In this study, a theoretical prediction model of mixed molten salt electrical conductivity was proposed based on the theory of heat generation by ion friction under an AC electric field, and a set of molten salt electrical conductivity measurement devices was designed to analyze the electrical conductivity of Solar salt, Hitec salt and Hitec XL salt. The results show that the mean absolute percentage error （MAPE） values between the theoretically predicted values and experimentally measured values of electrically conductivity for three types of mixed molten salts are 11.646%, 5.473%, and 3.419%, respectively. The trends of theoretical predicted values and experimental measured values are consistent, confirming the accuracy of the proposed theoretical prediction model.

To meet the needs of integrating renewable energy into coal-fired power plants, this study proposes a vertical arrangement of parabolic trough solar collectors by utilizing the vertical space of structures such as coal yard windbreak walls and perimeter walls. This approach significantly reduces land occupation while enhancing the utilization of renewable energy in coal-fired power plants. First, an optimization of the vertical arrangement, including the number of layers and spacing, was conducted to determine the optimal configuration. Given the unique solar input conditions associated with vertical PTSC placement, the performance of solar-aided power generation （SAPG） schemes was evaluated for 125 MW, 350 MW, and 600 MW units under different collector areas. The power generation performance of each scheme was simulated and compared using Ebsilon software, followed by an economic analysis. The results indicate that the optimal configuration consists of four vertical layers with a longitudinal spacing of 3.25 m. Under these conditions, the economic benefits of the vertical arrangement are comparable to those of the conventional horizontal layout, while the land cost savings compensate for the power loss caused by shading. The annual renewable energy contribution of the SAPG system in coal-fired power plants can reach up to 4%.

Solar thermal power generation based on supercritical CO₂ (S-CO₂） Brayton cycles offers several advantages, including environmental compatibility, operational flexibility, compactness, suitability for air cooling, and high solar-to-electric conversion efficiency. This paper develops a fully coupled dynamic model of an air-cooled solar tower power plant that integrates a cavity upper bubbling fluidized bed (UBFB） particle receiver with S-CO₂ Brayton cycles. The dynamic performance of the integrated system is evaluated under realistic meteorological conditions for two S-CO₂ Brayton cycle configurations： a simple recuperated cycle and a recompression cycle. The results show that the recompression cycle configuration achieves a solar-to-electric efficiency of 30.46% under design conditions, exceeding that of the simple recuperated configuration (25.67%） and meeting the target efficiency of third-generation concentrating solar power systems. Consequently, the recompression-based system requires a smaller heliostat field and reduced capacities for both the particle receiver and thermal energy storage. The system annual performance is strongly influenced by solar radiation and ambient temperature. Higher system efficiencies are obtained under clear skies and low winter temperatures, while elevated summer temperatures and increased precipitation lead to performance degradation. Although the recompression cycle suffers pronounced efficiency losses under off-design conditions at high ambient temperatures, resulting in lower annual electricity production compared to the simple recuperated cycle, it still achieves a higher annual average solar-to-electric efficiency and lower total auxiliary fuel consumption.

A new solid oxide fuel cell-gas turbine-Kalina cycle （SOFC-GT-KC） hybrid power system integrated with high-temperature solar therm chemical and methane complementary is proposed in this article, which uses high-temperature solar energy to drive methane reforming to produce hydrogen, and the produced hydrogen-rich syngas drives the SOFC-GT-KC hybrid power system to generate electricity, realizing efficient energy cascade utilization. The analysis model of the new system technical and economic performance is established, with simple payback period, dynamic payback period, net present value, and levelized cost of electricity as performance evaluation indicators. The effects of fuel price, electricity sale price, annual operating hours, interest rate and discount rate on the new system’s techno-economic performance under the given operation strategy are analyzed, and the economic performance of the new system performance in different regions with different operating conditions is studied. The research results show that the total cost of the new system within the design life is 19.23 million yuan, of which fuel cost accounts for the highest proportion, reaching 58.57%. The net present value of the new system at the end of its design life is 1.31 million yuan, with a simple payback period of 11.06 years. The calculation results of typical regional applications show that the net present value of the new system is higher and the investment payback period is shorter in areas with strong solar radiation and high electricity price, while the average levelized cost of electricity is lower in areas with low gas prices and strong solar radiation.

The temperature of the heat transfer oil plays a critical role in ensuring the safety and economic performance of parabolic trough solar thermal systems. However, due to the system’s inherent characteristics—such as large inertia, strong nonlinearity, and susceptibility to various disturbances—enhancing the performance of the outlet temperature control system in the collector field has emerged as a significant challenge and an important research focus. To address this issue, a comprehensive global nonlinear mechanistic model of the heat collection pipeline is first developed, along with a relative-degree transfer function model at typical operating points. Using these models as a foundation, the parameters of a linear active disturbance rejection controller （ADRC） are tuned and subsequently employed to train a Gaussian process regression model for predicting controller parameters at different outlet temperature setpoints. The stability of the closed-loop control system under this parameter-varying strategy is then analyzed using Kharitonov's theorem. Simulation results demonstrate that, under complex operating conditions, the proposed parameter-varying ADRC strategy achieves outstanding setpoint tracking, disturbance rejection, and robustness. Compared to traditional PI, ADRC, and parameter-varying PI controllers, the proposed approach delivers superior performance in both individual metrics and integral performance indices.

In response to the current lack of regional climate zoning studies that focus on low-carbon technologies aimed at maximizing solar energy utilization, this study investigates climate zoning for suitable low-carbon technologies in low-rise residential buildings in solar-enriched areas through an integrated methodological approach. Employing theoretical analysis and numerical simulation, this study establishes a novel climate zoning framework predicated on three cardinal indicators： heating degree-hours, annual cumulative solar irradiation, and the ratio of south-facting radiation to heating degree-hours during the coldest month. Subsequently, we developed a building performance simulation model for low-rise residential buildings using the TRNSYS platform to quantitatively evaluate the applicability of three technology categories across different climate zones： 1） building systems （incorporating direct-gain fenestration systems, Trombe walls, and attached sunspaces）, 2） energy consumption systems （featuring air-source heat pumps）, and 3） energy supply systems （comprising rooftop photovoltaic arrays and solar thermal collectors）, with consideration of both carbon mitigation potential and decarbonization costs.

A Bi-level optimization scheduling method for mixed energy storage in integrated energy systems based on photovoltaic interval prediction is proposed to address the economic dispatch problem of an integrated energy system with hybrid energy storage. The upper layer includes a data optimization and deep learning based ultra short term photovoltaic power interval prediction method and a hybrid energy storage capacity configuration. By accurately predicting and decomposing the photovoltaic power,the energy storage capacity boundary is formed. The lower layer performs an integrated energy system optimization scheduling considering the configuration of hybrid energy storage capacity,with the goal of minimizing operating costs. Based on the upper layer capacity boundary,the operating range of the integrated energy system is determined,and the capacity boundary is optimized,corrected,and fed back to the upper layer. After multiple iterations,the optimal result is obtained. The calculation results show that this method can effectively reduce system operating costs,increase the uilization of renewable energy generation,and reduce load shedding.

A sub-module（SM） open-circuit fault,as one of the common faults in Modular Multilevel Converters （MMC） affects the safe and stable operation of the system. To detect faulty SMs in a timely manner,this paper proposes an MMC open-circuit fault diagnosis strategy based on the variation of arm voltages variation. By using the abnormal arm voltage increment of the arm within two sampling periods, the faulty arm is identified. The average SM capacitor voltage within the faulty arm is then calculated and compared with each individual SM capacitor voltage. When the difference exceeds a set threshold,the faulty SM is located,and the average value is updated to continue detecting any remaining faulty SMs,this allows for the detection of both single and multiple SMs faults. Furthermore,a method for setting the threshold is provided based on theoretical analysis.This strategy features simple steps,fast detection speed,and low computational complexity, enabling the detection of both single and multiple SM faults. The feasibility and effectiveness of the proposed method have been validated through MATLAB/Simulink simulation analysis and hardware-in-the-loop （HIL） system experiments.

In order to explore the aging characteristics of insulating materials under the combined action of low temperature-stress, in this paper, epoxy resin material is selected. The aging test of epoxy resin under different pressures （0-0.3 MPa） and low temperature （-30 ℃） was carried out, and the microscopic and electrical properties of epoxy resin in different aging stages were tested, and the influence mechanism of low temperature-stress combined effect on the electrical properties of epoxy resin was analyzed. The results show that under the same stress, with the increase of aging time, the cracks in the epoxy resin widen and deepen, and the number increases significantly. As the applied stress increases from 0 MPa to 0.3 MPa, the DC breakdown voltage decreases as a whole, the conductivity increases significantly, and the densities of deep and shallow traps increase. This is mainly caused by the formation of more microcracks under the combined action of low temperature and high stress. In particular, after 30 days of combined aging with low temperature and 0.3 MPa stress, the DC breakdown performance of epoxy resin decreased by 32.0%, and the DC conductivity increased by 16.3 times.

This paper proposes a power interaction oscillation suppression strategy for virtual synchronous generator grid-connected system based on phase compensation, by analyzing the relationship between active output and phase, and establishing a feed-forward channel to compensate the output phase in the original VSG active power control, which is used for suppressing power interaction oscillations in multi-VSG systems during the frequency fluctuation grid-side frequency fluctuations, and improving the dynamic frequency response of the system. In addition, the method is not constrained by the matching conditions of multiple VSG parameters, nor does it affect the steady-state characteristics of the system, and the design and stability analysis of key parameters are given. Finally, the feasibility of the proposed strategy is verified by results derived from hardware-in-the-loop experiments.

