To alleviate the limitation on distributed photovoltaic integration imposed by the inflexibility of the spatiotemporal distribution of new energy and load, this paper proposes a co-planning strategy for energy storage and intelligent soft-switching to enhance the capability of power distribution networks for distributed PV power. Firstly, to evaluate the collaborative benefits of multi-port soft open point （MSOP） and energy storage system （ESS） in spatiotemporal flexibility, an evaluation metric integrating branch current time-uniformity level and network loss sensitivity is proposed. Subsequently, a two-level stochastic optimization model comprising planning and operation is established based on this collaborative benefit metric. By embedding the MSOP-ES model, this framework is capable of considering diverse integration forms of MSOP and ESS. Finally, the two-level optimization model is efficiently solved using the Benders decomposition algorithm. Case studies on the IEEE 33-node distribution network and a real-world distribution network demonstrate that the proposed method effectively enhances PV hosting capacity while improving voltage stability and flexible transfer capability by planning ESS and MSOP integration.

To address the insufficient accuracy and poor adaptability to parameter variations of Extended Kalman Filter （EKF） in lithium-ion battery state estimation, this paper proposes a method utilizing EKF for online battery parameter identification and updating, while integrating Sliding Mode Observer （SMO） and Unscented Kalman Filter （UKF） for lithium battery state estimation （SUKF）. The method establishes a dual-layer temporal scale collaborative estimation architecture, employing EKF for online battery parameter identification and State of Health （SOH） estimation, while combining SMO’s robustness with UKF’s nonlinear processing capabilities to achieve high-precision State of Charge （SOC） estimation. Verification through three representative testing environments—highway fuel economy test conditions, New European driving cycle, and urban dynamometer driving schedule—demonstrates that the fusion algorithm achieves average SOC estimation errors of 0.13%, 0.25%, and 0.14% respectively, with maximum errors constrained within 0.46%, representing over 85% improvement in accuracy compared to traditional EKF algorithms. SOH estimation under varying complexity conditions exhibits average errors ranging from 0.022% to 0.16%, with maximum errors not exceeding 0.53%. Under 5% noise disturbance conditions, the algorithm demonstrates robust estimation accuracy and convergence performance. As operational complexity increases, the performance advantages of the fusion algorithm become more pronounced, significantly enhancing the accuracy and robustness of battery state estimation, thus providing an effective solution for power battery management systems.

A novel two-phase interleaved soft-switching bidirectional DC-DC converter is proposed. The soft-switching auxiliary circuit of the converter is composed of an inductor and a capacitor in series. The auxiliary circuit can not only realize the sharing between two phases, but also work only once in a cycle. This paper presents the topological structure and soft-switching operating principles of the converter; analyzes the key waveforms under different duty cycles and the conditions for achieving soft-switching; proposes a full range soft-switching modulation method based on peak-valley value detection; and performs topology deduction. Finally, a 250 W experimental prototype is built for verification. The experimental results show that all the switches of the converter can achieve zero-voltage switching, and the peak efficiency can reach 99.38%.

In this review, the preparation strategy and synthetic methods of Fe-N-C catalysts in recent years including template method, impregnation method and in situ capture method etc are systematicauy summarized. The current developments of pyrolytic Fe-N-C catalysts derived from metal-organic frameworks, organic porous polymers and biomass are emphatically outlined. The structure of active sites and reaction mechanism in Fe-N-C catalysts are also discussed. Then, the challenges and future development directions of Fe-N-C catalysts for ORR are discussed and summarized.

Aiming at the problem of nonlinear decline of battery capacity, a RUL prediction method for lithium battery based on multi-channel feature fusion optimization VMD-SCNN-LSTM is proposed. Firstly, the health factors are selected according to the Pearson correlation coefficient, and the time-series characteristic curves of voltage, current and temperature during the charging and discharging process of Li-ion battery are extracted. Secondly, the battery capacity degradation curves are decomposed using variational modal decomposition （VMD）. Finally, the HI and the frequency-domain features of the capacity decomposed by VMD are used as the multi-channel parallel inputs to the convolutional neural network （CNN）, and the original capacity was used as the input to the long-short-term memory network （LSTM）, and then the two extracted features are fused to carry out RUL prediction. A triple cross-validation training method and dropout technique are also introduced to avoid the overfitting problem. The optimal hybrid model proposed in this study has an RMSE of no more than 3.1% and a MAPE of no more than 1.3%.

Shared energy storage is a new type of energy storage business model, which can freely and flexibly choose energy storage capacity according to different scenarios. In this paper, based on shared energy storage power station, considering the uncertainty of electric rehicles（EV） charging timing, a two-layer optimization model of capacity optimization configuration and system optimization operation of energy storage power station is established. Firstly, a prediction of the spatiotemporal distribution of EV charging is proposed and its power is calculated, and then a two-layer optimization model is established considering the optimal configuration of operation and capacity. The two-layer optimization model is transformed into a single-layer nonlinear optimization problem by using Karush-Kuhn-Tucher（KKT） condition, and finally the nonlinear problem is transformed into a linear problem by using large M method. The calculation examples show that, considering the charging load of electric vehicles, the shared energy storage mode can take advantage of the complementarity of different micro-grids, reduce the system operation cost, improve the profitability of shared energy storage power stations, and achieve a win-win situation between users and power stations.

A system calculation program for the full cycle process of energy storage and release was written by Python for a salt cavern compressed air energy storage （CAES） project. The effects of the highest storage gas pressure, expansion throttle pressure, compressor discharge temperature, and reheat stages on the characteristics of the salt cavern CAES were studied. The results show that there is an optimal highest storage gas pressure and expansion throttle pressure for CAES with the same reheat stage. At this time, the total compression power consumption is the smallest and the conversion efficiency is highest. The system conversion efficiency increases with the increase of the compressor discharge temperature for CAES with the same reheat stage, but the change trend slows down. The one-stage reheat CAES with compressor discharge temperature of 400 ℃ has the smallest compressor work consumption, the lowest gas consumption rate, and the highest conversion efficiency. The highest conversion efficiency is 71.43%, which is 6.48% higher than that of the three-stage reheat type.

In order to deal with the fluctuation of wind farm output, ensure the stable operation of power system, and take into account the characteristics of various energy storage devices, a stabilization-frequency modulation strategy based on flywheel-electrochemical hybrid energy storage is proposed. Firstly, according to the typical daily wind power data, the fully adaptive noise ensemble empirical mode decomposition is used to decompose it to obtain the direct grid-connected component of wind power and the hybrid energy storage power task. Secondly, considering the multi-type constraints of the energy storage system, taking the maximum annual overall net income of the wind storage system as the objective function, a capacity optimization configuration model of flywheel-electrochemical hybrid energy storage considering wind power stabilization and frequency modulation is established. Finally, the optimal capacity configuration scheme of hybrid energy storage is obtained by iterative solution of adaptive particle swarm optimization algorithm. The results show that compared with the traditional hybrid energy storage system that only stabilizes the fluctuations of wind power, although the investment cost of this hybrid energy storage system has increased, it will also generate higher frequency regulation benefits, which can effectively give full play to the advantages of energy storage stabilization-frequency regulation, and the average annual overall net income of the system is improved, which proves that the strategy proposed in this paper has better economy and feasibility.

In response to the problems of poor interpretability and strong dependence on data for battery state of health（SOH） estimation of lithium-ion batteries based on machine learning, this paper proposes a SOH estimation method for lithium batteries with interpretability. First of all, this work analyzes that as batteries age, the distance between the charging voltage and the first charging voltage exhibits a good trend, and proposes a distance metric based on the concept of Shapelets that can capture the degradation trend of the battery, and further determines the range of the Shapelets candidate set through correlation analysis to improve the efficiency of the feature extraction. The selection of Shapelets is carried out in combination with the BP model optimized by the subtraction-average-based optimizer (SABO) algorithm. Finally, the Shapelets-based SABO-BP model is designed to realize the effective estimation of battery SOH. The proposed method is validated on the Stanford-MIT dataset, and batteries with different charging strategies are selected for test, the MAE of the battery SOH estimation are all maintained within 0.5%, and can reach as low as 0.19%; the RMSE are also all maintained within 0.6%, and can reach as low as 0.26%; and the R² stays above 0.98 and reaches up to 0.995. The experimental results show that the proposed method is able to accurately predict the SOH of lithium batteries with limited data, which confirms the generalization and practical value of the proposed algorithm.

To lower the minimum operating temperature and broaden the working temperature range of mixed molten salts, a quaternary nitrate mixture of KNO₃-NaNO₂-KNO₂-Ca（NO₃）₂ was studied. Using DSC and TG analysis methods, a new low-melting-point quaternary nitrate was identified. The thermal stability of the new quaternary salt was investigated through isothermal weight loss measurements at various temperatures. Furthermore, its thermal properties such as specific heat, density, and thermal conductivity were tested and analyzed. The research findings indicate that the new quaternary salt has a melting point of 94.8 ℃, a decomposition temperature of 622.3 ℃, and an energy storage density of 696.43 kJ/kg. After maintaining a constant temperature of 550 ℃ for 48 hours, the sample exhibits a low weight loss rate of only 2.71%, demonstrating excellent thermal stability.

