capacity optimisation model is constructed. The upper layer is solved by the TLBO algorithm with the objective function of minimising the whole life cycle cost of energy storage; the lower layer is solved by the Gurobi solver with the objective function of maximising the operating revenue, and the optimal daily operation strategy is solved by the Gurobi solver. Finally, a real PV power plant in Daqing is taken as an example for simulation, and the simulation results show the effectiveness of the method.

The high-temperature solid heat storage device is made up of many heat storage units. Before investigating the whole heat storage device, the thermal characteristics of the unit should be revealed. In this paper, the heat transfer defect inside the heat storage device is revealed after analyzing its thermal characteristics. After 8 h of charging, the maximum temperature difference is about 60 K. The charging characteristic is influenced by the position of the heating element significantly. In 3 cases, the temperature distribution becomes worse as the heating element moves down. The heat storage unit has a minimum temperature difference of 20 K when the heating element is placed at the center of the unit. During charging, the radiation is the main heat transfer method, about 95% of heat is transferred by radiation. The air path volume proportion of the unit also influences the thermal characteristics. To get better temperature distribution, the proportion should be larger. But to get higher energy storage density, the proportion should be lower.

In order to solve the problems of system chattering, low estimation accuracy and poor anti-disturbance ability caused by the discontinuity of the symbol function of the traditional sliding mode observer, a new type of switching function is constructed, and then an improved sliding mode observer is designed to estimate the rotor position and improve the estimation accuracy, which is verified by the Lyapunov stability theory. It is equipped with a load disturbance observer to estimate the load torque in real time and compensate for the speed drop caused by external disturbance. Finally, the improved second-order generalized integrator notch phase-locked loop is used to calculate the rotor position. Through the comparison between simulation and experiment, the improved control strategy proposed in this paper can effectively improve the extended back EMF waveform, improve the accuracy of rotor position estimation, the speed fluctuation of the synchronous motor-generator is less than 1%, which has the advantages of the small speed drop after sudden disturbance, the high robustness and the high operation efficiency of the gravity energy storage system.

At present, over 90% of the world's hydrogen production is derived from fossil fuels. The hydrogen energy industry holds significant potential for reducing emissions as part of the pursuit of the "dual carbon" goal. Among the various methods for hydrogen production and emission reduction, carbon capture technology stands out as a crucial option for achieving low-carbon hydrogen production. Therefore, this paper proposes an optimized scheduling approach for an integrated energy system coupled with coal-based hydrogen production and carbon capture, while considering information gap decision theory. First, a multi-resource utilization model is developed for a carbon capture power plant integrated with coal-to-hydrogen production. Next, taking into account the operational constraints of this coupled system, a deterministic scheduling model is formulated with the objective of minimizing both system operation costs and wind curtailment costs. Finally, to address the uncertainty of wind power, information gap decision theory is applied to quantitatively analyze and enhance the system's adaptability to such variability. The scheduling scheme is evaluated under different risk attitudes through a numerical case study. The results demonstrate that the proposed strategy effectively promotes wind power integration and enhances the systems's low-carbon economy.

The compression deformation of the GDL is caused by assembly force. To determine the optimal assembly force for improving fuel cell performance, the GDL deformation, current density distribution, and oxygen concentration distribution of the PEMFC under different assembly forces were analyzed based on experimental and numerical simulation methods. The PEMFC performance was comprehensively evaluated by considering multiple parameters such as pressure drop, power density, and current density distribution. The results show that the assembly force resulted in nonlinear stress-strain compression of the GDL under the rib, with compression reaching 61 % at the assembly force of 2.0 MPa. The power density of PEMFC at 1.0 MPa increased by 4.57 % compared to the results of 0.5 MPa under 0.95 A cm^-2. Moreover, the overall performance of PEMFC at 1.0 MPa was the best by using the entropy weighting method.

This paper proposs a new bionic flow field （BFF） that integrates a spiral structure and petal-like fractals based on biomimetic principle, and compares the proposed BFF with the traditional single serpentine flow field （1SFF）, five snake flow field （5SFF） and parallel flow The performance indicators of PEMFC such as peak power density, water vapor transmission characteristics, current density distribution and pressure drop are compared and analyzed. The research results show that： under the operating voltage of 0.6 V, the average oxygen mass fraction of BFF is 67.9% higher than that of PFF, the average water mass fraction is 23.9% lower than that of PFF, and the uniformity index is higher than that of the other three flow fields, reaching 0.9; The membrane water content of each interface layer of BFF is less than that of the other three flow fields, effectively removing the product water; BFF significantly reduces the pressure drop, and the overall pressure drop is only 16.7% of PFF. The peak power density of BFF is 3.7% higher than 1SFF, 5.5% higher than 5SFF, and 30.1% higher than PFF.

Based on the research background of the working principle, heat generation, heat dissipation, and water-thermal management issues of proton exchange membrane fuel cells for vehicles, this paper first introduces the cooling methods and key system components of automotive fuel cell stacks. Then the integrated thermal management technologies and control strategies of fuel cell vehicles are summarized based on the thermal management of multiple heat sources including the fuel cell stack, power battery, motor, cabin and other heat sources. Finally, the research methods of thermal management for fuel cells are introduced from the perspectives of modeling, simulation and experimental research. Both the current research and future development direction of automotive fuel cells are summarized, aiming to provide reference for the research and long-term development of efficient thermal management technologies for automotive fuel cells.