To adapt to the green and low-carbon energy transition, this paper proposes a low-carbon optimal scheduling method for a multi-energy complementary virtual power plant （VPP） considering hydrogen energy cycle and electrolytic molten carbonate （EMC） carbon capture technology. First, a hydrogen cycle system including the electrolyzer, methane reactor, hydrogen-blended unit, hydrogen fuel cell （HFC）, and hydrogen storage tank is constructed. This system covers multiple stages of hydrogen production, storage, and utilization to fully explore the interconnected potential of hydrogen energy with electrical and thermal energy. Second, EMC technology is introduced to establish an EMC carbon capture power plant that incorporates the gas turbine, an organic Rankine cycle （ORC） low-temperature waste heat power generation unit, and waste heat boiler. Finally, considering the synergy between virtual and fixed energy storage, such as electric vehicles, alternative response loads, and air conditioning loads, as well as the differences in response performances among various storage types, a day-ahead and real-time optimal scheduling model for VPP is proposed to improve the operational flexibility. Case studies show that the proposed method can effectively facilitate renewable energy integration, reduce operating costs and carbon emissions, and enhance overall energy utilization efficiency.

Addressing the fact that new power systems have gradually evolved into complex large systems with multi-level structures and multi-time scales, it is difficult to accurately measure their safety and stability levels using a single indicator. Therefore, starting from the actual needs of the power grid, a safety and stability operation state evaluation method for new energy power systems is proposed by constructing a multi-parameter indicator system and evaluation model based on subjective and objective evaluation. The Apriori algorithm is used for association analysis and data mining of safety and stability indicator data of renewable energy power systems to obtain feature indicators closely related to the system’s operational state, and to establish a multi-parameter indicator system for system operation state. On this basis, the DEMATEL-AEW method combining subjective and objective approaches is used to calculate the subjective and objective weight values of the indicator system, respectively. Then, a cooperative game-based combined weighting method is proposed to solve for the combined weights of the evaluation indicators. Finally, the safety and stability operation state of the renewable energy power system is evaluated using cloud model theory. The results show that the proposed indicator system and evaluation method can accurately assess the safety and stability operation state of new energy power systems.

To address the impact of fluctuating reservoir capacity on the generation efficiency and stability of cascade hydropower stations,this paper proposes a two-level optimization scheduling method for a hydro-photovoltaic-storage complementary system that considers medium- and long-term reservoir capacity fluctuations.Typical output scenarios of PV and hydropower runoff are generated using kernel density estimation and Copula function,and the scenarios are reduced using K-means.The medium and long-term optimal scheduling model of water and solar power is established,and the fluctuation of the reservoir capacity of the cascade hydropower station is solved by genetic algorithm,which is used as the boundary condition of the short-term optimal scheduling model of the water-photovoltaic storage system, and the short-term scheduling model is solved by using Cplex to maximize the system benefit.The effectiveness of the method is verified by simulation analysis of a terrace hydropower station in Xinjiang.The results of the example analysis show that the water-photovoltaic storage complementary system can ensure the PV consumption through the fast regulation ability of the stepped hydropower station and the energy storage device,and after joining the medium- and long-term reservoir fluctuation constraints,the stepped hydropower station can reduce the outlet flow of the hydropower station and improve the power generation efficiency under the same output capacity.

To enhance the dynamic response capability of renewable energy grid-connected inverters under multiple operating modes （grid-connected mode and stand-alone mode）, a comprehensive virtual synchronous generator （VSG） control strategy based on optimal power compensation and adaptive inertia and damping is proposed. Firstly, the mathematical model of a conventional VSG is established, analyzing the impact of virtual parameter variations on system stability. Secondly, a radial basis function （RBF）-based adaptive method for the VSG is proposed, achieving parameter decoupling and enabling dynamic adjustment of system parameters in response to frequency variations. Subsequently, based on the VSG active power-frequency droop characteristics, a predictive model is constructed and discretized. A cost function using angular frequency deviation and VSG output power as performance indices is designed. The optimal power increment is computed via quadratic programming to adjust the VSG’s active power reference value in real-time, thereby improving the active power and frequency response characteristics during transients. Finally, simulation results demonstrate that the proposed control strategy effectively suppresses output power and frequency overshoot and oscillations, reduces the rate of change of frequency and frquency deviation, and enhances the response speed of active power and frequency.

This paper establishes a mathematical model for the electrical behaviour of cantilever-based harvesters using the principle of electric potential superposition. This research proposes a computational framework to compute the cumulative electric potential generated by continuously distributed charges within electrified domains. By partitioning the system into three distinct regions, the proposed method systematically analyses the interrelations among cantilever vibration dynamics, open-circuit voltage, and short-circuit current, culminating in an analytical expression for the electrical performance of cantilever structures under acoustic excitation. The theoretical predictions are validated against finite element simulations and published experimental data. Under boundary conditions of a 0.8° oscillation angle and 14.6 mm maximum displacement, the model yields average open-circuit voltage and short-circuit current peaks of 168.2 V and 12.7 μA, respectively. These results align closely with experimental measurements （183.1 V and 10.9 μA） and simulation outcomes, confirming the model’s efficacy in characterizing the coupled dielectric behaviour of flexible micro-nano architectures.

The large-scale wind-solar hydrogen production system is connected to the power grid, and the power grid has low inertia and weak damping, which affects the stability of the frequency of the system. To address this problem, a control strategy for a wind-solar hydrogen production system is proposed in this paper, which is based on a virtual synchronous generator with an adaptive moment of inertia and an adaptive damping coefficient. First, the inertia and damping characteristics are introduced into the grid-connected inverter control loop of the wind-solar hydrogen production system, so that the system has the inertial support characteristics similar to the synchronous generator. Then, considering the expansion of the scale of the wind-solar hydrogen production system and the improvement of the adjustment range and speed, according to the dynamic response characteristics of the inertia damping coefficient when the disturbance occurs, the inertia damping adaptive control rule is designed to expand the inertia adjustment range of the system, improve the disturbance compensation, and accelerate the recovery speed. The simulation results show that the proposed control method can effectively reduce the oscillation amplitude of the power-frequency response curve while ensuring stable operation of the large-capacity wind-solar hydrogen production system. While increasing the response regulation range of the system, the speed of system response and recovery is also guaranteed.

This paper proposes a planning method for cross-regional electricity-gas integrated energy system based on seasonal complementary characteristics. Kendall correlation analysis is used to analyze the seasonal complementary characteristics of electricity-gas integrated energy system in typical regions of China, and a joint planning model for electricity-gas integrated energy system is constructed, which integrates long-term and short-term coordinated energy storage mechanisms and flexible selection of cross-regional energy transmission modes with consideration of different time lag characteristics. Finally, an example of a cross-regional electricity-gas integrated energy system is constructed based on the actual data of Meng-Ji-Lu in China, and the validity of the proposed model is verified, which shows that the energy interconnection through the cross-regional electricity-gas integrated energy system can effectively improve the economy and energy efficiency of the system operation and analyzes the influence of the energy transportation mode on the improvement of the performance of the cross-regional system.

Large-capacity, lange-size wind turbines are the mainstream R&D trends in the wind power field. The loads imposed on the drive trains increase significantly with the increase in the capacities and sizes of wind turbines. Therefore, it is of crucial importance to conduct ground tests of wind turbine drietrains. First, the requirements for wind turbine ground test benches in wind power research are analyzed. The representative structures, technical schemes and key technologies of wind turbine ground test benches are reviewed, and the current application values of ground test benches in developing wind power technologies are briefly summarized. Furthermore, this paper analyzes the research requirements for simulation systems of ground test benches. The technical schemes and the application approaches of ground test bench simulation systems are elaborated, and the key technologies and the existing bottleneck problems in constructing ground test bench simulation systems are summarized. Finally, considering the current developmental requirements of wind power generation, this paper anticipates the future development of wind turbine ground test benches and their simulation systems.

In order to improve the energy and economic benefits of the integrated energy system （IES）, a two-stage optimal configuration method of the IES is proposed, which considers energy grade difference and the interaction between supply and demand. The first level combines the first and second laws of thermodynamics to optimize the rated capacity of renewable energy units, energy conversion and energy storage equipment with the objective of maximizing the system cost saving rate and exergy efficiency. At the second level, the demand response strategy is introduced to implement the cooperative optimization of source, storage and load, which aims to minimize the cost and energy consumption and determine the operation scheme of the system, and the differential evolution algorithm and nonlinear programming method are organically combined to solve the two-stage model. The simulation results show that the proposed method can improve the energy and economic performance of the IES while optimizing the system capacity.

To regulate PSU output in response to wind power output fluctuations, an optimization model for wind-thermal-pumped storage systems is proposed based on the sensitivity of equivalent pump-generation duration （PGD） of pumped storage units （PSU） with respect to wind power uncertainty. Firstly, a polyhedral uncertainty set is used to describe wind power uncertainty, and an optimal dispatch model for wind/thermal/pumped storage systems is established. Secondly, an analytical expression of the sensitivity of PGD with respect to wind power uncertainty is proposed to obtain PGD adjustments. Finally, using upper reservoir capacity change as an intermediate variable, the relationship between PGD and power adjustment is newly derived to adjust PSU according to wind power prediction deviation. Case study results verify that the proposed model improves system flexibility capacity and PSU utilization while ensuring system economics.

To further exploit system flexibility resources,an optimal scheduling model for integrated energy systems （IES） is proposed,which incorporates both thermal dynamics of heating networks and user thermal comfort.Firstly,a thermal dynamic model of heating networks is developed based on the thermal energy transfer characteristics of pipelines. Secondly,the predicted mean vote （PMV） index is introduced to quantify thermal comfort,and the thermal load is determined according to the range of user thermal comfort.Then,considering the impact of heat medium flow rate uncertainty on system scheduling,the information gap decision theory （IGDT） is employed for analysis,and a robust stochastic optimization model is constructed using a risk-averse strategy.Results show that the proposed model significantly improves economic performance and enhances wind power accommodation;while the IGDT method effectively characterizes the robustness of system scheduling against heat medium flow rate fluctuations.