This study extracts health characteristics of lithium batteries from charge and discharge data with incremental capacity analysis to predict the capacity of lithium-ion batteries. Meanwhile, a method for estimating the state of health of lithium-ion batteries is proposed based on the improved Harris hawk optimization algorithm （IHHO） and support vector regression （SVR）. To address the problem that the Harris hawk optimization algorithm （HHO） tends to stagnate at local optima, the IHHO algorithm introduces a pooling mechanism and a migrating search strategy based on the HHO algorithm, while modifying the position update equation to prevent premature convergence. Based on comparisons using the CEC2017 test suite, the optimization capabilities of IHHO and HHO algorithms are evaluated. This shows that the IHHO algorithm has a higher convergence speed and optimization accuracy. For some test functions, the optimization accuracy of the IHHO algorithm is improved by more than three orders of magnitude, effectively avoiding premature convergence. In addition, the comparative experiments are conducted on the NASA lithium-ion battery dataset to optimize the standard SVR with IHHO and HHO algorithms. The results show that IHHO-SVR significantly improves the prediction accuracy of the state of health, reducing the root mean square error by more than 40% compared to the HHO-SVR. In addition, comparisons with other literature models indicate the superior performance of the IHHO-SVR model. For certain prediction results, the root mean square error of IHHO-SVR is reduced by at least 15%.

The aerodynamic performance of a two-stage air compressor under variable altitude is severely degraded and cannot meet the dynamic demands of high-power-density fuel cells. In order to broaden the working limit of the two-stage air compressor, numerical simulation was used to study the aerodynamic performance under typical working conditions. The results show that： with the decrease of inlet pressure, the pressure ratio of the two-stage air compressor decreases by 32.73%, and the isentropic efficiency decreases by 34.60%; with the increase of inlet temperature, the pressure ratio of the two-stage air compressor increases by 23.27%, and the isentropic efficiency decreases by 12.76%; with the increase of rotational speed, the pressure ratio of the two-stage compressor increases by 145%, and the isentropic efficiency increases first and then decreases, reaching a maximum value of 66.83% near the design speed.

On the basis of the power, efficiency and hydrogen consumption curves of fuel cell, and its operating point efficiency, combining the optimal working area of battery SOC, focuses on a hybrid vehicle energy system powered by fuel cell and battery, builds up a model based on the Simulink platform, and proposes a new energy management and control strategy. The simulation result shows that the proposed strategy can effectively distribute the energy of the fuel cell and battery, and ensure the high-efficiency power output of fuel cell and the optimal working area of battery SOC.

This article introduces green hydrogen and its manufacturing technologies, with a focus on solar thermochemical hydrogen (STCH) production, elaborating on the basic principles, research status, key characteristics, and the possibility of combination with concentrating solar power （CSP）. The article also discusses the future development directions of this technology. The fundamental principles and key parameters of various STCH cycles are summarized, including Cl-based cycles （e.g., Cu-Cl, Mg-Cl）, S-based cycles （e.g., HyS, S-I）, and other cycles such as metal oxide cycles. The article highlights that, despite challenges in system design and economic viability, STCH offers significant environmental advantages and warrants continued in-depth investigation. Finally, the challenges confronting STCH technology are addressed, and promising future research directions are outlined.

In order to guarantee the stable and efficient operation of the new energy electrolysis hydrogen production system, a hydrogen production system efficiency model was first constructed, encompassing the PEM electrolyser, hydrogen production power supply, compression, drying, circulating cooling, and other auxiliary links. A power-adaptive control strategy based on the optimal efficiency of the hydrogen production system was proposed based on this model. The strategy takes the optimal efficiency of the hydrogen production system as the goal and combines it with the power control of the hydrogen production power supply. Matlab/Simulink simulations was carried out in order to facilitate a comparison of the efficiency curves of the hydrogen production system under the chain distribution strategy, the average distribution strategy and the proposed optimization strategy. The results demonstrate that the strategy proposed in this paper serves to enhance the application range of the hydrogen production system for high-efficiency work and to improve the utilization of new energy.

To improve the aerodynamic performance of two-stage air compressors, a CFD model was developed and then verified via the experiments carried out on a fuel cell air compressor test bench. Based on this model, the distribution and magnitude of exergy loss for each componentwere derived. The results indicates that the exergy loss at the high-pressure end is 58.06 J higher than the low-pressure end. The exergy loss of impeller accounts for 54% of the total exergy loss at the high-pressure end. The loss factor analysis reveals that irreversible loss due to turbulence is the primary contribution.

To enhance the safety and energy conversion efficiency of renewable energy-coupled hydrogen production systems, this study proposes an optimization control method for dual-channel hydrogen production systems in multi-source scenarios based on Petri nets. Considering the varying adaptability of different hydrogen production equipment to fluctuating inputs, a hybrid hydrogen production system architecture comprising alkaline electrolyzers, proton exchange membrane electrolyzers, and fuel cells is constructed. A robust empirical mode decomposition algorithm is employed to decompose the coupled wind-solar output power. Based on the lower limit value value of the power carried by the electrolyzers and cold start time of the electrolyzers, dual-channel electrolyzer start-stop control rules are formulated, establishing a Petri net-based optimization control model for dual-channel electrolyzers. The effectiveness of this method is validated through simulation examples using actual operational data from a wind and photovoltaic power plant. Compared to single-channel hydrogen production systems, the dual-channel approach enhances energy conversion efficiency to 61.52%, reduces alkaline electrolyzer start-stop frequency, and improves both safety and economic performance of the hydrogen production system.

Aiming at the problems of waste heat recovery in solid fuel cells using natural gas as fuel and the utilization of cold energy in liquefied natural gas, a combined power cycle of ammonia-water power cycle and two-stage organic Rankine was proposed. The thermo-economic performance of hexane and its mixture was analyzed by the calculation method of numerical simulation. The effects of TUR4 inlet pressure p₁₈, R1270 mass flow rate q_m,11 and TUR1 inlet temperature t₂₆ on system was also analyzed, followed by system optimization. The results show that the overall performance of Case 2 is superior to Case 1 in the high-temperature grade organic Rankine cycle, and the net output power is maximum at hexane/R600 mass fraction of （0.2/0.8）. Increasing p₁₈, q_m,11 and t₂₆ enhances net power output and thermal efficiency, while elevating p₁₈ improves exergy efficiency but compromises economic performance. Matlab optimization results are 3731.72 kW, 41.95% and 2.751×10^-2 $/kWh. The system depreciation payback period and cold storage cooling factor are 4.94 years and 0.233 respectively.

To enhance the light absorption properties and PCE of GeSe thin films, this study proposes a dye-sensitization surface modification method, employing single dyes such as Rhodamine B （RB）, Methylene Blue （MB）, and Congo Red （CR）, as well as their mixtures, to modify the GeSe thin films. Experimental results show that the light absorption capacity of all dye-modified GeSe thin films is significantly improved, with the RB+MB mixed dye demonstrating the most pronounced effect. By simulating the performance of dye-modified GeSe thin-film solar cells using Scaps-1D software, it is found that the GeSe thin film modified with MB exhibits the highest PCE, reaching 26.32 %, whereas the RB+MB modification results in a lower PCE, possibly due to excessive dye loading not being well-matched with other cell performance parameters. This study demonstrates the feasibility of dye-based modification.

An improved variational mode decomposition （VMD） algorithm based on modal correlation and reconstruction error, and an improved subtraction-average-based optimizer （CSABO） to optimize short- and medium-term photovoltaic （PV） power prediction model consisting of temporal convolutional network （TCN） and bidirectional gated recurrent unit （BiGRU） are proposed. Firstly, the historical PV data are decomposed into multiple components with different frequencies using the improved VMD. Then, the components are combined with key meteorological factors, and the PV power forecasts are reconstructed by the TCN-BiGRU model by forecasting each time series data separately. Finally, the parameters of the prediction model are optimized using CSABO to improve the model performance. The actual Australian PV data is used as an arithmetic example for experimental analysis, and the results demonstrate that the proposed model exhibits the best evaluation indexes and higher prediction accuracy compared with EMD-TCN-BiGRU, CEEMDAN-TCN-BiGRU and VMD-CNN-LSTM models.

To address the issues of electromagnetic interference around power frequency transformers in solar photovoltaic power grid connection, as well as complex signal analysis caused by multiple discharge sources discharging simultaneously or alternately, and difficulty in locating partial discharges, a method for locating partial discharges in power frequency transformers in solar photovoltaic power grid connection is studied to improve the effectiveness of partial discharge localization. Based on the analysis of the structure of the solar photovoltaic grid connected power generation system, determine the key components and monitoring point locations. Using ultrasonic sensors to capture the ultrasonic signals generated by partial discharge in power frequency transformers, and based on the time difference positioning method, utilizing the time delay difference between ultrasonic signals to locate the location of partial discharge; considering that the overdetermined equation system used in the time difference localization method is difficult to obtain an accurate solution for the location of partial discharge, the time difference localization method is transformed into an optimization problem for the location of partial discharge. The objective function is to minimize the time difference, and the nonlinearity of partial discharge localization is considered. The firefly algorithm is used to obtain an accurate solution for partial discharge localization that minimizes the time difference. The test results show that this method has a maximum positioning error of 0.05 cm and a positioning time of about 0.3 s for the partial discharge position of the power frequency transformer in the grid connected solar photovoltaic power generation. It has good positioning effect and high positioning efficiency.