With the lowest total cost as the objective, this paper establishes a collaborative optimization model of technology selection and capacity allocation for the photovoltaic-fuel cell-electrolyzer-hydrogen storage-lithium battery coupling system, accounting for component cost uncertainty. Proton exchange membrane fuel cells, proton exchange membrane electrolyzers, and reversible solid oxide cells are optional hydrogen-electricity conversion technologies. Results revealed that the technical path selected and variations in component prices have a significant effect on the system configuration. Reversible solid oxide cells are able to operate in dual modes, resulting in high equipment utilization and economy, offering greater advantages over lithium battery storage technology, and proton exchange membrane cells. The levelized cost of energy ranges in [0.40, 1.15] RMB/kWh. However, the electricity-hydrogen coupling technology route based on proton exchange membrane cells has greater advantages when the cost of reversible solid oxide cells is more than 20000 RMB/kW and the cost of proton exchange membrane electrolyzers is less than 2500 RMB/kW. When the cost of reversible solid oxide cells and proton exchange membrane electrolyzers is higher than 16000 RMB/kW and 4000 RMB/kW, respectively, and the cost of lithium batteries is as low as 400 RMB/kWh, it is only suitable to use lithium batteries as energy storage components. Under those scenarios, the levelized cost of energy is less than 0.73 RMB/kWh.

For a solar thermal power tower coupled solid oxide electrolysis cell （SOEC） hydrogen production system with molten salt thermal storage, the thermodynamic model of each subsystem is established to analyze the effects of key operating parameters and operating modes on the performance of SOEC hydrogen production and its impact on the system electrical power and heat flow rate supply and demand, thermal storage capacity and efficiency. The results show that SOEC operating in exothermic mode enhances both electrolysis efficiency and system hydrogen production efficiency compared with thermo-neutral mode, and reduces solar radiant power and heat storage capacity required by the system. The lower the operating temperature of the electrolysis cell, the greater the enhancement. At the operating temperature of 600 ℃, the hydrogen production efficiency of the system can be enhanced by 5.14 % to 14.49 % when the SOEC is operated in the exothermic mode where the temperature difference between the inlet and outlet is from 100 ℃to 400 ℃. Therefore, for the SOEC hydrogen production system coupled with solar thermal, appropriately increasing the current density at low operating temperatures to make the electrolysis cell work in exothermic mode is of significance to improve both the electrolysis efficiency and the system hydrogen production efficiency.

The types, materials, and applications of supporting structures for liquid hydrogen storage tanks are systematically reviewed and analyzed. The advantages and disadvantages of various struts are comprehensively evaluated, followed by a scenario-based analysis of the supporting structures used in liquid hydrogen storage tanks during the transportation of tank trucks, ships, aircraft, and rockets. Based on the current research status, the usage types of struts exhibit distinct scenario-specific characteristics, with rod supports being the most frequently selected structure. In terms of materials, composite materials demonstrate significant potential due to their excellent thermal insulation, anti-corrosion, and mechanical properties, and are currently in a phase of rapid development. Furthermore, as the application scenarios of LH₂ storage tanks expand, thermal insulation is no longer the sole concern. Particularly in mobile storage tanks, the energy transfer resulting from vibration and shock, as well as its impact on structural strength, requires special attention.

In response to the challenges associated with complex dynamic responses and inaccurate description of nonlinear relationships in proton exchange membrane fuel cell （PEMFC） modeling, this paper proposes a novel method for PEMFC modeling and parameter identification based on a multi-input single-output Hammerstein structure. Initially, an extreme learning machine （ELM） network is employed to capture the input nonlinearity within the Hammerstein model, thereby constructing a framework that precisely reflects both the dynamic and static characteristics of the PEMFC system. Subsequently, the key term separation technique is utilized to construct the identification model, from which the auxiliary model recursive least squares （AM-RLS） algorithm and the auxiliary model forgetting gradient （AM-FG） algorithm are derived for parameter identification by integrating the auxiliary model idea. Finally, by combining ElasticNet with mutual information analysis （MIA）, controllable variables strongly correlated with the output power quality are screened, thereby reducing modeling complexity and improving computational efficiency. Through simulation verification using the measured data under dynamic and steady current conditions, the results indicate that the established model can accurately predict the variation trend of output voltage and exactly reflect the quality fluctuation of output power.

Aiming at the problems of declining power prediction performance and high appraisal cost of photovoltaic （PV） power plants year by year, this paper proposes an optimization method integrating bidirectional long short-term memory network （Bi-LSTM） and attention mechanism. First, the time dependence of power data is captured by applying Bi-LSTM to improve the understanding of time series changes; second, the combined attention mechanism enables the model to focus on the features that have the greatest impact on the prediction results to further improve the prediction accuracy; and finally, the accuracy is verified by using field historical data modeling. The field model deployment shows that the model can better capture the rapid power changes compared with the traditional single model and integrated learning model, and the ultra-short-term prediction accuracy of the field station can be improved by more than 10%.

A genetic algorithm-dung beetle optimization （GADBO） is proposed to address the problem of low convergence accuracy of the dung beetle optimization algorithm due to being trapped in local optima. This improved algorithm combines the strategies of initial population and mutation disturbance to escape local optima and enhance global search capabilities. Through the testing and comparison of 10 benchmark functions, the effectiveness of the GADBO algorithm improvement has been verified, and GADBO has higher optimization accuracy than other swarm intelligence optimization algorithms. The GADBO algorithm is applied to the hyperparameter optimization of long short-term memory （LSTM） neural networks, and a photovoltaic power generation prediction model is established. The simulation results show that the LSTM prediction model optimized by the GADBO algorithm has a fitting coefficient of 98.45%, and the average absolute error and average absolute percentage error are also minimized. The accuracy and precision of the model have been improved, verifying the applicability and effectiveness of GADBO in neural network optimization.