The transmission of clean energy sources, such as wind power from the Northwest, across regions to southern provinces with primary energy shortages, is a crucial support for building a diversified clean energy supply system. The output of high-proportion hydropower in the southern receiving grids and wind power in the northern sending grids can be significantly affected by hydrological droughts and widespread low-output wind power events, respectively. This poses severe challenges to ensuring a stable power supply. Due to the lack of long-term operational experience with high-proportion clean energy transmission systems across large regions, a wind power shortage event assessment method based on meteorological data inversion is proposed. By combining ERA5 reanalysis meteorological data with wind turbine power output models, and constructing hourly time series of regional wind power capacity factors, the interannual and seasonal wind power shortage intensity and duration are analyzed. Using meteorological and hydrological data from Gansu and Hunan provinces, the monthly wind power shortage intensity and hydrological drought indices over several years are calculated. The coupling levels between hydropower and wind power are classified accordingly, and the seasonal characteristics and interannual variations of the output from both sending and receiving end power sources are analyzed, and the patterns of insufficient output from wind and hydropower at both ends are identified.

To address the issues of unreasonable power distribution in tdistributed wind power hydrogen production systems, which leads to frequent switching between different states. This paper proposes a group rotation strategy of the hydrogen production by wind power system hybrid electrolyzer based on fuzzy piecewise power control, which realizes reasonable power distribution between groups within the hybrid electrolyzer system through fuzzy controllers. Firstly, a mathematical model for a hybrid electrolyzer was developed, incorporating the operational characteristics of alkaline electrolyzer and proton exchange membrane electrolyzer. The hybrid electrolyzer was stabilized by direct power control and supplemental power control strategies. Secondly, the mixed integer piecewise model algorithm based on power intervals was used to ensure array's coordinated control capability. Simultaneously introducing a grid-forming energy storage system to improve the dynamic coupling of hydrogen production by wind power system at multiple time scales, while also ensuring the safe and reliable operation of the hybrid electrolytic cell array. Finally, a simulation model of the hydrogen production by wind power system based on hybrid electrolyzer was constructed. Compared with the simple time averaged control strategy, the results show that the proposed rotation strategy can achieve balanced switching frequency and operating time of the hybrid electrolyzer system, rational allocation of power, and improve the overall hydrogen production efficiency of hydrogen production by wind power system comprehensively.

This study aims to address the insufficient research on the flexible regulation characteristics of air conditioners under the DC architecture and the lack of long-term operational data support in real-world applications. A systematic investigation is conducted on the theoretical foundation, key technologies, and practical applications of DC flexible residential air conditioners. Using theoretical analysis, laboratory tests, and engineering case validation, the technical characteristics, operating rules, and application value of DC flexible residential air conditioners are identified. The results demonstrate that DC flexible residential air conditioners optimize their hardware architecture by removing rectifier and PFC circuits while adding protection circuits such as reverse polarity protection and surge protection. Their wide-voltage operation control and flexible control strategy enhance reliability and enable autonomous adjustment of power consumption. Furthermore, both laboratory tests and field cases verify that DC flexible air conditioners operate stably under various voltage conditions, with prominent flexible performance at low voltages.

To improve the accuracy of short-term prediction for non-stationary wind speeds, a rolling decomposition （RD） and prediction method was built by using a sliding window. By comparing the predictive results of various neural network models and incorporating the variational mode decomposition（VMD） algorithm, an RD-CNN-BiLSTM neural network combined model was proposed for predicting non-stationary wind speed sequences. Furthermore, addressing the predictive accuracy limitations of the neural network combined model, a short-term predictive approach for non-stationary wind speeds that integrates the error correction and RD-CNN-BiLSTM neural network combined model was proposed. The research results indicate that applying sliding windows to the actual wind speed data and carrying out a single prediction and extension, followed by decomposition, trimming, and reconstruction of the extended data, the impact of boundary effects can be effectively weakened and the information leakage can be prevented. The CNN-BiLSTM combined model outperforms traditional individual neural network models, such as autoregressive integrated moving average（ARIMA）, back propagation（BP）, long short-term memory（LSTM）, and bidirectional long short-term memory （BiLSTM） models, in terms of the predictive accuracy. It was found that the quadratic parabola error correction method is superior to the linear error correction method, particularly in high wind speed ranges, where the predictive accuracy improvement by the former method is especially significant. The predictive results from actual wind speed data at different time intervals further validate that the proposed approach of integrating error correction and RD-CNN-BiLSTM neural network combined model has high predictive accuracy and generalization in short-term prediction of non-stationary wind speeds.

To evaluate the health status of wind turbines, this paper proposes a CNN-BiGRU-based health evaluation method. Firstly, the quartile method is used to eliminate the abnormal data in the wind turbine supervisory control and data acquisition （SCADA） system, and the maximum relevance minimum redundancy （mRMR） algorithm is applied to select power-related features. Then,a CNN-BiGRU-based power prediction model is constructed to predict the wind turbine's output power.The convolutional neural network （CNN） extracts high-dimensional input features effectively, while the sparrow search algorithm （SSA） optimizes the parameters of the bidirectional gated recurrent unit （BiGRU） network. A benchmark distribution model is established based on the power prediction residuals of wind turbines in a healthy state, calculate the Mahalanobis distance （MD） from the residuals of real-time prediction results to the benchmark distribution model, and construct wind turbine health indicators based on this MD to evaluate the health status of wind turbine.

A robust model predictive control （RMPC） approach for wind turbines is introduced in this paper, which integrates the Koopman operator with robust control strategies. A dynamic model of the wind turbine and its wind speed model is derived based on fundamental principles. By collecting relevant data, a Koopman-based linear prediction model is developed and validated through Matlab simulations. To address model prediction errors, robust techniques are introduced for error management. Simulation results show that the proposed method enhances the overall robustness of the wind turbine system.

To explore efficient anti-icing and de-icing technologies, this paper employs electrical heating for wind turbine blade de-icing experiments in a small recirculating icing wind tunnel. Polyimide electrical heating films are used to cover the entire blade surface. The experimental conditions consist of an ambient temperature of-10 ℃, a wind speed of 10 m/s, and three energy flux densities： 8, 10, and 12 kJ/（m²∙s）. The de-icing process on the blade surface is observed, revealing the changes in the ice layer on the blade's leading edge. Additionally, the meltwater features produced during the de-icing process are analyzed. The experimental results indicate that as the energy flux density increases, the detachment rate of the ice layer accelerates. Furthermore, the energy consumption for electrothermal de-icing decreases. At the final stage, approximately 80% of the ice layer detaches in one piece, leaving meltwater residue on the blade surface. The meltwater detachment zone is between 65.43° and 90.86° under varying energy flux densities. As the energy flux density increases, the distance between the meltwater detachment zone and the blade's leading edge first increases and then decreases. This zone can provide a reference for addressing secondary icing issues in practical engineering.

Addressing the requirements for low-cost, lightweight, and large-scale development of floating offshore wind turbines （FOWTs）, a taut-moored FOWT foundation form based on the air-floatation characteristics of tetrapod bucket foundations is proposed. The mooring force characteristics and internal pressure variations of the foundation are directly related to the structural safety and stability. This paper presents an analysis of the effects of pre-tension, water depth-to-draft ratio, and anchoring distance on the response amplitude operator （RAO） of mooring line tension and internal pressure variations within the foundation through small-scale wave model tests of tetrapod bucket foundations with taut mooring. The research findings indicate that the changes in mooring line tension and internal air pressure exhibit similar patterns, with the RAO of the front mooring line tension showing smaller variations compared to the rear lines. As pre-tension increases and draft deepens, the RAO of mooring line tension initially decreases and then increases. A excessively small water depth-to-draft ratio for this structure leads to increased mooring line tension, whereas the structural performance is superior in deeper waters. When slow-drift motions dominate, increasing the anchoring distance to a certain extent can improve the mooring force characteristics of the structure.

The traditional PI controller has fixed parameters and weak adaptability in different scenarios, and the speed recovery process is easy to cause the problem of large secondary frequency drop depth. To this end, this paper proposes a wind turbine speed recovery strategy for mitigating secondary frequency drop based on fuzzy PI controll. Firstly, the frequency dynamic response characteristics of power system are analyzed, and the influence of wind turbine on frequency response characteristics in frequency regulation and speed recovery stage is studied. Secondly, a wind turbine speed recovery strategy based on fuzzy PI control is proposed, and fuzzy rules are designed to take into account the rapid recovery of wind turbine speed and the mitigation of secondary frequency drop. Finally, based on Matlab/Simulink simulation software, a system model with wind power is built for simulation analysis. The simulation results verify that the proposed strategy has faster speed recovery characteristics and the feasibility of weakening the secondary frequency drop under the same released rotor kinetic energy.

Steel pile jacket foundations are widely used in offshore wind power engineering, where the mechanical properties of the soil-steel pile interface largely determine the bearing capacity of the foundation. In this study, based on a self-developed interface shear testing apparatus and PFC （particle flow code） discrete element numerical simulations, three types of structural planes with different morphological characteristics were examined under identical roughness conditions to investigate the influence of surface topography on the shear characteristics of the sand-steel interface. The results indicate that： 1） The mechanical behavior of interfaces with different morphologies exhibits similar trends with increasing roughness. Both peak shear stress and interface efficiency increase with roughness until reaching a critical roughness value, which corresponds to a morphological depth equal to D₅₀ （median grain size）. Beyond this critical point, shear stress and interface efficiency change gradually. 2） When the morphological depth is small, different structural surface topographies significantly affect peak interface shear stress, with the maximum difference occurring at the critical roughness. As morphological depth increases, the influence of different structural surface topographies diminishes, and peak shear stresses converge to similar values. 3） Shear band thickness increases with morphological depth and then stabilizes as depth continues to increase. Furthermore, structural surface with a greater number of grooves exhibit larger shear band thicknesses.

This study investigates the impact of mountainous terrain on wind farm wake effects in a coastal peninsula of southeast China using a mesoscale weather forecast model coupled with a wind farm parameterization scheme. The results show that the peninsula's topography significantly modulates the spatiotemporal characteristics of atmospheric motion. Under low sea-surface wind speeds, atmospheric transport in the mountainous wake region is predominantly horizontal with limited vertical diffusion. This leads to wind acceleration near the sea surface （below 150 m） but a pronounced speed reduction between 150-350 m above sea level. Under these conditions, the wind farm wake deficit remains concentrated around the rotor area, extending horizontally downstream with minimal influence on the near-surface layer （0-150 m）. As wind speed increases, enhanced vertical transport and diffusion raise wind speeds in the 150-350 m altitude range and expand the vertical extent of the wake, which can reach up to 300 m above the sea surface.