Aiming at the equivalent modeling of concentrated PV power station and the traditional clustering algorithm's over-reliance on the initial clustering centers and the number of clusters, this paper proposes a clustering method for PV power station based on the affinity propagation （AP） algorithm, taking PV arrays with different solar irradiance as the object of study. The AP algorithm adopts the existing data points as the final clustering center, without specifying the initial clustering center and the number of clusters. Considering that the clustering index of PV power units needs to reflect the power output characteristics of PV power units, for this reason, the solar irradiance and the equivalent line impedance are taken as clustering indexes, and then the clustering algorithm is used to cluster the PV power units with different the solar irradiance and the equivalent line impedance into different categories, and the PV power units with similar categories are aggregated. At the same time, equate the parameters of the line impedance and inverters of the PV power station to establish a multi-machine equivalent model. Based on the PSCAD simulation platform, the clustering model of a typical 10 kV centralized PV power station is constructed by using the method of this paper, and the simulation results of the detail model, the AP clustering model, the C-FCM clustering model and the single-machine equivalent model are compared, which verifies the effectiveness of the clustering method of this paper under different solar irradiance of PV arrays.

In order to explore the influence of meteorological factors on the dust deposition loss of photovoltaic modules, a prediction model combining similar day clustering, sparrow search algorithm （SSA） and long short-term memory（LSTM） is proposed. Firstly, the main meteorological factors are selected by the Pearson coefficient method. Secondly, the historical data is clustered into three similar day sample sets of sunny, cloudy and rainy days by using the K-means algorithm and prediction models are established respectively. Finally, the LSTM hyperparameters are optimized by SSA. The results show that compared with other models, the SSA-LSTM model has the best prediction effect. And the, a model combination prediction method is proposed. The results show that this method has the characteristics of high prediction accuracy and strong generalization ability, which can provide a reference for the prediction of photovoltaic module dust deposition loss.

The installation inclination of photovoltaic modules is one of the key factors affecting the performance of photovoltaic power generation systems. Addressing the nonlinear relationship between solar radiation and the optimal inclination, as well as the multivariable optimization problems under different geographical locations and meteorological conditions, an optimal inclination model was constructed using the WOA-GWO algorithm. Taking Changsha, a Class IV area of solar energy resources, and Lhasa, a Class I area, as examples, the output power of photovoltaic modules was experimentally studied under various inclination control strategies by using the fixed adjustable bracket based on gas spring. The results show that the optimal inclination model constructed based on the WOA-GWO algorithm has better search efficiency and convergence speed. When adopting the south orientation, the optimal daily total power generation for the adjustable inclination in Changsha under sunny and sunny-to-cloudy conditions increases by 3.10% and 4.13%, respectively, compared to the annual fixed optimal inclination, while the optimal daily total power generation under sunny conditions in Lhasa can be increased by 6.65%. Under sunny conditions in Changsha, using the due east and due west orientations as examples, the optimal daily total power generation is 8.55% and 6.13% higher than the corresponding optimal fixed inclination, respectively. Therefore, the effectiveness of the optimal inclination angle adjustment strategy was verified through model simulation and experimental research, indicating that a reasonable inclination angle adjustment strategy can effectively improve output power in photovoltaic power generation systems. The benefits of this control strategy are more significant in areas with abundant solar energy resources, providing new design ideas for inclination angle control in photovoltaic power generation systems.

This paper proposes a high-gain coupled inductor Buck-Boost converter with resonant soft-switching to address the low voltage gain, high voltage stress, hard switching, and significant reverse recovery of diodes inherent in traditional DC-DC converters. The converter is realized by integrating the C-L-VD-C unit, active clamp branch, coupled inductor voltage-doubling unit, resonant capacitor, and output inductor into the Buck-Boost converter. High voltage gain can be achieved through the C-L-D-C unit and the coupled inductor voltage-doubling structure. A capacitor-inductor loop is established to achieve low output current ripple. The active clamp branch allows all switches to operate under zero-voltage switching （ZVS） conditions. Zero current turn-off of all diodes is realized through LC resonance, reducing losses caused by the reverse recovery characteristics of the diodes, and an extended structure of the converter is proposed. A detailed analysis of the converter’s performance is conducted, providing the critical conditions for achieving soft-switching. A 150 W prototype was built to verify the theoretical analysis results

In order to solve the problems that the modeling of the heating end of the bearing temperature of a wind turbine was simplified in the past and the actual environment was not considered, making it difficult to establish an accurate bearing temperature model, a dynamic prediction method of bearing temperature based on a digital twin is proposed. Firstly, the system dynamics model is established by the finite element method, and the bearing vibration acceleration is preliminarily obtained. Secondly, the mechanism model of bearing temperature calculation is constructed by the thermal network method. The bearing vibration optimization model based on the measured data is established by the optimization algorithm, and the digital twin model of the rolling bearing's thermal boundary conditions is constructed by using the experimental data and simulation results. The dynamic model after the digital twin optimization is used to update the thermal network simulation of temperature. The test results show that the model has good performance in vibration acceleration estimation accuracy and bearing temperature prediction accuracy under the condition of variable speed, which can effectively improve the accuracy of bearing thermal network analysis and prediction model.

In this paper, ABAQUS finite element analysis software is used to establish the rock-socketed model of three-pile jacket foundation, and the horizontal bearing capacity is determined by tangent intersection method, and the horizontal bearing capacity characteristics of the foundation are studied by analyzing the rock-socketed depth of steel pipe piles, grouting material properties and length-diameter ratio of steel pipe piles. The results show that the local regional stress and geotechnical resistance of steel pipe pile in strongly weathered layer reach the maximum; At the initial stage of loading, the grouting material is damaged in tension, and the top of the grouting material is separated from the steel pipe pile. Before reaching the horizontal bearing capacity, the damage zone extends from the tension side to the compression side. Increasing the depth of rock-socketed can significantly improve the horizontal bearing capacity of foundation; The grouting material label has little effect on the horizontal bearing capacity of the foundation, and increasing the grouting thickness can slightly improve the horizontal bearing capacity, but the influence is small; When the pile diameter is changed to improve the ratio of length to diameter, the horizontal bearing capacity of foundation decreases continuously, and the effect is remarkable. Generally speaking, changing the rock-socketed depth and length-diameter ratio of steel pipe piles in rock-socketed environment has a significant influence on the horizontal bearing capacity of foundation.

The phenomenon of foreign body occlusion often occurs in photovoltaic modules during long-term outdoor operation, which affects photovoltaic power generation. Studying the prediction of power generation efficiency loss under occlusion conditions is very useful for monitoring the severity of photovoltaic module occlusion defects and guiding module cleaning scientifically. In this paper, a forecast model of PV module output power loss under partial occlusion based on Tent-SSA-BP is proposed. Firstly, the output characteristics of PV modules under partial occlusion are analyzed through simulation experiments. On the basis of a large number of simulation data, the Tent-SSA-BP prediction model is established. Because BP neural network model is easy to fall into local optimal solution and the prediction accuracy is not high, the Tent-SSA-BP prediction model established in this paper solves the local optimal problem easily caused by the original network, improves the global search ability, and quickly finds a better parameter combination. Through comparative analysis of experiments, the determination coefficient of Tent-SSA-BP prediction model is improved by 0.04 compared with the original network, which has a better degree of model fitting and improves the prediction accuracy of the model.

The unconstrained access of distributed PV to the distribution network inevitably affect the security of the power grid. Therefore, a Monte Carlo importance sampling method based on voltage sensitivity is proposed, which considers the timing characteristics of PV and load to evaluate the maximum access capacity of distributed PV under security and energy efficiency constraints. A distribution network security domain model considering the energy efficiency of the system is established, and taking the feeder as the minimum evaluation zone. Then the bisecting K-means is used to perform cluster analysis on the PV nodes to be connected based on the voltage sensitivity index and node voltage. According to the clustering results, a Monte Carlo importance sampling method is used to construct a new probabilistic model to randomly generate PV deployment scenarios, which realizes the evaluation of the maximum access capacity of PV in all zones in the security region. The simulation results show that after adopting the security region considering system energy efficiency, the maximum access capacity of PV is only reduced by 0.39% compared to the conventional security region, while the system energy efficiency increases by 4.82%, which improves the system operational efficiency. The evaluation accuracy of the proposed method is improved by 12.41% compared with the analytical optimization algorithm, and the number of deployment scenarios required is reduced by nearly 60% compared to the traditional Monte Carlo simulation method under the same accuracy, which improves the accuracy and efficiency of the evaluation of the maximum PV access capacity of the distribution network.

To address the limitations of traditional similar-day clustering methods, which fail to achieve low-cost and accurate identification of short-term variations in weather types and thus affect photovoltaic （PV） power forecasting accuracy, this study integrates the differential features between ideal global horizontal irradiance （GHI） and measured GHI, employing the XGBoost algorithm for ultra-short-term weather scenario classification. Subsequently, a combined VMD-CNN-LSTM prediction model is adopted to capture both local and global characteristics of time-series data, thereby improving forecasting accuracy. Experimental results demonstrate that the proposed method can achieve higher accuracy in ultra-short-term PV power forecasting without requiring additional high-cost meteorological observations. Compared with traditional forecasting algorithms, under ideal sunny conditions, the 30-minute-ahead forecasting performance achieves an R² increase of 3.4%, while the MSE, MAE, and RMSE are reduced by 52.6%, 33%, and 29%, respectively.