The flexible cable supported photovoltaic structure has light weight and small stiffness, and is prone to large vibration under the action of strong winds, resulting in structural damage. In this paper, the displacement response of flexible cable-supported photovoltaic arrays under different component inclination angles is analyzed by combining the wind tunnel test of rigid model pressure measurement and the finite element analysis method considering nonlinearity, and the influence of lateral connection on displacement response is explored. The results show that： there is an obvious dynamic interference effect between the flexible cable-supported photovoltaic array modules, the fluctuating displacement of the photovoltaic modules at the windward side is smaller than that on the leeward side under large tilt angles, and the interference effect is a dynamic amplification effect; Under the condition of large tilt angle, the photovoltaic modules at the windward side occurs vibration dominated by vertical bending, and vibration dominated by small torsion and small vertical bending occurs at the leeward side photovoltaic modules; The lateral connection effectively improves the torsional stiffness and further inhibits the torsional response of the structure. It is suggested that the lateral connection should be used to control the large wind-induced vibration of the flexible cable-supported photovoltaic supports in practical engineering applications.

Aiming at the problem of insufficient prediction accuracy of large-scale photovoltaic power generation system under non-ideal weather conditions, which in turn triggers the difficulties in the implementation of the power system dispatch plan, a prediction method of photovoltaic power generation is proposed based on the similar day theory and the improved pelican optimization algorithm （IPOA） extreme learning machine （ELM）. First, the Pearson correlation coefficient method filters out meteorological factors that exhibit a significant correlation with PV power generation; then the combined Euclidean distance and Mahalanobis distance evaluation indexes are used to calculate the combined distance between the historical days and the days to be predicted at each time point to determine the similar days. Then, the sample set of similar days is input into the constructed IPOA-ELM power prediction model for training, and based on the actual measurement data, the performance of the IPOA-ELM model is compared with that of the POA-ELM, SCSO-ELM, and GJO-ELM models in terms of prediction accuracy. After comparative analysis, it is concluded that the selection of similar days for the weighted composite index can more accurately reflect the distance and distribution characteristics between each moment point; the IPOA algorithm is optimal in terms of convergence speed and adaptability compared with POA, SCSO and GJO; and the root-mean-square errors of the IPOA-ELM model are lower than the other comparative models in the prediction of photovoltaic power generation under different weather conditions. It is worth noting that the model still shows good stability under rainy weather. It is fully proved that the applied IPOA-ELM prediction model has strong adaptive ability and prediction accuracy.

This paper carries out a study on the optimal configuration of the photovoltaic residual electricity thermal conversion and heat storage system based on the energy system optimisation theory, it establishes a capacity optimisation model of the photovoltaic residual electricity thermal conversion and heat storage system with the goal of minimising the levelized cost of heat（LCOH）, and then simulates the optimisation of the system using the TRNSYS analogue simulation platform. The results show that, compared with the system capacity before optimisation, the power of the electric heating boiler and the volume of the water body in the optimised system are both reduced, and although the system's capacity for residual electricity utilization is reduced, the overall C_LCOH of the system is reduced, and the economy of the photovoltaic residual electricity thermal conversion and heat storage system has been significantly improved.

This paper proposes an improved incremental conductance (INC) method to address the variability of solar irradiance in maximum power point tracking （MPPT）. Based on the MPPT algorithm, the photovoltaic power generation model is established in Matlab/Simulink, and its feasibility is verified. The six photovoltaic modules with the same temperature conditions are used to verify the performance of the algorithm. Simulation results in Matlab/Simulink show that the improved increment conductance method has better maximum power tracking performance than the traditional increment conductance method. Experimental results show that the improved increment conductance method achieves approximately 4.33% higher maximum power tracking efficiency than the traditional increment conductance method.

In order to improve the efficiency and stability of photovoltaic power generation systems, a maximum power point tracking （MPPT） control algorithm based on the P-V characteristic curve is proposed. This method can accurately control and locate the maximum power point of the system by collecting P-V characteristic curve data in real time, and avoids the system stability problem caused by frequently adjusting the duty cycle. The dual BOOST converter topology is adopted, where the main circuit and P-V characteristic curve acquisition circuit cooperate with each other to ensure that the system always operates at the maximum power point. Through theoretical derivation and in-depth analysis of the working principle, the key parameters of the dual BOOST circuit are designed. The circuit modeling, simulation, and analysis are performed using Matlab/Simulink, and the MPPT algorithm for a 16 kW photovoltaic power generation system is verified through hardware-in-the-loop （HIL） simulation. The experimental results show that this method can quickly and accurately locate the maximum power point, with minimal impact on the system and load, effectively improving system efficiency and stability.

As building integrated photovoltaic/thermal（BIPV/T）walls are structurally different from ordinary walls, which leads to changes in their thermal and moisture insulation performance, this study takes Shenyang, a cold region, as a case study. A two-dimensional numerical model of coupled heat and moisture transfer was established to study the internal transfer processes inside the BIPV/T walls under the winter conditions, and to experimentally validate the validity of the method. Comparing the BIPV/T wall with a conventional externally insulated wall, the results show that the time lag factor (τ=4-5 h) and attenuation factor (f=0.064) of the BIPV/T wall are superior to those of the ordinary wall (τ=2-4 h, f=0.151), indicating better thermal performance. It can significantly improve the thermal comfort of the indoor environment. The internal water content of BIPV/T wall is 6% lower than that of ordinary wall, and it can accelerate the decrease of water content in the wall, keeping the moisture content within BIPV/T walls low. The probability of producing freeze-thaw damage in BIPV/T walls is 61.56% lower than that of ordinary walls, which improves the durability of the building envelope. The air channels of the BIPV/T wall can play a role in cooling down the PV modules, whose photovoltaic conversion efficiency is consistently above 18%.