This paper reviews recent developments in short-term extreme value prediction for offshore wind turbine responses, with a particular focus on statistical and machine learning methods. Statistical methods, including the Gumbel distribution, Peak Over Threshold （POT）, Mean Upcrossing, and Average Conditional Exceedance Rate （ACER） methods, model turbine response probability distributions based on sample data for short-term extreme value estimation. Additionally, machine learning techniques, such as artificial neural networks and Gaussian process regression, improve prediction accuracy by automatically extracting relevant data features and constructing nonlinear models. This review highlights the strengths and limitations of various short-term extreme value prediction methods, discusses their applicable scenarios, and outlines potential future directions for advancing prediction technologies.

Wind turbine foundation soils in mountainous regions are often subjected to harsh climatic conditions characterized by intense rainfall and rapid evaporation, which may potentially induce structural deterioration in compacted residual soils. To investigate the effects of rainfall and solar drying on the structural damage characteristics of compacted residual soils in wind turbine foundations in mountainous areas, consolidated drained triaxial tests, scanning electron microscopy （SEM） examinations, and mercury intrusion porosimetry （MIP） tests were conducted on residual soils from wind turbine foundations in the mountainous region of Chenzhou, under 0 to 4 drying-wetting cycles. The results show that cohesion exhibits an exponential decay trend with an increasing number of drying-wetting cycles, decreasing by 37.57% after 4 cycles, whereas the internal friction angle remains almost unaffected. During water absorption, clay minerals expand, while during drying, internal stress imbalance caused by soil volume changes generates additional pores and cracks within the soil samples. This leads to alterations in the microstructure and ultimately results in the degradation of macroscopic mechanical properties. After four drying-wetting cycles, the volume proportions of micropores and small pores decreased by 1.8% and 11%, respectively, whereas the volume proportions of mesopores and macropores increased by 7.4% and 5.4%, respectively. The shear strength of the foundation residual soil exhibits a linear negative correlation with the average pore diameter. These findings are significant for understanding the mechanical behavior and microstructural evolution of residual soils in wind turbine foundations.

To tackle the issues of large search space and long computation time for searching the optimal circular collection line in wind farms, the optimization method is proposed based on the frequency graph-ant colony algorithm. Firstly, the distance matrix is constructed using according to the coordinates of wind turbines in a given wind farm. Secondly, the frequency graph model is computed with the frequency quadrilaterals, which illustrates the wind turbines and their connections. The distance matrix is converted into the frequency matrix. In the frequency matrix, the frequencies of the lines in the optimal solution are higher than those of most of the other lines between the wind turbines. Based on this property, a lot of the lines excluding from the optimal solution while between the wind turbines are eliminated. The search space of the optimal solution is significantly reduced. The ant colony algorithm is employed to detect the optimal solution within the reduced search space and the optimal or near-optimal solution is found. The experimental results illustrated that approximately 70% of lines that are not in the optimal solution are deleted. It greatly reduces the search space of the optimal solution and verifies the advantages of the frequency graph.

To analyze the impact of direct-drive wind turbine integration on power system small-signal stability while avoiding complex modeling, this study combines modal energy analysis technology with the eigenvalue method from classical small-signal stability theory, establishing a modal damping energy function for evaluating system dynamic behavior. The low-frequency dynamic phenomena caused by grid integration of direct-drive wind turbines are quantitatively characterized in an energy terms. Firstly, the distribution characteristics of low-frequency oscillation modal damping energy in systems with multiple direct-drive wind turbines integrated into the grid are analytically examined. Subsequently, the modal damping energy of key oscillation links is extracted. Based on modal energy analysis and eigenvalue sensitivity theory, a quantitative stability evaluation framework suitable for direct-drive wind turbine grid-connected systems is established, and the effects of different integration locations of direct-drive wind turbines on power system dynamic oscillation characteristics are investigated. Finally, a small-signal stability regulation strategy for direct-drive wind turbine integration is proposed based on small-signal stability evaluation indices and sensitivity indicators. The reliability and applicability of the proposed method are validated through small-signal stability analysis in both a 3-machine 9-bus system and the New England 10-machine 39-bus system.

In order to improve the starting performance of lift-type vertical axis wind turbines, this study combines drag-type blades with the cross braces of the wind turbine and proposes a new type of drag-type cross brace, thus forming a lift-drag hybrid vertical axis wind turbine. Computational fluid dynamics（CFD） method is used to analyze the impact of the drag-type cross brace on the aerodynamic performance of the wind turbine at different rotational speeds. The results show that the drag-type cross brace can effectively increase the torque of the wind turbine at low rotational speeds and improve the starting performance of the wind turbine. At the rated rotational speed, it slightly reduces the torque of the wind turbine. At high rotational speeds, it significantly reduces the torque of the wind turbine and plays the role of an aerodynamic brake. With the increase in the length proportion of the drag-type blades on the cross brace, the improvement in the starting performance of the wind turbine at low rotational speeds is enhanced. At the rated rotational speed, the torque of the wind turbine further decrease, while there is no significant change in the aerodynamic performance of the lift-type blades.

Aiming at the problem that wind turbine blades are prone to fault and difficult to detect in harsh environments, a new fault diagnosis method is proposed. This method combines the Frequency octave theory and the Mel Frequency Cepstrum Coefficient （MFCC） algorithm, and optimizes the traditional MFCC algorithm by introducing the symmetric variable frequency octave-based technology. In terms of frequency band division, according to the characteristics of blade sound signals and octave theory, the mapping relationship between physical frequency and Mel frequency is reconstructed to enhance the algorithm’s ability to extract fault features distributed in the middle frequency band and high frequency band, and effectively reduce noise interference. Then, the K-means clustering algorithm is used to cluster the acoustic features extracted by the optimized MFCC algorithm. The elbow rule is used to determine the optimal number of clusters under different states of blades, and the noise clusters are removed according to the short-term energy distribution, so as to effectively distinguish the sound signals of different states of blades. Finally, a classifier based on random forest algorithm was constructed to accurately diagnose blade faults. It verifies the ability of the improved MFCC algorithm to extract the acoustic features of the wind turbine blades and anti-interference.

Considering the issues of cracking and slurry leakage resulting from the fatigue damage of a wind turbine foundation with a foundation-ring under long-term wind loads, a three-dimensional calculation model of the wind turbine foundation was established using the midas FEA NX finite element software. This model was employed to simulate the response of the foundation under the measured wind load. The location of cracks and the stress level in critical areas of the wind turbine foundation were analyzed, thereby revealing the failure development process of the foundation. The analysis results indicate that under long-term wind loads, the interface between the foundation ring and the concrete foundation experiences fatigue shear damage, leading to interface disengagement and stress concentration in the radial reinforcement. The swing of wind turbines causes the lower flange to continuously grind the concrete to form a cavity, and the concrete around the foundation ring is repeatedly stamped and broken. To solve the above problems, based on the on-site investigation and video detection of the flange located beneath the foundation ring, it is recommended to utilize structural epoxy adhesive to fill and reinforce the cavities and cracks surrounding the flange, thereby enhancing its structural integrity. The relevant data of the reinforced wind turbine foundation show that the integrity and crack resistance of the reinforced concrete foundation have been effectively enhanced under the same measured wind load, and the reinforced wind turbine is in good operating condition.

In order to reasonably determine the scour protection measures and engineering quantities, it is necessary to accurately estimate the local scour range and volume of the pile foundations. Based on the analysis of concepts such as internal friction angle of sediment particles, underwater angle of repose, and slope angle of scour pits, this article uses effective data from 240 sets of unprotected pile foundation scour monitoring at an offshore wind farm in Yancheng, Jiangsu and 96 sets at an offshore wind farm in Zhanjiang, Guangdong. Local scour pits are generalized as annular circular cones, and the empirical relationship between local scour pit slope angle and scour depth is statistically analyzed. A new method for calculating the local scour volume and range of pile foundations under the combined action of waves and current is proposed. The verification using the reserved 60 sets of scour monitoring data showed that the local scour range and volume of the pile foundation calculated according to the formula based on internal friction angle in the current specifications were significantly smaller, while the scour volume calculated by the method in this paper was quite close to the measured volume as a whole. The local scour pit of the pile foundation is located below the bed surface, and the sediment particles on the slope do not need to be in a critical incipient-motion. The slope angle is smaller than the underwater angle of repose and internal friction angle. The calculation formula for local scour range in current standards should be revised accordingly. This method provides a basis for adopting targeted scour protection measures and developing reliable scour protection devices. However, in the future, it is still necessary to continue accumulating local scour monitoring data of offshore pile foundations to continuously improve the calculation accuracy of scour volume and range.

Two parametric stability calculation methods are developed for semi-submersible platforms with medium- and large-angle inclinations, using a typical four-column semi-submersible platform as the study subject. The theoretical calculation methods are based on the equal displacement volume approach, which eliminates the need for geometric modeling and facilitates rapid parameter iteration in the early stages of platform design. These methods are employed to examine the effects of key design parameters on the stability of the semi-submersible platform. The results reveal that： the variation in the center of gravity position exhibits a sinusoidal relationship with the change in the stability arm; the most critical inclination axis for the platform corresponds to the 0° wind direction, implying that future studies on similar platforms will not need to identify the most critical inclination axis; parameters such as the water ingress angle, waterline moment of inertia, freeboard height, and column radius all show a positive correlation with stability; column spacing has an optimal value for maximizing the stability arm; and beyond a certain threshold, draft depth does not significantly affect the stability arm. These findings provide valuable insights for adjusting design parameters in platform development.