Utilizing CGSim, a professional crystal growth simulation software developed and designed by STR, combining crystal dynamics and thermodynamics, selecting N-type monocrystalline silicon commonly used dopant phosphorus is selected, according to the polycrystalline silicon mass of 10^-7 times to add phosphorus monomers as dopant, meanwhile selecting eight different crucible rotate speeds of 7-14 r/min and six different crystal rotate speeds of 6-11 r/min are selected, and orthogonal simulation experiments with a higher crystal pulling rate of 1.8 mm/min were conducted to obtain the change of electrical resistivity. By analyzing the parameters of inner crucible melt temperature field and flow field, outer crucible melt temperature field and flow field, solid-liquid interface, defects in the crystal, appropriate crucible rotational speed and crystal rotational speed were obtained. By comparing with the radial resistivity change obtained by converting the phosphorus concentration to the resistivity at the solid-liquid interface, the influencing mechanism of the radial resistivity of the continuous direct-drawing monocrystalline silicon was researched.

This paper proposes a backtracking trajectory optimization method to improve power generation for photovoltaic array that uses dual-axis bracket to track sunlight, which may cause the front and rear shadow during the sunrise and sunset period. Firstly, the real-time position of the sun and the time of sunrise and sunset throughout a whole day are calculated based on the SPA algorithm, with the SunCalc meteorological data being compared for verification. Then, the bracket rotation geometry model is established to derive and compare the backtracking and non-backtracking trajectories, and the shading ratio-power loss relationship formula under specific conditions is fitted based on the shadow shading experimental datum of a single photovoltaic component. To verify this formula, the peak power values corresponding to different sun positions, rotation angles and shading ratios are calculated based on the HDKR anisotropic irradiance model, which are compared with the measured values. Finally, to achieve backtracking trajectory optimization, the optimal rotation angle corresponding to the maximum power value is solved. The statistical result of the component's power generation and motor rotation power consumption throughout a whole day shows that the optimized backtracking trajectory reduces power loss and further improves power generation than that before being optimized, while the power consumption of the driver motor remains basically unchanged.

In addressing the issue of multiple sensor requirements in traditional photovoltaic cell distributed maximum power point tracking （DMPPT）, a design approach for photovoltaic cell time-division multiplexing （TDM） DMPPT is proposed, incorporating a TDM sampling circuit. This involves the analysis of TDM circuit structures and control strategies, the design of DC/DC converter along with their PI control parameters, and the development of MPPT algorithms aimed at improving the performance of the photovoltaic DMPPT system. Grounded in theoretical foundations, a simulation model is established and a physical prototype is constructed to validate theoretical integrity. Experimental findings indicate that the TDM photovoltaic DMPPT system achieves rapid and effective tracking of each photovoltaic cell's maximum power point using only one set of sensors, thereby significantly reducing the overall cost of DMPPT implementations.

The power-voltage output characteristic curves of PV series modules have multiple peaks when they are blocked by different shadows. The traditional maximum power point tracking algorithm has slow convergence speed and complex control parameters,so it can not achieve the global maximum power point tracking well. In this paper,a simplified sand cat swarm optimization algorithm is proposed,which is compared with particle swarm optimization algorithm and Gray Wolf algorithm respectively from static to dynamic under the complex lighting environment of shadow. Simulation and experiment verify that the simplified sand cat swarm optimization algorithm can realize the global photovoltaic maximum power point tracking. Under the same external conditions,compared with the other two algorithms,the simplified sand cat swarm optimization algorithm has fewer control variables affecting the experimental results. The tracking speed is increased by more than 60% and the tracking efficiency is increased by more than 0.5%. In addition,the power and voltage fluctuation in the convergence process is small,which improves the reliability of intelligent group algorithm applied in photovoltaic maximum power point tracking and improves the utilization rate of photovoltaic power generation.

Four main size cell models of 182 mm×182 mm, 210 mm×210 mm, 210 mm×105 mm and 105 mm×210 mm were established by using Ansys finite element software, and nonlinear structural analysis was carried out on the warping behavior of double-sided crystalline silicon solar cells after metallization. The influence mechanism of warping behavior of crystalline silicon solar cells was further analyzed. The results show that the maximum warpage values of the four different sizes of cells are 1.30, 1.69, 1.67 and 0.45 mm, respectively, at the thickness of 150 μm cells. Under the size of 210 mm×210 mm, the warpage values of 70, 90, 110, 130 and 150 μm cells with different thicknesses are 8.25, 4.96, 3.24, 2.28 and 1.69 mm, respectively. The thickness and size of the battery, the width of the metal grid line and the yield strength are the main factors affecting the warping deformation.

To address the challenges of weak hotspot signals and blurred features in infrared images of photovoltaic modules captured by drones, as well as the high miss and false detection rates in automated inspections, this paper presents an optimized version of the YOLOv5 deep learning model to enhance its ability to detect faint infrared hotspots. First, by incorporating a hybrid feature module （HFM）, the network's feature extraction capability is further enhanced, leading to a significant improvement in detection accuracy. Additionally, a hierarchical aggregation Injection （HAI） network is employed after the backbone of YOLOv5, enabling the convolutional kernels to capture global feature information, thereby enforcing high detection precision. Finally, the WIoU loss function is introduced to improve the original bounding box loss and increase the detection speed of difficult samples. The experimental results show that the mAP of the improved model reaches 80.3%, which is an improvement of 5 percentage points compared to the original model.

With the wide application of distributed PV systems, the problem of users' illegal capacity expansion is gradually highlighted, which poses a threat to the stable operation of the power grid. In order to effectively identify the expansion behavior of distributed PV users, a distributed PV violation expansion identification method based on LSTM-Transformer is proposed. The method firstly screens out the PV power plants that are consistent with the meteorological conditions of the benchmark power plants through time series numerical and morphological similarity preprocessing, then constructs the LSTM-Transformer model, and uses the preprocessed data for training and parameter optimization to predict the theoretical output power of PV power plants. By comparing the actual power generation with the predicted output of the model, Gaussian kernel function is used to calculate the illegal expansion index （EVI）, detecting the severity of illegal expansion of PV users through the size of EVI, and determining the time of illegal expansion of users through the time of EVI mutation. The effectiveness of the proposed method was verified based on the actual data of photovoltaic users.

As a kind of clean and environmentally friendly sustainable energy, photovoltaic power generation plays an important role in the process of building a new type of power system with new energy as the main body. However, the random intermittency of photovoltaic power generation brings great challenges to the stable operation of the grid. Therefore, this paper proposes convolutional neural network-bidirectional long short-term memory （CNN-BiLSTM） prediction model based on grey wolf optimization （GWO） algorithm improved variational mode decomposition （VMD） to improve the accuracy of short-term photovoltaic power generation prediction. Firstly, the pre-processed PV power generation data is decomposed into multiple frequency modal components by optimal variational mode decomposition （OVMD） algorithm. Then, different modal signals and related influencing factors are used as the input of CNN-BiLSTM network for training, verification and testing. Finally, the prediction error is analyzed and reconstructed. The simulation results based on the practical data of a photovoltaic power station in Ningxia verifies that the OVMD-CNN-BiLSTM model proposed in this paper has significant advantages in prediction accuracy and stability compared with various reference models.

This article focuses on mountainous photovoltaic （PV） power stations as the research subject. An automated layout model for these power stations is developed based on the TIN algorithm for contour-to-grid terrain modeling, integrated with scanning line and terrain projection algorithms. Additionally, a shadow occlusion and power generation simulation model is constructed using the polygon （W-A） clipping algorithm and the Perez model. This study analyzes terrain data from a mountainous area in Chongqing, demonstrating that the automated layout algorithm effectively resolves the challenge of transforming the multi-faceted layout of triangular grids into a single-faceted layout. Furthermore, the proposed shadow occlusion, irradiance, and power generation models are compared with PVsyst simulation results. The maximum shadow occlusion error is 0.52%, the average irradiance error is 1%, and the annual power generation error is 0.83%. With a power generation deviation of less than 5%, the results indicate that the method presented in this study is highly accurate.

In order to explore the performance of solar photovoltaic thermal （PV/T） system in the area of relatively poor solar energy resources, we set up a PV/T system experimental platform in Changshou District of Chongqing and carried out the research. The experimental results indicate that： The PV/T system exhibits merit power generation performance in summer and winter, with daily average power generation efficiencies of 14.08% and 14.59%, respectively. The average daily thermal efficiency in summer can reach 35.47%, and the heat collected in a single day makes the water temperature in the 1 m³ tank rise by 22.0 ℃; The average daily thermal efficiency in winter is 12.41%. Use the sensitivity analysis method to compare the impact degree of solar irradiance, cooling water temperature and ambient temperature on the power generation performance of the system. The sensitivity coefficients of the three factors are 110.15, 20.09, and 5.60, respectively, and the system has the highest sensitivity to solar irradiance This study provides reference for the application of PV/T system in Chongqing and other areas with relatively poor solar energy resources.