In order to improve the conversion efficiency of photovoltaic DC modules, a high-gain Boost converter with low voltage stress and zero-voltage-switching （ZVS） is proposed. Benefitting from the split-switched-capacitor and charge pump circuits, the voltage gain and power transistor voltage stress becomes twice and half times that of a conventional Boost converter. Furthermore, one diode in the split-switched capacitor network has been substituted with a synchronous rectifier switch. Meanwhile, the rear-end inductor current is allowed to flow in both directions and with a specific constrained peak-to-peak value, thus achieving ZVS turn-on of all switches and natural turn-off or near zero-current turn-off of the diodes throughout the entire operating condition range. Firstly, the working principle and steady-state characteristics of the converter were analyzed, followed by the implementation conditions of soft switching and the design method of inductance parameters. Finally, the theoretical analysis was validated through experiments on a 250 W prototype. The research results show that the maximum efficiency of the converter can reach 97.6%.

This study proposes the use of a wavy polydimethylsiloxane （PDMS） film to enhance the photovoltaic conversion efficiency of silicon solar cells through antireflection and radiative cooling. Through finite-difference time-domain （FDTD） simulation, the effect of PDMS thickness and waveform parameters on the optical performance is investigated. The results show that the wavy PDMS film with a thickness of 60 μm, a wave period of 2 μm, and a wave amplitude of 5 μm has the optimal performance. It achieves 94% emissivity in the atmospheric window band （8-13 μm） and 99.5% solar transmittance in the photovoltaic response band （0.3-1.1 μm）. In addition, this wavy PDMS film can reduce the temperature of bare silicon cells by 5.82 ℃ with the combined antireflection and radiative cooling, effectively avoiding 3.15% electrical losses.

To investigate the wind-induced vibration characteristics and stability of large-span flexible PV support structures under typhoons, a weather forecast model （WRF） based on the nonhydro-static equilibrium Euler equation model was used to simulate three-dimensional near-surface typhoon with the high spatiotemporal resolution, using a customized field function to incorporate a meso-scale typhoon as the inlet boundary condition for small-scale computational fluid dynamics（CFD） numerical simulations; Three structural optimization models were proposed based on a certain project of a 40 m large-span flexible PV support array. The wind pressure distribution characteristics and nonlinear wind vibration characteristics of large-span flexible PV support arrays under typhoons were investigated, and the entire process of structural instability of different optimization models was analyzed. Finally, a structural instability assessment model of large-span flexible PV support arrays was established. Research has shown that the maximum wind pressure of the large-span flexible PV support array occurs in the windward first-row area, with a maximum wind pressure coefficient of 0.85, and the minimum wind pressure occurs in the second row, with a minimum wind pressure coefficient of 0.15; The total displacement of different structural models is mainly in the Z-direction, and the structure is the first to fail in the local triangular braces of the first row of side spans on the windward side. The instability determination model shows that considering cross-diagonal braces and horizontal links is the optimal configuration for large-span flexible PV support arrays.

This paper proposes a method for constructing a knowledge graph for hot-spot failures. First, the hot-spot failure knowledge representation is standardized, and relevant text information is collected to create a corpus. Secondly, an improved RoBERTa-BiLSTM-MHA-CRF model is used to identify entities related to hot-spot failures. Based on this, the BERT model is used for relationship extraction. Finally, the results of these two parts are integrated to construct a complete hot-spot failure knowledge graph. Experimental results show that the proposed entity recognition model achieves an accuracy of 96.02% for hot-spot failure knowledge, a recall rate of 93.56%, and an F1 value of 94.77%. This method precisely constructs a knowledge graph for hot spot faults in photovoltaic modules,enabling intelligent fault diagnosis of PV modules and offering strong support for subsequent applications.

To improve the accuracy of photovoltaic （PV） power prediction,a hybrid short-term photovoltaic power prediction model based on the Pearson interpolation algorithm （PIA）,fuzzy C-means clustering algorithm （FCM）,multivariate variational mode decomposition （MVMD）,convolutional neural network （CNN）,bidirectional long short-term memory network （BiLSTM）,and attention mechanism （ATTENTION） is proposed.Firstly,the PIA is utilized to repair the missing data in the dataset. Secondly,the FCM algorithm clusters historical data into sunny,cloudy,and rainy days. Then,the MVMD is employed to decompose the PV power into several intrinsic mode functions （IMFs）.Subsequently,a combination of CNN and BiLSTM networks is used to fully extract the features of each IMF,with the Attention Mechanism introduced to emphasize and weigh important information. Finally, the forecasting of each IMF is conducted,and the predicted values are summed to obtain the final prediction result. Compared with other PV power forecasting models,the proposed hybrid model demonstrates superior forecasting accuracy.

This paper proposes a two-layer optimization scheduling strategy for distribution networks with a high proportion of distributed PV systems based on the C&CG algorithm. Firstly, a two-layer optimization scheduling model for distribution networks is established. The upper-layer model multi-dimensional voltage deviation levels and static voltage stability, with the objectives of minimizing distribution network losses and optimizing multi-dimensional power quality. The lower-layer model considers energy storage devices, strategies such as demand response participation in scheduling, with the objective of minimizing the overall operational expenses of the distribution network. The adaptive ε-constraint method combined with multi-objective particle swarm optimization algorithm is employed to obtain a uniformly distributed Pareto frontier, and the TOPSIS decision-making method is used to select the optimal solution. Finally, the C&CG algorithm is used to iteratively solve the two-layer model. The proposed strategy is validated through improved IEEE 33-node system example, demonstrating that it can reduce network losses, increase the consumption rate of renewable energy sources, and decrease the comprehensive operating costs of distribution networks while ensuring the optimal power quality of the distribution network.