This study leverages the FLAC 3D finite difference software platform and utilizes the SANISAND constitutive model to simulate the stress-strain behavior of sand soil. It systematically investigates the dynamic behavior of suction bucket foundations under the combined action of vertical tensile loads and seismic loads in the context of local scour conditions. The findings reveal that seismic actions can induce cumulative vertical displacement in suction buckets subjected to vertical tensile loads, potentially leading to foundation failure due to excessive displacement. Scour exacerbates this issue by increasing the depth of liquefied sand under seismic loading, reducing the bucket-soil frictional resistance, and diminishing the uplift capacity of the suction bucket, thereby further compromising the foundation's integrity. Additionally, the degree of liquefaction in the surrounding sand decreases with decreasing distance from the outer wall of the suction bucket in the horizontal direction. Notably, the cumulative pore pressure inside the suction bucket is significantly lower than that outside, indicating that the presence of the suction bucket exerts a substantial inhibitory effect on the liquefaction of the surrounding sand.

To address the degradation in fault diagnosis accuracy caused by the imbalance of bearing fault data, this paper proposes a fault diagnosis method based on a wavelet-like transform generative adversarial network （WLT-GAN）. In the proposed method, the wavelet-like transform neural network is embedded into the generator and combined with a dual-discriminator architecture, enabling the WLT-GAN to jointly learn time-domain and frequency-domain features from vibration signals and generate high-quality fault samples to effectively alleviate data imbalance. In addition, an ensemble learning strategy is employed to construct the fault diagnosis model, where a soft-voting mechanism integrates multi-source features to improve diagnostic accuracy. Experimental results demonstrate that the samples generated by WLT-GAN exhibit high similarity to real data in both time- and frequency-domain feature distributions. Leveraging the advantages of ensemble learning, the proposed method achieves high accuracy and robustness, providing an efficient and reliable solution for bearing fault diagnosis in wind turbine generators.

For the accuracy decreasing in fault diagnosis derived from the samples insufficient of the rolling bearings in wind turbines, a fault diagnosis method based on deep convolutional-conditional Wasserstein generative adversarial network （DC-CWGAN） is proposed. Firstly, the continuous wavelet transform （CWT） is applied to the vibration signals to construct a dataset of time-frequency feature images. As a result, the feature capture ability for the fault model is increased. Secondly, the fully connected layers in the conditional generative adversarial networks （CGAN） are replaced by the convolution al structures. Followingly, the Wasserstein distance is introduced to reconstruct the CGAN loss function. Thus the quality of the generated samples and the stability of the DC-CWGAN training are both strengthened. Thirdly, with the application of the model transfer strategy, the generalization and the computational efficiency of the objective classification network are enhanced. Finally, it is demonstrated that the diagnosis accuracy of the rolling bearings under the small-sample condition is improved effectively by the proposed method.

Taking the OC5 semi-submersible offshore floating wind turbine developed by NREL as the research object, an integrated simulation model was established for both the floating foundation and the wind turbine. Time domain and frequency domain spectral analysis methods were used to perform structural fatigue calculations on the floating foundation. Various approaches were employed to combine the fatigue damage induced by wind turbine loads and wave loads, and the results were compared with the fatigue damage of the structure under combined wind-waves loading conditions obtained through time-domain method. The research results indicate that the fatigue damage estimates derived from the damage combination method, equivalent stress 90° out-of-phase superposition method, and DNV specification method show the closest agreement with the time domain calculation results, making them suitable for the preliminary design of floating wind turbines.

To address frequency drops in the power system during various frequency support intervals, this paper proposes a method for setting frequency support control parameters for wind power based on weighing frequency extreme points. First, we qualitatively analyze how frequency support control parameters of wind power impact the system’s dynamic frequency response. Next, we derive the time-domain expression for the system frequency dynamic response model, taking into account the contributions of synchronous generators and wind power during frequency support. We also investigate how different damping ratios affect the accuracy of the time-domain analytical formula. Subsequently, we analyze the power disturbances experienced by the system when wind turbines exit frequency support, developing a calculation method to evaluate the system’s frequency extreme points during the rotational speed recovery period of wind turbines. Based on this analysis, we establish an optimization model aimed at setting frequency support control parameters that minimize all frequency extreme points during the frequency support intervals. Finally, case studies validate that the proposed method can effectively set frequency support parameters according to varying wind power proportions, ensuring the frequency support capability of wind turbines without causing significant secondary frequency drop in the system due to excessive release of kinetic energy during frequency support.

Addressing the inefficiency of existing wind power data cleaning algorithms, a data cleaning method based on the raindrop erosion algorithm is proposed. This algorithm effectively removes outliers from wind power data by simulating the impact and erosion of raindrops on terrain. Experimental results indicate that the correlation coefficient between wind speed and power using the raindrop erosion algorithm reaches 0.977, demonstrating significant effectiveness in reducing data dispersion with a runtime of 2.1 seconds. Additionally, after cleaning the data using the raindrop erosion algorithm, the optimal wind power curve for a single wind turbine is fitted using a support vector regression （SVR） model with Bayesian optimization, from which the scheduled surplus electricity is calculated. Subsequently, simulation models of proton exchange membrane （PEM） electrolyzers and alkaline water （ALK） electrolyzers are used for comparative analysis of electricity consumption for hydrogen production utilizing surplus electricity. Simulation results show that the electricity consumption is 39.4 kW·h/kg for the PEM electrolyzer and 53.9 kW·h/kg for the ALK electrolyzer, suggesting that the PEM electrolyzer is more suitable for hydrogen production from surplus electricity.

This paper conducts a physical model experimental study on the penetration resistance of the multi-bucket foundation of an offshore wind power booster station during installation and construction and the leveling process after the inclination is generated. Two different penetration depths are set for the foundation structure： the foundation structure sinks into place by its own weight and the foundation structure sinks to 2/3 of the height of the bucket by suction. The leveling process under two different penetration depths with structure inclinations of 1°, 2°, and 3° was studied, and the applicability of the leveling strategy and the changing rules of the internal pressure and vertical displacement of the barrel foundation during the leveling process were analyzed. The test results show that accurate selection of suction value is the key to successful leveling. If the suction value is too small, the penetration rate of the bucket foundation will be seriously reduced and a soil plug will be formed. If the suction value is too large, seepage failure will easily occur, leading to failure of the installation process. In the actual leveling process, it is necessary to select appropriate leveling strategies according to different penetration depths and inclination states.

Regarding the issue of scour protection for the foundations of a certain offshore wind farm in Zhanjiang, a target wind turbine foundation was selected, and multibeam sweeping was carried out on its periphery.A three-layer composite device consisting of riprap, waste tires and bionic grass was designed in combination with the actual seabed conditions. Onshore experiments and offshore measurements were carried out for the device. The results of the experiments show that the device fully meets the requirements for the installation of scour proteltion derices. It can be used in actual field applications. The composite scour device combines the advantages of a single form of anti-scour device. It can be adjusted according to the seabed condition of the turbine foundation, which can effectively prevent secondary scour and provide better protection.

To improve the accuracy of the ultra-short term wind power prediction, an ultra-short-term wind power prediction model based on a Long Short-Term Memory （LSTM） neural network algorithm combined with the Transformer （TRF） model is proposed. The cosine annealing with warm restarts （CAWR） strategy is introduced to optimize the proposed prediction model to prevent the prediction model from converging to local optima. Firstly, the density-based spatial clustering of applications with noise （DBSCAN） and random forest （RF） methods are employed for anomaly detection and data imputation. Secondly, the CAWR-LSTM-TRF combined model is utilized to extract wind power features. Finally, the experiments for ultra-short-term wind power forecasting are conducted. The results of the study show that the symmetric mean absolute percentage error of the combined LSTM-TRF prediction model optimized by CAWR is reduced by an average of 0.46 percentage points compared to the LSTM-TRF model. Therefore, the proposed model achieves higher prediction accuracy and superior forecasting performance.

The valleys of the southern Tibetan plateau possess abundant wind energy resource classified as ‘rich’ and ‘very rich’, with favorable geographical conditions and power grid infrastructure supporting wind energy development. Accordingly, this paper investigates the formation mechanisms of wind energy resources in the wide-valley terrain of the southern Tibetan plateau. By conducting coupled mesoscale and computational fluid dynamics （CFD） numerical simulations and verifying the results with meteorological station and sodar （acoustic radar） wind measurements, the following conclusions are drawn： Valley wind circulation is the dominant factor in forming wind energy resources in the southern Tibetan valleys; Post-sunrise differential heating—slow warming of snow-capped peaks versus rapid heating of desert valley surfaces—produces pressure gradients that induce glacier winds, explaining frequent afternoon gales observed across the Tibetan Plateau; Meso-to-microscale coupled numerical simulation can effectively incorporate local atmospheric circulation background wind fields into CFD calculations, enhancing our understanding of wind resource characteristics in wind farms; In plateau valleys, wind direction shows diurnal variation, so it is best to identify the local atmospheric circulation background wind field characteristics through mesoscale numerical simulation before deploying wind measurement station.

Numerical weather prediction has an important impact on the accuracy of short-term wind power prediction models. In order to fully mine the deep mapping relationship between the information of numerical weather prediction and actual wind power,this paper proposes a short- term wind power forecasting model based on ResNet-UNet model with incorporation of multi-head attention mechanism. Firstly,considering the meteorological factors such as wind direction,wind speed, air pressure, temperature, relative humidity at different altitude levels,the features of numerical weather prediction information are extracted using the grid as a unit and then form the high-dimensional feature vector. Secondly, a wind power prediction model is constructed by fusing the UNet model and ResNet model, in which a multi-head attention mechanism is introduced to capture the spatial correlation characteristics of numerical weather prediction. Finally, the actual data of a wind farm in Zhejiang province is used to verify the model and compared with the prediction accuracy of the UNet, the ResNet, the LSTM, the BP models. The results indicate that the proposed method can effectively impove prediction accuracy.