To accurately forecast the grid connected power generation of solar thermal power stations, an improved sparrow search algorithm combined with long short-term memory networks （ISSA-LSTM model） is proposed. Firstly, based on the traditional LSTM model, the sparrow search algorithm is introduced to construct the SSA-LSTM forecast model, in order to break through the local optimal trap and enhance the global search capability. Secondly, using the elite opposition-based learning strategy to generate reverse solutions and obtain dynamic boundaries of elite individuals, the SSA-LSTM forecast model is improved to further enhance the algorithm's global search capability and search accuracy. Finally, the constructed model is trained and analyzed using real data from the solar thermal power station, and the forecast results of each model are compared with those of other models. After multiple training sessions, the results show that the ISSA-LSTM model has significantly higher forecast accuracy than other models, with a 14% improvement in forecast accuracy compared to the SSA-LSTM model. At the same time, the training results of the model are basically consistent with the actual values. Based on the ISSA-LSTM model, this article predicts the grid connected power generation of solar thermal power plants, providing a theoretical reference for comprehensively improving the accuracy of power generation pre and rational layout of solar thermal power stations.

A modified porous solar receiver with a hollow glass tube at the center was developed to decrease the surface temperature of the receiver and improve thermal efficiency and security.The hollow glass tube matches the heat transfer fluid （HTF） and the concentrated solar flux （CSF）,which can increase the velocity of the HTF by suction effect and reduce the CSF by increasing the absorption area. Then, the high-temperature heat transfer characteristics of the single porous receiver （SPR）,modified porous receiver with a center hole （MPR-CH）,and modified porous receiver with a hollow glass tube （MPR-HGT） were investigated and compared.Results show that the maximal velocity of the HTF at the center zone of the SPR,MPR-CH,and MPR-HGT increases from 0.75 m/s to 1.6 m/s and 1.8 m/s, respectively. The peak CSF of the SPR, MPR-CH, and MPR-HGT increases from 0.75 m/s to 1.6m/s and 1.8 m/s,respectively.Accordingly,the peak solid temperature of the SPR,MPR-CH,and MPR-HGT decreases from 1740 K to 1444 K and 1436 K, respectively.The thermal efficiency of the three receivers increases from 59.3%~71.9% to 65.7%~79.9% and 70.4%~81.6% during the mass flow rate of the HTF from 3.0 g/s to 10.0 g/s. This modified porous solar receiver design can be implemented into advanced, high-temperature power cycles.

Based on the hourly observations of surface temperature, air pressure, relative humidity, visibility, and other data from 170 meteorological stations in China from 2016 to 2023, the atmospheric transmittance under different distances between heliostats and receivers was calculated using the SMARTS model. A sensitivity experiment was conducted on typical representative stations. The average atmospheric transmittance under a distance of 1km between heliostats and receivers nationwide is generally between 70% and 90%. Regions with abundant solar energy resources, such as the Tibetan Plateau, northwest China, Inner Mongolia, and parts of northeast China, have higher atmospheric transmittance, exceeding 85%. After sunrise, the atmospheric transmittance at each representative station rapidly decreases and then gradually increases, with the lowest values appearing in the morning. Stations with lower visibility exhibit greater daily variations in atmospheric transmittance. Atmospheric transmittance is higher in the summer half-year and lower in the winter half-year. The sensitivity experiments indicate that atmospheric transmittance increases logarithmically with visibility. The impacts of other factors on atmospheric transmittance are also related to visibility. Lower visibility increases the sensitivity of atmospheric transmittance to changes in other factors. Atmospheric transmittance decreases with increasing relative humidity.

With the goal of enhancing new-energy consumption capacity and achieving low-carbon operation of the system, in the trading environment of the electricity-carbon-green certificate market, considering the energy sharing model, an operation mechanism for the alliance of thermal power, wind power, photovoltaic power, and pumped-storage energy has been constructed. Firstly, based on the mixed integer linear programming （MILP） method, optimize the output strategies of each power generation entity at different time periods. Secondly, build a many to many energy sharing matching model within the alliance to improve resource allocation efficiency. In addition, in terms of income distribution, the multi factor Shapley value method is used to analyze the income changes of each entity within the alliance under different algorithms. The research results indicate that the energy sharing matching mechanism of the multi energy alliance effectively enhances the economic benefits of the alliance in a diversified market and reduces the level of system carbon emissions. At the same time, through multi market collaborative optimization, the consumption capacity of wind and solar power are improved, and the phenomenon of wind and solar power curtailment is reduced. In the electricity carbon green certificate market environment, the collaborative optimization operation mode of the multi energy alliance can effectively enhance the new energy consumption capacity, optimize revenue distribution, and promote low-carbon transformation.

To address frequency deviation or instability in islanded microgrids with droop control caused by power imbalances, a distributed secondary frequency control strategy based on the Kuramoto model was proposed. The Kuramoto model was employed to systematically model the dynamic behavior of frequency within the islanded microgrid and to design frequency control at the system level. The PI consensus algorithm was used to design the distributed secondary frequency controller, optimizing frequency deviation. Each distributed generation unit relied solely on local and adjacent unit information to achieve a rational distribution of active power loads according to its capacity. The strategy demonstrated robustness against communication network delays, and its asymptotic stability was proven using the Lyapunov direct method. Simulation under various operating conditions on the Matlab/Simulink platform verified the effectiveness of the proposed strategy in reducing system frequency deviation.

In order to further investigate the mechanism of grid-forming control to enhance the stability of the system, this paper firstly establishes the mathematical models of grid-following and grid-forming control, and analyzes the grid-connected oscillation characteristics of the two through the eigenvalue analysis method. Secondly, based on the deficiencies in the calculation of short-circuit ratio of existing renewable energy stations, a correction method for the calculation of equivalent short-circuit ratio of hybrid systems of grid-following and grid-forming is proposed. Subsequently, the article quantitatively deduces the change rule of system short-circuit capacity and equivalent short-circuit ratio caused by the access of grid-forming control units to the system of grid-following units, proves that the process of increasing the proportion of grid-forming units can effectively improve the stability of the system from the point of view of improving the system short-circuit ratio of grid-following units, and verifies the validity of the obtained conclusions based on the electromagnetic transient simulation model.

To address the issues of slow tracking speed and local optimum of conventional maximum power point tracking （MPPT） algorithms for single chamber plant microbial fuel cells （SPMFC）, a hybrid algorithm combining adaptive backstepping control and fuzzy control is proposed. This algorithm utilizes the logical reasoning capabilities of a fuzzy controller to overcome the effects of nonlinearity and coupling on tracking speed, and leverages the adaptability and robustness of backstepping control to avoid local optimum. Grid-connected tests are conducted on the SPMFC. The results indicate that the adaptive backstepping-fuzzy control enables the SPMFC to achieve higher output power and faster tracking speed, ensuring accurate tracking of the maximum power point and maintaining grid symmetry after grid connection. This effectively solves the issues of low power generation in SPMFCs, local optimum in MPPT, and asymmetry on the grid side during grid connection.

To address the issue of the decline in the inertia effect due to the rise in the damping effect, this paper integrates the DC capacitor voltage self-synchronization control and fractional order control theory, proposing a novel grid-forming converters based on DCFOVSC （DC link fractional order virtual synchronization control） control strategy. The dynamic characteristics of the DC link capacitance are employed to achieve self-synchronization, and fractional-order control is incorporated to augment the control degrees of freedom, optimize the dynamic characteristics of the system, and furnish sufficient inertia and damping support for the system. In this paper, a small-signal model of the DCFOVSC control strategy is established, and the dynamic performance and influencing factors of the DCFOVSC are discussed using frequency domain analysis. The analysis results demonstrate that the DCFOVSC is capable of stable operation in a weak grid with good robustness. A grid-connected converter based on the proposed control strategy is constructed in Matlab/Simulink, and the effectiveness of the control strategy is verified under four working conditions： active power variation, grid frequency variation, different grid strengths, and grid voltage amplitude variation. Finally, experimental validation is carried out.

In order to solve the problem that the traditional frequency characteristic analysis method cannot accurately capture the influence of new energy participation inertia support and primary frequency regulation on the frequency characteristics of the system, this paper proposes a power system frequency dynamic model based on the contribution of wind-solar-storage multi-resource frequency regulation. The model comprehensively describes the transient and steady-state characteristics of the system frequency under the conditions of new energy grid connection by introducing the uncertainty of new energy output and energy storage regulation mechanism. Then, an analytical relationship is established between frequency deviation, frequency dead zone, primary frequency modulation coefficient, and the power capacity of each unit. The feasibility boundary of the active output of the energy storage unit not exceeding the limit is derived, and a parameter optimization method under capacity constraints was proposed to determine the optimal energy storage frequency modulation coefficient, effectively improving the accuracy and stability of frequency regulation. Finally, simulation results verify that the proposed method has significant advantages in improving the frequency stability of the system.