This work focuses on the many problems existing in offshore photovoltaic projects, such as high salt spray, high load, high UV, high humidity, electrical insulation, and hot spots. By optimizing the selection of packaging materials, multiple types of high weather resistant packaging materials are used, combined with Bycium+ cell technology and high-density packaging technology, a high-power and efficient module suitable for offshore photovoltaics is designed and manufactured. The power degenerations of this module are less than 0.3% in various conventional and triple reliability tests, which is better than conventional PV modules and considerably less than the standard value of 5%. At the same time, it can pass the level 8 （acidic） salt mist tests ,wind tunnel tests with the wind speed of 60 m/s （equivalent to a level 17 hurricane） and UV sequence comprehensive testing ,providing a guarantee for its long-term stable operation in offshore photovoltaic application scenarios.

In light of the influence of photovoltaic penetration on the grid-integrated stability of PV systems, and bearing in mind the fluctuating characteristics of solar power generation as well as the grid's requirements for regulation, this paper proposes a stability analysis approach for a grid - connected model of PV systems that takes photovoltaic penetration into consideration. Firstly, considering the impact of photovoltaic penetration on grid-connected PV systems, a state-space mathematical model of PV systems is established based on the dynamic model of grid-connected PV systems. Then, the state-space mathematical model of PV systems is optimized using fractional-order differential equations, and the validity of the state-space model is verified by means of step response. Finally, PV grid connection models are constructed based on the IEEE 14-bus and IEEE 33-bus systems, and the PV penetration threshold is determined using the Lyapunov stability criterion and state-space model analysis.

In response to the differences in fault response capabilities among different topology structures of PV arrays, the output characteristics of 13 different topology structures are analyzed under four typical faults： partial shading, short-circuit fault, open-circuit fault, and component aging. Based on the characteristics of different scenarios such as ports, mountainous areas, and the Gobi and desert, optimization suggestions for PV array topology structures are proposed. The experimental results show that using the maximum power point output as the evaluation index, the optimal structures under local shading fault, short-circuit fault and open-circuit fault respectively are TCT structure, SP structure and TCT structure. At the same time, when the components are aging, the changes in the output characteristics of the topology under different faults show no significant differences. After further comprehensive analysis, it is believed that the most suitable topology structures in the port environment are SP structure and BL structure, the most suitable topology structure in mountainous environment is SP-TCT structure, and the most suitable topology structure in the Gobi and desert environments is BL-TCT structure.

The laws of dust accumulation on the PV modules on highway slopes and the influence of the dust accumulation on the operating efficiency of the PV modules are obtained by experiments and simulation, as well as the optimization model of the cleaning cycle of the PV on the highway slope is established. The results show that the dust accumulation density on the PV modules on highway slopes increases with the increase over time, but the dust accumulation rate decreases with the increase over time. When the dust accumulation density increase from 0 to 2, 4, 6, 8, 10 g/m², the operating efficiency of the PV modules decrease from 21.1% to 17.4%, 14.7%, 11.2%, 10.0%, 8.4%, respectively. During a period of 28 days without precipitation in winter in Jinxiang County, Shandong Province, the highest net benefit of the cleaning of the PV on the highway slope is 0.48 RMB/kW by carrying out 1 cleaning operation.

In order to solve the problem of power loss of photovoltaic modules caused by the“hot spot effect”, a multi-modal YOLOv8n-cls model prediction method based on Super Token Attention Mechanism （STA） was proposed. The STA attention mechanism was added to the image classification network YOLOv8n-cls to solve the problem that the neural network tend to capture the local hot spot features in the shallow layer with high redundancy. At the same time, a multi-modal model of infrared image and digital information fusion of photovoltaic modules was built based on the YOLOv8n-cls algorithm to explore the influence of irradiance parameters on power loss prediction. A photovoltaic multi-source dataset composed of photovoltaic infrared images, power loss and irradiance parameter labels was constructed, and power loss prediction experiments were carried out. Experiments show that the Top1 accuracy of the improved network model for the hot spot effect caused by leaves, dust and guano reaches 95.6%, 89.0% and 88.1% respectively, which is 3.3%, 10.0% and 7.5% higher than that of the original model, which proves the superiority of the proposed algorithm in predicting the power loss of photovoltaic modules with hot spot effect.

In this work, the optical simulation by Sunsolve and device simulation by Quokka3 are used to investigate the effects of cell structure, film material and film thickness, front contact resistance, wafer resistivity, and front finger pitch on the optical and electrical performance of HJT cells. Compared with the conventional HJT solar cell, the MgF_x/Ag back reflector and the thin ITO of 12 nm are used on the back side, improving reflection at long wavelengths and reducing parasitic absorption on the backside. On the front side, the nc-Si： H（n）/ITO contact regions with high parasitic absorption are limited below the fingers, while a highly transparent SiN_x/SiO_x stack is used in the passivated regions, which significantly reduces the parasitic absorption and external reflection on the front side, and increases the cell J_SC by 1.63 mA/cm², reaching 41.75 mA/cm². By using the optimized front finger pitch of 1.25 mm, resistivity of 1 Ω·cm, and front ρ_c of 0.2 mΩ·cm², localized passivated contact HJT solar cells with an efficiency of 27% are achieved. Compared with conventional HJT solar cells, the efficiency is improved by 0.97%, while the front and back thin ITO design reduces the fabrication cost of HJT cells.