4-ethylphenol and benzyl alcohol, the main depolymerization products of lignin, were used as reaction substrates to prepare high-density jet fuel precursor in an alkylation catalytic system coupled by acetic acid-choline chloride （Aa-ChCl） deep eutectic solvent and Amberlyst-15 ion exchange resin. The effects of reaction temperature, reaction time, molar ratio of the deep eutectic solvent components, catalyst amount and substrate molar ratio on alkylation were investigated. The results showed that a high conversion rates of benzyl alcohol （94.89%） and 4-ethylphenol （80.78%） were achieved at 170 ℃ over 4 hours with a molar ratio of benzyl alcohol to 4-ethylphenol of 1∶1 in an optimized system （Aa-ChCl at a molar ratio of 1：6 and an addition of 5% Amberlyst-15）. Meanwhile, the total selectivity and yield of 4-α-cinnamophenol （C_15-1） and o-isopropylphenol （C_15-2） could reach 74.58% and 60.24%, respectively.

Based on the concept of space sharing and cost allocation for the integration of wave energy converter and floating breakwaters, a three-pontoon floating breakwater-WEC integrated system is proposed. A numerical model is established using computational fluid dynamics methods to study the performance of the multi-pontoon floating breakwater, and the effects of PTO damping and draft on the energy capture and wave attenuation performance are investigated. The results show that the split-module design of the breakwater has an obvious effect on improving the energy acquisition of the integrated system with the same total volume, and the staggered configuration of the resonance frequency of the front and middle pontoons significantly broaden the effective frequency band. The design of the front pontoon with a triangular shape at the bottom can further improve the energy capture level. The reasonable selection of the PTO damping coefficient and the increase of the draft of the rear pontoon can improve the performance of the integrated system in terms of wave dissipation.

A heat transfer model is established for shallow coaxial ground heat exchanger （SCGHE） based on a composite G-function considering groundwater seepage, and then based on the proposed model and a model ignoring groundwater seepage, an estimation method is proposed to estimate groundwater seepage velocity （u） and ground thermal conductivity （λg） by matching thermal response test （TRT） data. The TRT is simulated under different conditions by a three-dimensional numerical model, which is used to validate the proposed model and method. After a long time, the established model is precise, which basically has higher precision than that of the model based on moving line source theory. The proposed estimation method is feasible： for the studied cases, the errors of estimated λg are basically within 1%, and the errors of estimated u are basically within 8%, but the errors of estimated u are larger for smaller u.

In order to extend the operating lifetime of large-scale centralized energy storage and realize full life-cycle utilization of batteries, we propose a centralized shared energy storage grouping coordination control strategy considering battery state of health（SOH）. First, the centralized shared energy storage operation mode for batteries with different SOH is built to break the energy sharing barrier, so that it can coordinate the power output of new energy stations and smooth out the net load fluctuation of the grid. Second, the dynamic charging and discharging safety margin is set based on the SOH of batteries, so that the group coordination control strategy for batteries with different SOH can be proposed to meet the challenges brought by the inconsistency of retired batteries in operation regulation. The simulation results show that the proposed strategy can meet the demand of grid and power plant regulation in multiple scenarios while improving the economic performance of centralized shared energy storage plants and extending the cycle life of retired batteries.

Comprehensively considering the optimal allocation scheme and operation strategy of lithium battery energy storage system, an optimal allocation model of lithium battery cloud-edge collaborative energy storage system is proposed, which takes into account economic performance and reliability. Firstly, a mathematical model is established with the objective functions of the cost of the lithium battery cloud-edge collaborative energy storage system, the benefit of energy release by the energy storage system and the reliability of the power grid. On this basis, considering the constraints of distribution network and energy storage system, lithium battery cloud-edge collaborative energy storage system is optimized to improve the absorption capacity of renewable energy such as photovoltaic power and improve the stability of the power system. Secondly, SPEA2-SA algorithm was used to solve the multi-objective model to find the optimal solution of lithium battery cloud-edge collaborative energy storage system under the multi-objective situation, overcome the power grid fluctuation problem, and obtain the optimal allocation and operation scheme of the energy storage system. Finally, using the above research model and algorithm, the IEEE-33 node model is used to conduct simulation experiments on the effectiveness of the lithium battery cloud-edge collaborative energy storage system. The simulation results of the example show that the optimal allocation scheme of the lithium battery cloud-edge collaborative energy storage system optimized by the SPEA2-SA algorithm can significantly reduce the operating cost and effectively improve the operating stability of the photovoltaic power grid, thus validating the superiority and effectiveness of the SPEA2-SA algorithm.

Phase change heat storage technology can solve the problem of solar energy intermittency and instability, and is a very promising energy storage technology. However, the efficiency of the heat storage unit is related to the phase change material and the heat transfer structure. In this paper, the heat storage capacity and phase change process of composite phase change materials in a double helical tube heat storage device were studied. The effects of different operating conditions （flow rate and inlet temperature） on the heat storage device in the melting stage were investigated by experiments. The numerical simulation method is used to explore the evolution of the temperature field inside the heat storage unit, and the structure of the heat storage unit is optimized to solve the problem of slow melting area in the heat storage unit. The results show that the change of flow rate has little effect on the melting and power of composite phase change materials. The inlet temperature has a significant effect on the melting of phase change materials. When the temperature increases from 80 ℃ to 90 ℃, the melting time of the composite phase change material is shortened by 30.23%. The maximum storage energy is 5.26 MJ. Experiments and simulations show that there is a slow melting zone. The effects of different coil diameters and compression ratios on the thermal performance of the heat storage unit are analyzed. The results show that when the coil diameter is 88 mm and the compression ratio is 21∶10∶21, the heat transfer of the heat storage unit is more uniform, the complete melting time is shortened by 71.3%, the average heat flux density is increased by 42.5%, and the slow melting zone is completely melted in 22578 s.

In order to study the effect of battery cyclic aging on the mechanical properties of lithium-ion batteries, the paper analyzes the effect of the cycle number on the failure of battery cells and battery component materials （failure displacement and failure load） and the distribution of Young’s modulus of battery component materials through the compression test of battery cells and components with different number of cycles. The results show that the failure displacement of the battery conforms to the normal distribution characteristics, and the cycle aging leads to the earlier failure of lithium-ion batteries under compression, in which the failure displacement of the battery decreases from 6.0 mm to 5.0 mm under the indentation condition, and that of the battery decreases from 6.8 mm to 6.4 mm under the flat-plate compression condition, and under the condition of 100 aging cycles, the Young’s modulus of the anode of the battery increases from 434.1 MPa to 725.2 MPa, and the Young’s modulus of the battery components also conforms to a normal distribution.

Sodium acetate trihydrate （SAT） has been identified as a potential energy storage material in the field of solar thermal collectors because of its advantageous latent heat of phase transition and suitable phase transition temperature. However, SAT is subject to defects such as subcooling and phase separation during the phase transition. In the present study, a range of thickeners and nano-nucleating agents were utilised in the modification of SAT, with the objective of producing composite SAT with optimal performance. It was observed that the composite SAT not only ameliorated the inherent limitations of SAT, but also exhibited favourable thermal properties. The study proceeded to explore the mechanism of phase separation and subcooling of SAT, and the associated solutions. A comparative analysis was conducted on the inhibition of phase separation and subcooling of SAT with thickening and nucleating agents. Finally, the potential applications of the composite SAT in solar energy post-modification were examined. Following the modification of composite SAT, the subsequent direction of application in the field of solar energy is discussed.

Fault diagnosis techniques play a crucial role in the normal operation of proton exchange membrane fuel cells. In this paper, we propose a diagnostic technique based on equivalent circuit and KOA-CNN framework, which obtains the PEMFC impedance spectrum information by using electrochemical impedance spectroscopy （EIS） and uses the equivalent circuit for parameter identification, and uses the fitted circuit parameters as the training data for the diagnostic algorithm, and extracts the fault features by using a convolutional neural network, which can significantly improve the accuracy of PEMFC fault diagnosis. The Kepler optimization algorithm convergence speed, strong global search ability, and few parameters to optimize the hyperparameters of the convolutional neural network, to get an optimal convolutional neural network parameters, which can significantly improve the accuracy of fuel cell fault diagnosis. It is verified that the accuracy of this method reaches 99.75% in the fault diagnosis of water flooding, membrane drying and oxygen starvation.

In order to ensure the safe and economic operation of hydrogen industrial parks, a co-optimized scheduling model of electric-heat-cool-gas-hydrogen multi-energy cogeneration system that takes into account the uncertainty of wind and photovoltaic power output and the demand response of hydrogen load is established. Firstly, based on the Green Hydrogen Industrial Park, a typical park-level electricity-heat-cooling-gas-hydrogen multi-energy cogeneration system architecture is proposed, and a model including electric-hydrogen coupling as well as electric-heat-cooling triple-supply system is established from the perspectives of the system’s source-grid-load-storage. Secondly, a multi-objective function is established, and the NSGA-III algorithm combined with Pareto frontier optimisation is used for the co-optimised scheduling of the MECS in hydrogen industrial parks with the objectives of achieving the minimum overall operating cost, the maximum energy utilisation rate, and the minimum carbon emissions in the park. Finally, the uncertainty of the wind and photovoltaic output is simulated by the Latin hypercube method and K-means clustering algorithm, and the optimal scheduling results in different scenarios are compared to verify the low-carbon and economic performance of the proposed model.

When an asymmetrical fault occurs in the grid, in order to support the grid, the hydrogen generation power supply needs to use the positive-negative-sequence separation control method to provide positive-sequence reactive power to the grid and absorb the negative-sequence reactive power of the grid, and the stability of the system during the fault needs to be considered at this time. In this paper, the stability factors affecting the hydrogen production power supply during asymmetric faults are investigated. Firstly, the topology and control method are briefly introduced, and then a quasi-steady state model of the system is established, and the stability of the hydrogen power supply is analyzed from three factors, namely, the positive and negative sequence components of the grid voltage, the magnitude of the grid-side impedance, and the ratio coefficients of the positive and negative sequence currents to the reactive power, respectively. Finally, the feasibility of the control method is illustrated through simulation and the reasonableness of the theoretical analysis is verified.