In response to the seasonal supply-demand mismatch caused by uneven distribution of monthly power generation and differences in user energy consumption, a two-stage capacity allocation model for an integrated energy system with seasonal thermal energy storage is constructed by considering the thermal adaptability of users. Firstly, the comfort characteristics of users in different heating periods are portrayed, and a strategy for dynamic setting of heating temperatures and differentiated assessment of user comfort is proposed, taking into account the influence of user thermal experience. Secondly, considering the economic and environmental costs, a one-stage capacity allocation model is constructed for an integrated energy system with seasonal thermal energy storage, which is solved by a multi-objective particle swarm algorithm under time series reconstruction and generates an annual reference capacity state sequence for seasonal thermal energy storage. Then, a two-stage multi-timescale operation optimization model is developed to study the system operation under different configuration conditions considering the integrated demand response of long and short-term electricity and heat, and ultimately determine the optimal configuration capacity of the system that makes the highest user satisfaction. The model is simulated with examples to verify the feasibility of the proposed model and its solution method.

To address the performance deviation of simplified component models for S-CO₂ Brayton cycles under off-design conditions, this study establishes an integrated system simulation program based on one-dimensional predictive models incorporating variable efficiency for rotating machinery components. The program investigates the influence patterns of key parameters on the thermodynamic performance of both components and the system under off-design operation. Furthermore, it analyzes the deviation in overall system performance between the one-dimensional predictive models and traditional simplified constant-efficiency models. The research findings demonstrate that an increase in the main compressor inlet temperature results in reduced CO₂ density, consequently leading to increased power consumption by the main compressor. However, under the combined effects of system pressure losses and turbine efficiency, the turbine output power exhibits an opposing trend. System efficiency decreases with rising main compressor inlet temperature, with a more pronounced reduction observed under lower inlet pressure conditions. The maximum efficiency reduction of 11.6% occurs at a suction pressure of 7.68 MPa. Higher system thermal efficiency is achieved when the main compressor inlet parameters approach the pseudo-critical point. Comparative analysis reveals that the maximum discrepancy in system efficiency between the simplified model and the one-dimensional predictive model reaches 6.6%.

Clean and low-carbon development is the core goal of the new-type power system. To facilitate a comprehensive evaluation of the carbonintensity, a source-grid-load-storage evaluation system of new-type power system under carbon peaking and carbon-neutral goal is established. A comprehensive new type of low-carbon indicator system for power system source-grid-load-storage is constructed to address the issues of redundant indicators, strong inter-correlation, limited practical utility, and the absence of energy storage in existing source-grid-load evaluation system. In response to the limitations of subjective and objective weighting methods, the AHP-CRITIC hybrid weighting method is introduced, and the comprehensive weights are calculated based on the least squares method. In light of China’s dual carbon goals and addressing the subjective biasof traditional fuzzy evaluation, an improved FCM fuzzy comprehensive evaluation method based on data-driven prediction is proposed. This method enhances the objectivity and scientific rigor of the evaluation model, while ensuring its applicability over extended time spans, thereby achieving a comprehensive evaluation of the low-carbon performanceof the power system. Finally, the effectiveness of the evaluation model is validated using the power system of a city in western China as a case study.

To analyze the suitable integrated energy system in rural areas, a multi time scale optimization method for rural multi regional integrated energy systems is proposed, considering the impact of system energy sources, energy storage settings, multi station collaboration mode, and grid connection distance on the rural integrated energy system. The results show that distributed energy systems in rural areas are more suitable for adopting a single station independent operation strategy. Multi time scale scheduling can improve system scheduling accuracy. In the scenario of not being connected to the grid, the total cost of renewable energy systems is 59.36% higher than that of fossil energy systems. In the scenario of grid connection, when the grid connection distance is 20 km, the economic efficiency of renewable energy systems is better than that of systems supplemented by fossil fuels, and the consumption rate of renewable energy is 62.5% higher than that of isolated operation. When the grid connection distance is greater than 40 km, the economy of the isolated operation system of the fossil energy supplementary supply is better, with a cost reduction of more than 10.15% compared to the renewable energy grid connection system. The selection of rural distributed energy systems should consider the distance between rural areas and the power grid.

A control method of DC bus oscillation suppression based on duty cycle calculation is proposed in this paper, which is mainly composed of two parts： oscillation current control ring and duty cycle calculation ring, which can effectively extract the main frequency component of oscillation and calculate the required duty cycle, and has a better suppression effect. The small signal impedance analysis of the system shows that the contorl method can effectively reduce the resonant peak of the DC bus and effectively improve the stability of the system. Finally, experiments demonstrate that the method effectively suppresses bus voltage oscillations.

Aiming at the problem of multi-subject benefit distribution in the integrated energy microgrid permeated by hydrogen-enriched compressed natural gas （HCNG）, this paper proposes a low-carbon economic dispatch method based on a bi-level game. Firstly, the electrolysis hydrogen production equipment and hydrogen mixing combustion engine are modeled in a refined way, and the HCNG load is introduced on the load side to improve the whole process of hydrogen from production to utilization. Secondly, carbon trading and demand response mechanisms are introduced to fully explore the carbon reduction potential on the load side while limiting carbon emissions on the source side. The game relationship between microgrid operator （MGO） and load aggregator （LA） is analyzed, and a Stackelberg game model of the upper MGO and the lower LA is established. Then, the model of the paper is solved by using the black-winged kite optimization algorithm combined with CPLEX. Finally, the effectiveness of the proposed method is proved through the comparison of arithmetic examples. The proposed method can effectively equalize the benefit distribution between MGO and LA while reducing the carbon emission of microgrid and improving the rate of renewable energy consumption.

To address the limitations of traditional carbon sharing mechanisms, which often overlook the additional emissions caused by the uncertainty of renewable energy generation, this paper puts forward a novel bi-level method aimed at achieving low-carbon dispatch in integrated energy systems. The strategy accounts for both renewable energy carbon responsibility sharing and the synergistic effects of various demand responses. Firstly, on the supply side, a stochastic optimization method based on scenario comparison is used to quantify the additional carbon emissions induced by renewable energy fluctuations. Secondly, an improved Shapley value-based approach is employed to allocate the residual carbon responsibility to load-side entities and to design a differentiated dynamic tiered carbon pricing mechanism, thereby addressing the combinatorial explosion problem arising from multiple stakeholders. Then, carbon pricing is combined with energy pricing to guide low-carbon demand responses on the load side, maximizing the carbon reduction efficiency of users. Finally, simulation studies on an integrated energy system with electric-thermal coupling verify the effectiveness of the proposed approach. The results indicate that the method accurately allocates carbon emission responsibilities among stakeholders. Moreover, the proposed dispatch strategy achieves a coordinated balance between carbon reduction and economic performance by effectively lowering emissions without compromising system cost-effectiveness.

The stability of floating photovoltaic structures is one of the critical issues constraining the development of offshore floating photovoltaic. Currently, there is a lack of research on the stability of multi-floating photovoltaic arrays, particularly regarding the influence mechanism of connector stiffness on system dynamic response under combined wind and wave action. This study explores the impact mechanism of connector stiffness on the dynamic response of multi-floating photovoltaic arrays under combined wind and wave action based on potential flow theory, utilizing AWQA numerical simulation technology. A single/double-floating-body photovoltaic power station of the pontoon-support type was selected as the research object. The equations of motion for the hinged multi-floating photovoltaic in moored and articulated linkage states were constructed. Comprehensive numerical calculations and analyses were performed on the six degrees of freedom motion of the multi-floating bodies.The effect of connector stiffness on the motion response and mooring force of the double-floating-body photovoltaic system under different wind-wave incidence angles was studied. The results indicate that as the stiffness of the floating body connection increases, the motion response decreases. Setting the connector stiffness appropriately can significantly enhance the stability of the floating photovoltaic platform, reducing motion amplitude, and the optimization reference values for stiffness are proposed.

Based on the analysis of the power angle and current transient characteristics of the grid forming-permanent magnet synchronous generator （GFM-PMSG）, this paper explains the mechanisms behind power angle instability and overcurrent, as well as their mutual influence. A transient coordinated control strategy considering power angle stability and fault current limiting is proposed. The unbalanced power compensation is constructed in the active power loop of the grid forming control to suppress the increase in the power angle, while the reactive power command is adjusted to meet the current limit target, thereby limiting overcurrent. Simulation tests of the GFM-PMSG with different transient voltage drop depths, along with comparisons to other transient control strategies, show that the proposed coordinated control strategy can effectively suppress power angle increase and wind turbine instability. Additionally, it limits the output fault current within the allowable range, ensuring the stability and safety of the GFM-PMSG.

To enhance the accuracy of short-term wind turbine power forecasting, a forecasting model based on variational mode decomposition （VMD） is proposed. The model constructs a long-term trend forecasting module （LTTFM） and a short-term periodic forecasting module （STPFM） for multi-time scale classification forecasting. Firstly, considering the non-stationarity of the wind power sequence, the original power sequence is decomposed by VMD, and the intrinsic mode functions are categorized into low-frequency components, high-frequency components, and residuals based on their correlation. Then, according to the characteristics of each component, different forecasting modules are designed. Long-term trend forecasting module based on GRU is constructed for the forecasting of low-frequency components. Short-term periodic forecasting module containing dimensional transformation and 2D-CNN is constructed for the forecasting of high-frequency components and residuals. Finally, the final power is obtained by synthesizing the forecasting results of each component. The proposed model is verified using actual operation data from a wind turbine, and the results show that the mean absolute error （MAE） of the proposed model is reduced by 34.9%-44.1% compared to commonly used models, indicating that the proposed model has a higher forecasting accuracy.