Aiming at the problem that the forecast of a single model in forecasting the generation power of photovoltaic power plants is not ideal due to its inherent limitations, and that the weight of the traditional combination model is constant and not optimal, and its forecast potential is not fully exploited, a prediction method based on the Q-learning combination model is proposed. Firstly, meteorological features and time series with strong correlation with power generation are selected as input features through Pearson correlation coefficient; Secondly, two single models namely XGBoost（eXtreme Gradient Boosting） and LSTM（Long Short Term Memory） are used to forecast the generation power; Finally, the Q-learning algorithm is used to perform online rolling optimization on historical samples and update the combination weights of each preliminary forecast in real-time, thereby achieving the optimal time-varying weight combination. The experimental results show that compared to other models, the method can significantly improve forecast accuracy and effectiveness, verifying the effectiveness of this method.

In order to investigate the corrosivity of metalicl materials at high temperatures in two new wide temperature range mixed molten salts, the paper studied the corrosion behavior of 316L stainless steel in two new wide-temperature range mixed molten salts using the weight loss method. The results showed that, over a period of 1000 h, the corrosion rates of 316L stainless steel in solar salt, quaternary nitrate-nitrite mixed salt and ternary nitrate-carbonate mixed salt were 0.0121, 0.0290 and 0.0900 mm/a, respectively, and the corrosivity of ternary nitrate-carbonate mixed salt was greater than that of solar salt and quaternary nitrate-nitrite mixed salt. The results of microscopic characterization and product analysis on 316L stainless steel samples after corrosion testing showed that 316L stainless steel corrodes in ternary nitrate-carbonate mixed salt with the formation of loose and porous NaFeO₂, which leads to increased corrosion of stainless steel.

Aiming at the special structure of multi-inverter parallel connection in distributed power generation systems, this paper analyzes the mechanism of zero-sequence circulation, establishes the corresponding equivalent electrical model, and proposes a zero-sequence circulation suppression strategy which combines neural network PI control and quasi-proportional-resonant control. Firstly, the traditional PI controller parameter design method is improved, and the neural network is introduced to calculate the PI controller parameters, so as to solve the nonlinear problem of inverter output and complete the zero-sequence circulation suppression in the fundamental wave domain. Secondly, a quasi-proportional resonance controller is designed for the zero-sequence circulation in the third harmonic domain, and the zero-sequence circulation component is controlled by neural network PI control and quasi-PR control to obtain control instructions, and SVPWM zero-sequence duty ratio modulation is carried out, so as to accurately suppress the third-harmonic circulation; Finally, the large-scale simulation software Matlab/Simulink is used for modeling analysis and testing by experimental means. The results show that the proposed control strategy is reasonable and feasible, which can effectively suppress zero-sequence circulation, and has important guidance value for large-scale distributed power generation engineering and application.

To solve the problem of energy trading in the power market of multi-microgrid systems in the distribution network side, this paper proposes an optimization method for energy trading in multi-microgrid systems based on Stackelberg game. Firstly, the energy storage output and transaction status of the microgrid in the game stage are determined by single microgrid optimization strategy. Secondly, the optimization results of microgrid operation scheduling are interacted with the distribution system operator （DSO）, and a Stackelberg game model is established to engage in the game with internal electricity prices and dispatchable energy as the strategy. The optimization operation objective of the proposed method is to reduce the operation cost of microgrid, improve the operation efficiency of DSO, and reduce the impact of the multi-microgrid on regional power grid. The feasibility of the game strategy is verified by simulation cases.

This article first constructs the basic framework of mine integrated energy system（MIES）, which includes foundational models for utilizing mine-derived energy resources such as mine water, exhaust air, and methane. Subsequently, an exergy analysis coupling material flows with exergy flows is conducted for both the supply and demand sides of the system. Energy quality coefficients are utilized to quantify the quality differences among various types of energy, including mine-derived energies within MIES, achieving high-quality and cascading utilization of system energy. Then, a multi-objective optimization operation model is constructed with objectives of exergy efficiency and economic feasibility, considering constraints related to equipment characteristics and energy balance. The NSGA-Ⅱ algorithm is used to solve the model. Finally, a case study from a mining area in Taiyuan, Shanxi, is selected to validate the proposed method, proving the effectiveness of the optimization model.

In order to reduce the impact of wind power prediction errors on the economics and accuracy of dispatch of electricity and hydrogen integrated energy systems, an intraday two-tier rolling optimal dispatch method for integrated energy systems that takes into account the electric-hydrogen cogeneration system is proposed. First, considering the characteristics of electricity hydrogen production, the electric-hydrogen cogeneration system is applied in the integrated energy system. Secondly, a rolling optimization method is adopted in intraday dispatch. In the rolling cycle, the upper level model controls the electricity and hydrogen cogeneration system based on the improved exponential moving average, and uses interval prediction as a control constraint to provide stable power supply for the integrated energy system; The intraday lower-level model determines the intra-period dispatch plan based on the upper-level results; Finally, different scenarios are compared through numerical examples. The results show that the proposed method can reduce the impact of wind power prediction errors on dispatch results while improving system economy.

To address the optimal design of a wind-solar complementary integrated energy system （IES）, a coordinated optimization method of system structure and equipment capacity is proposed. A layered model for IES is firstly established using the principle of energy cascade utilization. The coordinated optimization problem of system structure and capacity that minimizes annual total cost is then formulated as a bi-level iterative optimization problem. The upper level optimizes the investment in renewable energy resources, energy conversion and storage equipment, and their interconnection scheme. The lower level optimizes the capacity and operation scheme of the selected equipment. A genetic algorithm and commercial solver CPLEX are applied to solve the problem. Finally, simulation results show that the proposed method reduces the annual total cost by 8.7% compared with the capacity configuration method based on the predefined system structure, which verifies the effectiveness of the proposed method.