This paper addresses the challenges of background interference, computational redundancy, and the difficulty in balancing model accuracy with processing speed in existing photovoltaic module electroluminescence （EL） defect detection methods. We propose an enhanced YOLOv8-based defect detection method, named YOLOv8-LSB, to tackle these issues. Firstly, we introduce the SCConv convolution module into the backbone network, reducing spatial redundancy while improving the extraction of small target features. Secondly, we incorporate the LSK attention mechanism in the neck to effectively mitigate background interference. Additionally, we use the BiFPN structure to enhance multi-scale feature fusion, enabling the model to capture features from various perspectives more effectively. Lastly, we employ Inner-CIoU as the bounding box regression loss function, which improves both regression accuracy and convergence speed. Experimental results show that YOLOv8-LSB achieves 91.2% mAP@0.5 and 170.2 FPS. Compared to the baseline model YOLOv8n, the proposed method improves average accuracy by 2.6 percentage points and FPS by 4.8 frames per second, making it a more real-time and accurate solution for photovoltaic EL defect detection.

A high gain DC-DC converter with an enhanced coupled inductor voltage multiplier cell is presented. This converter is particularly suitable for elevating the relatively low voltage of a photovoltaic panel to the voltage level necessary for a high voltage DC bus. The proposed converter comprises a Sepic network in the front stage and an improved coupled inductor voltage multiplier cell in the back stage. It exhibits several notable advantages, such as high gain, low ripple, low switching voltage stress, light weight and high efficiency. By modifying the connection between the boost unit and the coupled inductor, the voltage of the energy storage capacitor can be further augmented using improved coupled inductor voltage multiplier cell. In comparison with converters described in other literature, the proposed converter achieves higher voltage gain at low and medium duty cycles while employing the same or a similar number of components. The low input current ripple is beneficial for prolonging the operational lifespan of both the PV panel and the converter. The clamp circuit effectively suppresses the voltage spikes across the switchirg device, thereby enhancing the operating conditions of the switching device and improving the electromagnetic environment of the converter. Firstly, a comprehensive analysis of the working principle and steady-state of the proposed converter was conducted. Then a comprehensive comparison was made with other recent converters to illustrate the unique characteristics and advantages of the proposed converter. Finally, a 200 W prototype was designed in the laboratory, and the measured waveforms successfully validated the accuracy of the theoretical analysis of the proposed converter.

A multi-factor photovoltaic power prediction method based on a time-frequency domain decomposition enhancement fusion Transformer is proposed to address the performance limitations caused by insufficient mining of the trend and randomness of the photovoltaic power change curve in existing photovoltaic power prediction methods. Firstly, an encoder-decoder framework is constructed, in which historical and current PV power data are decomposed into periodic and trend components through time-domain and frequency-domain enhancement modules. The semantic relationships between current and historical data are further extracted using time-domain and frequency-domain enhanced attention mechanisms. Subsequently, a stochastic layer is employed to capture the stochastic component of the current data. Then, a multi-level adaptive fusion strategy integrates the periodic, trend, and stochastic components of PV power data for forecasting. In addition, multiple factors such as solar irradiance, humidity, wind speed, and temperature are incorporated using an independent multi-channel feature extraction approach to reduce data redundancy and further improve model performance. Experiments on PVGD-1 and DKASC-Sanyo datasets show that the proposed method outperforms Informer, Autoformer, and Fedformer.

This article investigates active distribution networks with distributed photovoltaic （PV） integration. Analyzing the specific control strategies of distributed PV units, the study characterizes the fault current magnitude and phase characteristics under varying PV output conditions. On this basis, it analyzes the operating range of the phase difference of the positive-sequence fault component of the current at both ends of the faulted line and concludes that the traditional current phase comparison type pilot protection may fail when the photovoltaic output is small. Therefore, this paper improves the criterion by combining the amplitude ratio and phase angle difference of the positive-sequence fault-component current and proposes an active distribution network pilot protection scheme based on comprehensive phase angle difference. This solution only needs to collect current information and does not need to install voltage transformers, making it highly economical. The simulation results show that this solution is not affected by variations in PV output or fault types.

To improve the accuracy of fault diagnosis in photovoltaic arrays, a strategy is proposed to optimize the Long Short-Term Memory network （LSTM） using the Cauchy Gaussian Mutation Artificial Rabbit Algorithm （CGARO） to achieve efficient diagnosis of multiple types of composite faults in photovoltaic arrays. Firstly, in order to the problem of getting trapped in local optima in the artificial rabbit optimization （ARO）, a Cauchy Gaussian mutation artificial rabbit algorithm is proposed. Comparative analysis was conducted between CGARO and ARO, Sparrow Search Algorithm （SSA）, and Grey Wolf Optimization Algorithm（GWO） to verify the effectiveness of CGARO algorithm. Then, the CGARO algorithm was optimized to optimize the parameters and learning rate of LSTM, and a CGARO-LSTM photovoltaic array fault diagnosis model was established. Based on four types of single faults and three types of composite faults, the CGARO-LSTM model was compared with LSTM, SSA-LSTM, GWO-LSTM, and ARO-LSTM. The results showed that the CGARO-LSTM model had better performance, with an accuracy of 97.75%, significantly improving the accuracy of photovoltaic array fault diagnosis.

To improve the accuracy and stability of ultra-short-term photovoltaic （PV） power prediction, the BiLSTM model based on dual-layer decomposition and an improved multi-objective coati optimization algorithm （AMOCOA） is proposed. The dual-layer decomposition combines improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and variational mode decomposition （VMD） to fully extract information from high-frequency signals. AMOCOA enhances algorithm convergence and diversity by introducing an adaptive region search strategy and polynomial mutation operator. First, ICEEMDAN is used to decompose the historical PV power series into multiple components, and the high-frequency components are further decomposed using VMD to extract periodic components. Then, AMOCOA is applied to optimize the BiLSTM parameters, building the optimal AMOCOA-BiLSTM model for each subseries. Finally, the subseries are reconstructed to obtain the final prediction results. The experimental results show that, under cloudy weather scenarios, compared to the BiLSTM, the root-mean-square error of the proposed model decreased by 51.56%, the average absolute error decreased by 68.75%, and the stability index decreased by 50.74%, showing better prediction accuracy and stability.

To address the challenges to grid security posed by the randomness, intermittency, and fluctuations of photovoltaic （PV） power in PV power generation planning, an innovative deep learning model—the DRSTCG-GPR model based on fused spatiotemporal feature extraction—is proposed. This model fuses spatiotemporal features, introduces soft thresholding and attention mechanisms into the residual module, automatically optimizes hyperparameters via a Bayesian algorithm, and quantifies the uncertainty of predictions using Gaussian process regression, ultimately achieving high-precision interval prediction for short-term photovoltaic （PV） power.. Experimental comparisons show that, compared to traditional CNN, GRU, and CGRU models, this model exhibits significant advantages in point prediction accuracy （R² improvement of 2.20%）, interval prediction coverage performance （I_CP improvement of 2.10%）, and probabilistic prediction reliability.

To enhance the accuracy of photovoltaic power forecasting, this study proposes an ultra-short-term photovoltaic power prediction method incorporating similar-day selection and an Error Correction Model（ECM）. First, data is decomposed and reconstructed into high-frequency and low-frequency components using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （ICEEMDAN） method. which feeds into a feature extraction and prediction model combining CrossAttention-based Bidirectional Gated Recurrent Units（BiGRU） and eXtreme Gradient Boosting（XGBoost）. Second, the comprehensive similarity factor between the forecast day and historical days is calculated using grey correlation analysis. Meteorologically similar days for the forecast day are selected as input to the BiGRU-based similar-day information enhancement module. A residual forecast sequence is constructed based on the initial forecast sequence to build an error correction model using BiGRU. Finally, the prediction results from the ICEEMDAN-BiGRU-XGBoost-CrossAttention model, integrated with similar-day information, are combined with the prediction errors from the error correction model to derive the final intraday PV power prediction. Using actual meteorological and PV power generation data from photovoltaic stations, comparisons with different PV power generation models validate that the proposed method enhances the accuracy of intraday ultra-short-term PV power prediction and demonstrates practical application value.

This paper presents a study on the horizontal bearing performance of open-ended gravity-based foundations（OGBFs）for offshore floating photovoltaic （FPV）systems. In this study, a combination of numerical analysis and centrifuge model tests was conducted used to investigate the horizontal bearing behaviors of OGBFs in clay. The horizontal ultimate bearing failure mechanisms of OGBFs were identified, and the effects of embedment ratios, hole ratio, and various contact conditions on the bearing characteristics of OGBFs were examined. The results indicate that the horizontal bearing failure mechanisms of OGBFs is dominated by sliding failure. Under different hole ratios, embedment ratios, and contact conditions, failure mechanisms can be categorized into global sliding failure and partial sliding failure. Based on the analysis of failure mechanisms, an optimal hole ratio and a calculation method for the horizontal ultimate bearing capacity applicable to OGBFs are proposed.

Addressing the challenge of accurately characterizing uncertainties in short-term photovoltaic （PV） power forecasting, this paper proposes a short-term PV probabilistic prediction method based on an improved CNN-Autoformer network. Firstly, convolutional neural network is used to extract and establish a mapping relationship between high-dimensional meteorological features and PV output based on numerical weather prediction. Secondly, a self-organizing map （SOM） neural network is employed to reduce and categorize weather types as discrete features of the daily PV sequence. Based on this, a temporal Autoformer network is constructed to deeply decompose the PV sequence, incorporating an autocorrelation mechanism to capture the periodicity and trend features. Finally, combining maximum likelihood estimation with gradient optimization, the parameters of the PV output probabilistic distribution are derived through a probability density estimation layer. Simulation results demonstrate that the proposed method can effectively improve the performance of PV probabilistic prediction compared to the comparative methods.