Traditional intelligent fault diagnosis methods face challenges in extracting reliable features, achieving high diagnostic accuracy, and providing feature interpretability under strong noise and complex operating conditions. To address these issues, this paper proposes an adaptive spectral graph wavelet convolutional neural network （ASGWCN） fault diagnosis method for the bearings in large rotating equipment,such as aircraft engines and wind turbines. The method considers the interactions between multiple sensors and converts vibration signals into graph-structured data. It integrates the adaptive spectral graph wavelet and re-weighted wavelet coefficient strategies,optimized by the Cheetah optimization algorithm,into the graph convolution layers,thereby constructing the ASGWCN. This approach dynamically adjusts the design parameters and decomposition levels of the spectral graph wavelet based on the characteristics of the vibration signal,enabling efficient multi-scale feature extraction. The different scale features are assigned different weights,enhancing the signal denoising and weak feature extraction capabilities. The method achieves end-to-end bearing fault diagnosis in situ under high noise environments,while also providing improved interpretability for the graph convolution process. The experimental results show that the proposed method exhibits excellent diagnostic performance and robustness under both high noise and complex working conditions.

In order to better promote the sustainable development of offshore wind power in the future, this paper excarates and analyzes a total of 49 legal texts and policy documents of offshore wind power policies in China and the UK （30 in China and 19 in the UK） by means of quantitative textual analysis, obtains the concerns of offshore wind power policies in China and the UK, establishes an AI policy evaluation index system using the policy modeling consistency （PMC） index model, and combined with the PMC index value and PMC surface diagram, four representative policy texts from each of the two countries are quantitatively evaluated and comparatively analyzed. It is found that there are obvious differences between the two countries' offshore wind policies, mainly in terms of policy areas, policy contents and incentives, with the UK's offshore wind policy focusing on innovation and development, investment in basic research, and social services, while China still faces challenges in construction incentives, sea area management, and co-operative development. Therefore, this paper concludes that the path to optimise China's offshore wind power policy should focus on the sequence of ‘incentives→policy content→policy areas’, and puts forward optimization proposals to improve China's offshore wind power policy from these three aspects.

The growing capacity of wind turbines and the tendency towards offshore wind turbines pose a challenge in balancing the need for lightweight structures and high reliability. Simultaneously, intensifying market competitiveness results in a reduction in the research and development cycle for the entire machine. To address the aforementioned issues, a structural topology optimization approach for the wind turbine front mainframe was proposed, utilizing a surrogate model. With the goal of minimizing compliance, an artificial volume ratio constraint was set, and torques in different directions were applied to the hub center. The topological optimization results in each direction were obtained and their characteristics were analyzed. The front mainframe was remodeled, and the surrogate model using the finite element method was analyzed to identify the structural construction and its dimensions. The verification results of the optimized structure for its ultimate and fatigue strength demonstrate that the suggested topology optimization methodology is both possible and effective in achieving the lightweight design for the wind turbine front mainframe. Additionally, the product development cycle is significantly reduced.

Four unsteady pulsation sources including turbulence, yaw, shear flow and tower shadow are constructed, and large eddy simulation was used to study the influence of the unsteady pulsation sources on the angle of attack and aerodynamic unsteady characteristics of the wind turbine airfoil. The study shows that under yaw and wind shear conditions, the angle of attack and lift coefficient both show an approximate sine and cosine fluctuation pattern. Under tower shadow conditions, the angle of attack and lift coefficient drop sharply near an azimuth angle of 180°. Under turbulence conditions, the angle of attack shows randomness in both time and space dimensions. Except for wind shear, the fluctuation amplitude of the angle of attack and lift coefficient under other working conditions increases roughly from the tip to the root of the blade. The unsteady pulsation source greatly increases the pressure fluctuations on the leading edge of the pressure side and the suction side, and the increase is more obvious as it approaches the blade tip; In addition, the pressure fluctuation on the suction side of the blade is greater than that on the pressure side. Turbulence is an unsteady pulsation source that induces most significant fluctuations in the blade angle of attack, lift coefficient, and pressure. The incoming velocity spectrum shows a slope of -5/3 in the inertial subrange, while the output power spectrum shows a slope of -11/3. There exists a nonlinear interaction between these two slopes. Furthermore, the pressure fluctuations caused by turbulence near the leading edge exhibit strong periodicity and respond more intensely to large-scale turbulence.

Since both ends of the AC transmission and output lines of the low-frequency transmission system of offshore wind power are power electronic equipment, current distortion in the process of grid failure may cause the adaptability decline of the existing protection . To solve these problems, this paper proposes a new method of multi-criterion comprehensive identification of transformer excitation inrush current based on fuzzy progress. The method synthesizes several characteristics of transformer inrush current, including the second harmonic, waveform symmetry, waveform sine and the ratio of reactive fundamental wave to active DC. By using different membership functions and combining the characteristics of each criterion, different weights are calculated. By synthesizing multiple criteria, the similarity degree between the comprehensive fuzzy set and the preset inrush current or internal fault standard fuzzy set is calculated using the progress function. At last, PSCAD is used to build the low-frequency transmission system model, and the operation state of the low-frequency transformer under different working conditions is simulated. The current data generated under different working conditions are processed and analyzed by Matlab, and the proposed identification method is verified. The results show that this method can synthesize the advantages of each criterion and realize the accurate and reliable identification of inrush current and internal fault in low-frequency offshore wind power system.

This paper constructs an integrated electromagnetic transient model for floating offshore wind turbines （FOWTs） to investigate the transient effects of lightning strikes. Utilizing the ATP-EMTP simulation program, the effects of submarine ground resistivity, anchor length, and anchor chain length on tower base overvoltage during lightning events are analyzed. With the objectives of minimizing overvoltage at the tower base and mooring chain cost, the NSGAⅡ genetic algorithm is employed, using mooring chain length as the decision variable, to obtain a Pareto solution set. Optimal mooring chain length is determined through entropy weighting and TOPSIS methods, culminating in a multi-objective optimized model. Results indicate that lightning-induced overvoltage at the tower base reaches the kV level, positively correlating with seabed soil resistivity and anchorage length, and negatively with mooring chain length. Multi-blade lightning strikes produce overvoltages 1.57 to 3.48 times higher than single-blade strikes. The multi-objective optimization yields an optimal mooring chain length of 41.19 m.

To quantify the effect of earthquake directionality on the seismic behaviors of 15 MW monopile offshore wind turbines, a fully coupled simulation framework is developed based on the modal acceleration method. The dynamic responses of a 15 MW offshore wind turbine over the complete range of wind-earthquake misalignment angles （0°-360°） under wind-wave-earthquake environmental conditions are calculated. Tower-base bending moment, tower-top displacement, and tower-top acceleration of the wind turbine under several typical simulation conditions are compared. The results indicate that the tower-top acceleration is highly sensitive to the wind-earthquake misalignment angle, whereas the sensitivities of tower-top displacement and tower-base bending moment are governed by the external excitations. These findings underscore the necessity of explicitly accounting for the wind-earthquake coupling effect in the design and assessment of large-scale offshore wind turbines.

Compared to traditional steel towers, prestressed concrete wind turbine towers exhibit superior global and local stability, enabling their deployment in complex terrains and for greater hub heights. This study investigates the local and global mechanical properties, as well as the buckling stability, of a full-scale concrete tower under conditions of local symmetric prestress loss in the steel strands. Based on the scale reduction test and the dual scale modeling method. Results demonstrate excellent agreement between the dual scale model predictions and the scaled test data, with relative errors in load and displacement of 0.3% and 5.5%, respectively. For the full-scale model, local edge prestress loss exerts a more significant influence on structural displacement than the case without such loss. Specifically, prestress loss on the windward side results in a 12.2% increase in maximum displacement, while loss on the leeward side causes a 23.6% reduction. Regarding varying magnitudes of prestress loss, tensile stress changes remain minimal, whereas compressive stress changes are pronounced. Linear buckling modes remain largely consistent across different loading conditions, and the location of prestress loss has negligible impact on these modes. In contrast to linear buckling analysis, the critical load derived from non-linear buckling analysis is significantly reduced by 52.3%.

A new three-dimensional yaw wake model （3D_k-yaw Jensen model） with a simple form and convenient calculation is proposed in this paper, aiming to reduce the power generation loss caused by the wake effect and achieve the yaw optimization strategy of wind farms. This model is improved on the basis of the 3D_k Jensen model, introducing the mass and momentum conservation theorems under yaw conditions. It comprehensively considers the impacts of various factors on the wake, such as incoming wind conditions, local geographical information, aerodynamic characteristics and operating conditions, and also takes into account the influence of the yaw effect on the wake center offset and wake wind speed distribution. The accuracy and universality of the model are verified comprehensively through actuator disk/large eddy simulation（AD/LES） numerical simulation and unmaned aerial vehide（UAV） field measurement examples. The results demonstrate that the new 3D_k-yaw Jensen model accurately predicts the crosswind and vertical wake velocity distribution and its development pattern for different turbine types, incoming wind conditions, and yaw angle conditions. This paper provides a theoretical basis and technical support for the research on yaw optimization strategies of wind farms.