To make grid-connected power generation systems more compatible with photovoltaic, fuel cell, wind power and other clean energy power generation equipment, a coupled inductance DC-DC converter based on a new type of double-switched structure capacitor is proposed in this paper. The high gain, low loss and low switching voltage stress of the converter are realized by a novel double switched capacitor structure and a coupled inductance boost unit. Importantly, the voltage stress of the two power switches can be evenly distributed through the passive clamping circuit in the novel double-switched capacitor structure, thus reducing their peak voltages. At the same time, the leakage energy can be recovered and the efficiency of the converter can be improved. This paper introduces its operating mode and steady state analysis. The stresses and theoretical efficiency of the components are analyzed and compared with other DC-DC converters. Finally, according to the design criteria, a 200 W prototype was built in the laboratory to verify the effectiveness of the proposed converter.

To address the uncertainties associated with renewable energy output and load, and to achieve a robust, economical, and low-carbon balanced optimization of the system, this paper proposes a two-stage robust optimization model for integrated energy system scheduling, considering a variety of uncertainties and demand response mechanisms. On the operator side, demand response model based on pricing and substitutive demand response model are constructed. On the customer side, a user-incentive demand response model is developed. Additionally, user psychological factors, such as the endowment effect and environmental awareness, are incorporated to describe the extent of user participation in the incentive demand response. A set of adjustable uncertain parameters is defined, including wind power, photovoltaic power, load, and user psychological factors, and a two-stage robust optimization model is constructed with the primary objective of minimizing the system's operational costs. Finally, through simulation, data analysis, and scenario comparison, the economic efficiency, robustness, and low-carbon attributes of the proposed model are validated.

Aiming to address the problems of excessive energy consumption, high emission and significant cost of data centers, this study proposes an integrated energy system for adata center with solar energy and natural cooling source combined with hydrogen storage, which integrates photovoltaic module, collector, internal combustion engine, absorption refrigeration unit, and fuel cell. System configuration and operating parameters are optimized using a two-layer optimization method. In the upper layer, a non-dominated sorting genetic algorithm is adopted to optimize the system capacity, In the lower layer, a multi-objective grey wolf optimization algorithm is used to optimize the ratio of operating equipment output. System performance is significantly improved by weighing the multi-dimensional indicators of energy efficiency, economic benefit, environmental protection, system independence, and sustainability. Through optimization, primary energy conservation rate reaches 46.33%, annual cost efficiency rate is 38.25%, CO₂ emission saving rate is 49.08%, grid integration level is 56.07%, annual total operating cost is $ 3.41×10⁵, and carbon usage effectiveness is 0.62. This study provides a new idea and method for the planning and design of data center energy systems by flexibly adopting multiple energy supply strategies.

The probability prediction problem of regional new energy power generation in the province faces difficulties such as multiple power station objects, high dimensional characteristic variables, and influence of regional meteorological factors. Therefore, this paper proposes a non-parametric probabilistic prediction method for regional new energy power in the province. Firstly, the historical meteorological factors of each station are used as input data, and the improved clustering method is used to construct wind power and photovoltaic clusters based on meteorologically similar areas, and then the meteorological information of each cluster is weighted and aggregated into available meteorological factors for the provincial regional prediction model. Based on this, the meteorological factors most related to new energy output are screened using Spearman and maximum information coefficient as the final input factors of the model to improve the model training efficiency. Finally, a nonparametric probabilistic prediction model based on time convolutional neural network （TCN）-quantile regression （QR）-kernel density estimation （KDE） is constructed. Taking all the wind power and photovoltaic plants in a southwestern province as application examples, the effectiveness of the proposed method is verified.

To balance exergy efficiency and economic cost of microgrids, and to ensure the accuracy and objectivity of the optimization results, a multi-objective capacity optimization method is proposed for hydrogen-based combined cooling, heating, and power （CCHP）independent microgrids, with a focus on exergy efficiency. First, the recovery of waste heat during the electricity-hydrogen-electricity conversion process is considered. An independent microgrid system is proposed, which includes wind and photovoltaic power systems, hydrogen storage systems, air source heat pumps, and absorption chillers. Secondly, the coupling relationship and energy utilization levels of various energy sources in the system are measured using exergy analysis. A multi-objective capacity optimization model is then created with the optimization goals of system's economic cost and exergy efficiency. Lastly, to acquire the best configuration results that consider system's economic cost and exergy efficiency, the ε-constraint approach in conjunction with CRITIC-TOPSIS is employed. The illustration verifies the validity of the proposed model and method, which can satisfy the demand of multiple loads in different typical day scenarios.

In order to study the operational characteristics of a combined solar photovoltaic-thermal system and multi-source heating system, an experimental platform for a solar photovoltaic-thermal combined dual-source heat pump system was constructed. The system was tested in heating, cooling, and hot water production modes, with a focus on analyzing the heat output, COP, electricity consumption, etc., when heating from the PV/T source and air source in heating hot water production and heating modes. The impact of heating from the PV/T water tank on the electrical and thermal efficiency of the PV/T panel was also examined, along with an analysis of the system's energy saving performance during actual operation. The experimental results indicate that in heating mode during cold and sunny winter weather, the average COP of the heat pump unit is 2.18, while the system's average COP is 3.17, with heating from the PV/T source accounting for 61.57% of the operating time. In the hot water production mode, at the same heating temperature, the heat pump performance is superior when heating from the PV/T source compared to heating from the air source. Heating from the PV/T water tank can effectively reduce the temperature of the PV/T panel, thereby enhancing its electrical and thermal efficiency.