In order to address the problems of complex defect types and low efficiency of manual detection in solar cell electroluminescence images, a defect detection method based on Mamba and convolutional neural networks is proposed. The method overcomes the limitations of a single architecture in complex scenarios by fusing the local feature extraction capability of convolutional neural networks with the global information capture advantage of Mamba structures. The Mamba structure introduces a lightweight attention mechanism that dynamically adjusts the weights of features at different levels to enhance global information perception capability. Combined with convolutional neural networks, the model can both extract local detailed features and efficiently fuse global contextual information, thus significantly improving the accuracy and performance of defect detection. After a large amount of experiments, it is proved that the proposed method has the advantages of a low number of model’s parameters, high detection accuracy and strong robustness, which significantly improves the defect detection performance of solar cells.

The characteristics of photovoltaic power generation and residential electricity consumption in a rural residential building installedwith photovoltaics in Beijing were studied, and the adaptation of photovoltaic power storage capacity with the objective of improving the contribution rate and self-consumption rate of photovoltaic power was analyzed. The results indicate that the electricity consumption characteristics of rural residential buildings are not only seasonal but also relateds to working days and non-working days, which means that the electricity consumption characteristics are closely related to residents’production and living behavior patterns. When the air source heat pump（ASHP） is used for heating, the proportion of heating electricity consumption is relatively high, reaching 43.2% of the total electricity consumption. The lack of coordination between photovoltaic power generation and residential electricity consumption in terms of time results in low contribution rate and self-consumption rate of photovoltaic power. The annual contribution rate of photovoltaic power, when the centralized heating and ASHP heating are used, are only 52.8% and 36.6%, respectively, and the self-consumption rate is only 33.2% and 38.5%, respectively. When electricity storage is used, the self-consumption rate of photovoltaic power has been increased to about 62.4% and 64.9% respectively, and the photovoltaic contribution rate has been increased to about 99.5% and 61.7% respectively. When the photovoltaic area is fixed, increasing the electricity storage capacity of the photovoltaic system is no longer a reasonable and economical solution when the electricity storage is beyond a specific value.

In order to explore the effect of waves on the power generation performance of floating photovoltaic modules at sea, photovoltaic modules with rated power of 595 W were selected as the research object. Based on theoretical analysis and physical model experimentals, the effects of maximum swing angle of 5°-30°, swing period of 5~10 s and irradiance of 200~900 W/m² on the power generation performance ratio were compared and analyzed. The results show that with the maximum swing angle of the photovoltaic module increasing from 5 to 30, the ratio of the power generation of the swing photovoltaic module to that of the fixed module with a 0° tilt angle, defined as the power generation performance ratio η_A, decreased by 5.16 percentage points. The ratio of the power generation of the swing photovoltaic module to that of the fixed module with an 18° tilt angle, referred to as the power generation performance ratio η_B, decreased by 5.30 percentage points. Furthermore, the decline rate gradually accelerated with an increase in the maximum swing angle. Compared with the influence of the maximum swing angle on the maximum swing angle on the performance ratio, the effect of swing period variation is relatively insignificant,and the power generation performance ratio of the module increases with the extension of the swing period. The power generation performance ratio of the module decreases with increasing irradiance. When the irradiance increases from 200 W/m² to 900 W/m², η_A decreases by 4.61 percentage points and η_B decreases by 5.48 percentage points.

This study pioneers the investigation into the applicability of conventional glare indices for assessing daylight glare from inhomogeneous STPV windows. A glare perception survey was designed, involving 38 participants to capture their subjective glare perception under conditions with cell strip widths of 4 mm and cell coverage ratios of 40% and 60%. Simultaneously, experiments were conducted using cameras and illuminance meters to measure key lighting environment parameters, such as vertical eye illuminance and DGP. The correlation between image-based glare indices and the survey results was analyzed to assess the relevance of existing indices and determine the influence of cell coverage ratios on the applicability of DGP. The findings reveal that DGP demonstrates the highest coefficient of determination with perceived glare, indicating it is the most relevant index. However, DGP tends to underestimate glare caused by inhomogeneous STPV windows. Furthermore, when the cell coverage ratio increased from 40% to 60%, the accuracy of glare evaluation using DGP decreased by 6.79%.

Aiming at the increasing number of off-grid problems caused by low-voltage ride-through of distributed PV, this paper proposes a static clustering and equivalent modeling method of large-scale distributed PV in distribution networks for low-voltage ride-through scenarios. Firstly, we analyze the low-voltage ride-through switching characteristics of distributed PV, and put forward the low-penetration off-grid risk indices considering the electrical distance and installed capacity of each distributed PV; secondly, we take the low-penetration off-grid risk indices as the basis of clustering, and divide the distributed PV clusters based on the distance cost function and the K-means clustering algorithm; lastly, according to the principle of aggregation of the distributed PV parameters, we adopt the REI lossless network equivalence and the Ward network equivalence to lossless the distribution network. and conduct simulation in PSASP for case comparison, the results show that： large-scale distributed PV static clustering and equivalent modeling method for distribution network oriented to the low-voltage ride-through scenario can significantly improve the transient simulation accuracy.

To address the issue of missed detections of small hotspots caused by the loss of detailed features in photovoltaic hotspot defect detection, this paper proposes an improved method for cross-level local feature fusion based on YOLOv8. The method introduces the BiFormer concept into the YOLOv8 backbone to enhance the model’s ability to extract detailed information. It replaces traditional upsampling methods with a Dysample dynamic upsampling module, better preserving the details of high-resolution feature maps. Additionally, an FCLAHead detection head is designed for cross-level local feature fusion, establishing feature associations across different feature map levels to achieve information complementarity. This fusion operation enhances the feature representation of small hotspots, improving the model’s detection capability for small hotspots across various scenarios. Compared to the base model YOLOv8n, the improved model increases mean average precision （mAP） from 86.3% to 90%, with precision and recall improving by 1.1 and 3.1 percentage points, respectively. The model’s parameter count is only 3.08×10⁶, with a slight increase in computation, making it suitable for applications requiring high accuracy in hardware-limited environments.

In order to solve the problems of low utilization rate of distributed photovoltaic grid-connected inverters and high cost of power quality control at the end of the distribution network, a control method for a three-phase four-wire multi-functional photovoltaic grid-connected inverter was proposed. By analyzing the urgency of the demand for photovoltaic power generation, reactive power compensation, harmonic control and three-phase imbalance compensation at the end of the distribution network, the priority of photovoltaic power generation function and various power quality control functions is established, and the target current distribution method of multi-functional photovoltaic grid-connected inverter is proposed, so that the photovoltaic grid-connected inverter can output corresponding currents according to the priority of the power quality compensation for the distribution network. The simulation results show that this method can not only ensure the effective utilization of photovoltaic power, but also make full use of the residual capacity of the inverter to control power quality, improve the utilization efficiency of photovoltaic grid-connected inverters and reduce the cost of power quality control in distribution network.

Using the Quokka3 simulation software, we systematically analyze the impact of boron-doped polysilicon （p-poly） passivation and contact performance, as well as pattern design, on the performance of interdigitated back contact （IBC） solar cells. On this basis, we further discuss in detail the effects of the tunnel oxide interface recombination rate, the doping concentration of the polysilicon layer, and the diffusion region formed in the silicon substrate on the passivation performance of p-poly, and we outline the essential conditions required to achieve excellent passivation performance with p-poly passivating contacts.

This investigation reveals three failure mechanisms of glass-backsheet tunnel oxide passivating contact （TOPCon） modules under damp heat conditions： 1） corrosion of the fine grid lines; 2） corrosion around the interconnected areas of the busbars and solder ribbons; and 3） failure of the solder ribbons, main busbars and cell interconnection regions. Damp heat failure is caused by electrochemical corrosion due to Ag-Al paste, moisture, acetic acid from solder fluxes and encapsulation materials （EPE/EVA）, and contaminants containing Na+ and Cl-, which leads to weakening of the interconnections within the component, and ultimately to failure. Through the comparative study of different welding solutions, the use of Integrated Film Covering welding technology can effectively block the penetration of corrosive media by virtue of the innovative encapsulation process, showing excellent resistance to damp heat, and providing the solution to enhance the damp-heat-resistant performance of the single-glass TOPCon modules.

To obtain SiO₂ superhydrophobic thin films suitable for photovoltaic module surfaces, an improved Stober method was used to prepare MTES/TEOS hybrid sol and different amounts of HMDS were added for hydrophobic modification. Finally, SiO₂ thin films were prepared by the impregnation method. Morphology and structure, optical properties, surface properties, and self-cleaning performance tests were conducted on the prepared thin film, and artificial accelerated aging tests were conducted on the films to obtain changes in their properties. The results showed that when HMDS/SiO₂=3, the transmittance of the film was 92.30%, the water contact angle could reach 165.28°, the surface energy was only 1.06 mJ/m², and the self-cleaning effect was excellent. The degree of change in transmittance and water contact angle caused by 1500 hours of photoaging and high-temperature aging was less than 5%. The comprehensive performance of the thin films meets the application requirements of photovoltaic module surfaces and has broad prospects in the field of photovoltaics.

The P-U curve of PV array shows multiple peak characteristics under time-varying conditions, which makes the traditional MPPT control algorithm easily fall into the local optimal solution, and the optimization speed is slow, which reduces the overall power generation efficiency of PV array. A two-layer MPPT control strategy based on improved immune algorithm and perturbation observation method is proposed. Firstly, according to the characteristics of multiple peaks of P-U curve under time-varying conditions, an improved immune optimization algorithm based on MPPT comprehensive performance index is proposed, and the MPP points iterated in the upper layer are imported to the lower layer for disturbance tracking. Simulation results show that the optimization time of the photovoltaic MPPT control algorithm based on improved immune and disturbance observation method is 20% of that of similar algorithms, the steady-state tracking accuracy is 0.26-0.84 percentage points higher than that of similar algorithms, and the outlier offset is 38.36%-60.89% lower than that of similar algorithms. It can effectively improve the maximum power tracking efficiency and accuracy of photovoltaic arrays under time-varying working conditions, and has the characteristics of small steady-state fluctuation.