An energy storage scheduling method for photovoltaic-storage microgrids is designed, taking into account the concentrated response of electric vehicle （EV） charging demand. The composition structure and operating principles of photovoltaic-storage microgrids are comprehensively considered, and the influencing factors on photovoltaic-storage microgrids under concentrated EV charging demand response are analysed. Based on the analysis results in the context of concentrated EV charging demand response, scheduling constraints are set from aspects such as energy storage output, demand response, and power balance. Through the calculation of energy storage capacity and EV electricity demand, the results for energy storage scheduling quantities are obtained. Based on these results, a scheduling method is planned to accomplish the energy storage scheduling tasks for photovoltaic-storage microgrids. Experimental results indicate that, compared with traditional scheduling methods, under concentrated charging demand response, the average charging capacity and charging speed of electric vehicles reached 52.3 kWh and 89.6 kWh, respectively, while the balance degree of the photovoltaic-storage microgrid is significantly improved.

Access to virtual power plants that aggregate a large number of dispersed demand-side resources provides new possibilities for the efficient development of power systems in the future. The flexible load in the virtual power plant can participate in the demand response and bring this flexibility into the power system planning framework, which can effectively reduce the system planning and operation costs and promote the consumption of renewable energy. Therefore, this paper proposes a multi-type power planning method considering virtual power plant empowerment. Firstly, the adjustable potential model of virtual power plant is established for different types of virtual power plants such as electric vehicles, air conditioning loads and industrial loads. Secondly, considering the operation constraints of virtual power plant for power planning, a power planning model considering virtual power plant empowerment is constructed. Furthermore, considering the uncertain factors such as the prediction error of new energy and the characterization error of the response potential of virtual power plants, the interval optimization method is adopted to deal with it. Then, the interval programming model is definitely transformed by interval order relation and interval possibility degree, and an improved benders decomposition algorithm is proposed to realize the efficient solution of the transformed model. Finally, an example is given to verify the effectiveness of the proposed power planning model and method.

Aiming at the problems of low thermal efficiency and poor thermal stability of traditional Trombe wall in winter, a central heat storage type Trombe wall is proposed. Through finite element fluid analysis software, continuous transient simulations are carried out using the dynamic data of winter and summer solstice in Baoding area, and the changes in indoor temperature, heat collection efficiency and liquefaction rate of phase change materialc （PCM） are analyzed under 9 conditions with different heat storage materials, locations, wheater the central wall is opened or closed, etc. The results show that in cold areas, the thermal efficiency of central phase change heat storage type two-channel Trombe wall （PCM-TP） can reach 31.6% in winter, which is 4% higher than that of traditional Trombe wall structure （AL-OP）, the relative daily temperature range is 2.1 ℃ lower, and the night temperature can be up to 8.23 ℃ higher. Compared with traditional Trombe wall in summer, the temperature peak time delay is 4 hours, and the relative value of daily range is 1.57 ℃ lower. In both winter and summer, the performances of the central phase change thermal storage two-channel Trombe wall （PCM-TP） are better than other schemes.

For the dynamic heat transfer characteristics of building external walls in solar-enriched areas under the dual thermal disturbances between low temperature and intense solar radiance, it is necessary to efficiently optimize the thermal insulation design in winter. Taking Lhasa as an example, this study uses a dynamic heat transfer numerical calculation model to analyze the influence of the change of thermal resistance and thermal inertia index on the thermal response characteristics of composite external walls. After that, the functional relationship expressions among these three factors, namely Poly2D, LogNormal2D, and Plane, were constructed, and the variations patterns of the interior surface temperature of the south and north facing external walls were revealed under the combined influence of thermal resistance and thermal inertia index. On this basis, the matching relationship suggestions between the minimum thermal resistance and thermal inertia index of external walls were proposed. The findings obtained in this study can provide rapid and convenient parameter optimization guidance for the optimized design of the thermal performance of building envelopes in solar-enriched areas.

To address the challenges of complex construction process, difficult transpot ation and install atim, and excessive steel usage that arise with the increasing height of traditional monopole wind turbine towers, this study puts forward a novel hybrid lattice tower design. The proposed structure integrates a monopole steel tube at the top with a three-legged lattice structure at the bottom, with the goal of enhancing structural performance and material efficiency. Firstly, a force-based model was developed using the spatial matrix displacement method to calculate the member stresses of the tower. Subsequently, a multi-objective optimization model was established to maximize stress uniformity across cross-sections. The tower's diameter and thickness were selected as key design variables, and six constraints were considered. To optimize the cross-sectional dimensions of the lower tower section, a computational optimization program was developed by integrating the spatial matrix displacement method with a particle swarm optimization algorithm. The results were validated using finite element models at three angles （α=60°、α=45°、α=30°） in ABAQUS, which facilitated a comparative analysis of the structural performance before and after optimization. The results demonstrate that the proposed force-based model accurately predicts the member stresses, and the structure is effectively optimized. Specifically, whenα=45°, the optimization effect is optimal and the convergence is fastest. The optimized hybrid lattice tower achieves substantial steel savings, with a reduction of approximately 48.8%. Additionally, the cross-sectional efficiency is enhanced by 70.3%, and the stress differences among the column members are diminished by approximately 70.2%. These improvements confirm the feasibility of the proposed optimization model. The methods and results presented in this study offer valuable insights for the design and analysis of similar hybrid lattice towers.