In order to quantitatively analyze the influence of various aeroelastic models on the response for wind turbine support structures, the IEA 15 MW wind turbine is taken as the researoh object in this paper. Four types of aeroelastic models are developed based on Blade Element-Momentum theory （BEM）, Generalized Dynamic Wake （GDW）, Geometrically Exact Beam theory （GEBT） and Model Superposition Method （MSM）. The tower-top displacement and bending moments at the pile base are investigated and compared under typical operational and extreme parked conditions using the open-source software OpenFAST. The results show that the blade model based on GEBT results in a 34.89% reduction in tower-top displacement compared to that achieved by MSM under the wind speed of 11 m/s. The fluctuation of standard deviations of bending moments at the pile base based on GEBT is larger than that using MSM. The GEBT model is able to predict the dynamic response of the support structure more accurately in contrast with the MSM model, albeit requiring greater computational accuracy in time steps. Additionally, the discrepancies between structural models under the extreme sea conditions are notably significant. Specifically, the tower-top displacement and bending moments at the pile base of the GEBT model are more dramatic and severe than those of the MSM model under the wind speed of 50 m/s.

The current interpretation methods for the cone penetration test (CPT), which is used to obtain in-situ parameters of clay in offshore wind power investigations, are based on the elastic-perfectly plastic soil model and do not adequately account for the small-strain nonlinear stiffness characteristics of clay. This paper conducts numerical studies to investigate the impact of clay stress-strain nonlinearity and small-strain parameters on cone tip resistance. Saturated clays are modeled using the hyperbolic hardening elastoplastic model and the small-strain elastoplastic constitutive model, simulating the cone penetration process within the ABAQUS finite element software framework. To mitigate mesh distortion during large deformation analyses, the Arbitrary Lagrangian Eulerian （ALE） remapping technique is adopted. Finite element numerical simulations indicate that soil stiffness, in-situ stress of soil strata, cone surface roughness, and failure criteria significantly influence the cone bearing factor. Moreover, an increased soil failure ratio corresponds to a decreased cone factor. Importantly, accounting for small-strain stiffness leads to a marked augmentation of the cone factor, with the extent of this increase being closely related to the interplay among small-strain stiffness, reference shear strain, and large-strain stiffness. Finally, the proposed method for evaluating the cone factor, which incorporates the small-strain stiffness of clay, is validated through a field engineering case, demonstrating robust applicability.

The ultra large-scale development of floating offshore wind turbines （FOWTs） enables them to generate greater motion under smaller environmental loads. Based on this, a novel 15 MW FOWT multi-column with reduced diameter semi-submersible foundation （MRD） suitable for deep sea applications is proposed. Conduct frequency-domain hydrodynamic calculations based on AQWA, and establish a fully-coupled numerical model with open FAST for time-domain simulation analysis. Assess its performance in comparison to with the UMaine VolturnUS-S （UMaine） reference foundation under rated wind conditions. The free decay test proves that the MRD foundation moderates the natural frequencies in the heave and pitch directions, and minimizes the risk of structural resonance. The frequency-domain analysis indicates that the wave excitation in the heave direction of MRD decreases, while the additional mass and radiation damping in the heave and pitch directions increase, and the RAOs in the surge, heave, and pitch directions shrink. The results of fully-coupled time-domain analysis show that MRD has better surge and heave oscillations than that of UMaine, and the maximum output power rises. The research results can provide theoretical reference for the conceptual design of semi-submersible foundations for deep-sea ultra-large FOWTs.

To address the challenge of insufficient primary frequency regulation in grid-connected wind turbines during frequency disturbances, a control strategy is proposed that integrates energy storage on the DC side of direct-drive wind turbines. Firstly, this strategy incorporates a virtual synchronous generator （VSG） control method in the grid-side converter to deliver immediate power support during frequency fluctuations. Furthermore, the addition of a DC-side energy storage system ensures rapid stabilization of the DC voltage to its reference value during frequency dips, allowing the machine-side converter to maintain stable operation in maximum power point tracking mode. Meanwhile, the grid-side converter enhances power output, enabling the wind turbine to perform primary frequency regulation. The proposed approach is validated using simulations of a single-machine system and a four-machine two-area system in Matlab/Simulink. Results indicate that, compared to traditional grid-connected wind turbines, this method increases power output by 24.2% and reduces frequency drop depth by 60% during sudden load changes, demonstrating superior primary frequency regulation capabilities.

This paper introduces a new composite suction caisson foundation. In this paper, the commercial software Abaqus was used to carry out finite element calculations on the foundation, and the effects of foundation embedment depth and soil strength on the bearing capacity under different loading conditions were investigated; Failure mechanisms of composite suction caisson foundations in homogeneous and normally consolidated clays under uniaxial, coplanar composite loading were simulated; The failure envelopes under coplanar composite loading were calculated; A set of close-formed mathematical expressions was presented; And based on the existing research results of the existing skirted circular foundation, the enhancement effect of the small suction caisson on the outer side of the new foundation on the bearing performance is comparatively analyzed. The results of the study show that： the small outer suction caissons have a significant effect on both uniaxial and composite loading performance of this foundation. Due to the evolution of the failure mechanism, the new type of foundation has a better improvement in torsional bearing capacity than other ultimate bearing capacities. The three small suction caissons of composite suction caisson foundation significantly expand failure envelopes in V-H, V-M, H-M, V-T, T-H and T-M loading spaces. The H-M failure envelope gradually shifts towards the first quadrant with increasing embedment depth.

In order to solve the problem of poor flexibility and low accuracy of wind speed probability distribution model in fitting wind speed, a better performance TLW（Topp-Leone-Weibull） distribution model is constructed based on the Topp-Leone distribution family and introducing Weibull. Considering that the increase of model parameters brings about the increase of computational complexity, differential evaluation algorithm is used to optimize the model parameters to ensure the computational effect. Meanwhile, the wind speed data of a region offshore of Fujian over 8 years are selected for the simulation study, and the model is comprehensively evaluated by root mean square error（RMSE）, mean absolute error（MAE）, coefficient of determination test（R²） and chi-square test （X²）. The results show that, compared with the traditional Weibull distribution and other types of distributions, the TLW distribution mode1 shows stronger adaptability in fitting the irregular fluctuations that exist in the upper convex section of low wind speeds of 0-5 m/s and the lower concave section of high wind speeds of 15-20 m/s, and it can effectively capture the trend of the wind speed changes, significantly improve the fitting accuracy, and better track the actual wind speed distribution.

The article suggests a load prediction control strategy for the independent pitch system based on the online identification model of the parameters in an effort to address the impact of time-varying wind speed on the aerodynamic load coefficient of wind turbines and the ensuing increase in the unbalanced load of the wind turbine. Using FFRLS, the time-varying factors in the independent pitch system are first detected online. Based on these parameters, a multi-step prediction model is then built. Lastly, rolling optimization is used to determine the blade's pitch angle in real time. The experiments based on the NREL 5 MW wind turbine model in FAST-Matlab co-simulation validate the prediction accuracy of the multi-step prediction model constructed based on the online identification parameters and the benefits of the designed control strategy in mitigating the unbalanced load of the wind turbine.

The settlement-adjustable wind turbine foundation is a new form of active settlement-regulated wind turbine foundation suitable for deep dump ground layer. The model test method was used to investigate the differences in the force response and deformation patterns of the various components of the traditional gravity wind turbine foundation and the settlement-adjustable wind turbine foundation during operation under horizontal cyclic loading. The test results show that： under horizontal monotonic loading conditions, the ultimate bearing capacity of the settlement-adjustable foundation is better than that of the traditional gravity foundation, with an increase of about 160%; under horizontal equal-amplitude short-term cyclic loading conditions, the amount of change in the inclination of the foundation of the same foundation form and the size of the amplitude of the cycle load are positively correlated, and the amount of change in the inclination of the settlement-adjustable foundation of the same amplitude of the load is obviously smaller than that of the gravity foundation; under horizontal variable-amplitude long-term cyclic loading conditions, the amount of change in the inclination of the settlement-adjustable foundation is significantly smaller than that of the gravity foundation. Under the condition of horizontal amplitude long-term cyclic loading, the amount of change of inclination, displacement and soil pressure of settlement-adjustable wind turbine foundation keeps obviously smaller than that of gravity-type foundation. The axial force of settlement-adjustable wind turbine foundation keeps increasing during the loading process, but the growth rate is gradually slowing down, and the increment of axial force decreases with the increase of monitoring depth of the pile body.

To eliminate the effect of load oscillation caused by cross-link coupling of loading force between multiple nodes in the static loading test of wind turbine blades, a proportional-integral-derivative（PID） decoupling control strategy based on an intelligent optimization algorithm with self-tuning parameters is proposed. Firstly, the mathematical model, between multi-node loading force and the deflection variation of multiple nodes is established, and the algorithm-free control loading of a 71.5 m blade is taken as an example to reveal the cross-link coupling response characteristics. Secondly, combining the variable speed integral and incomplete differential PID control strategy, an improved Beetle Antennae Search algorithm is proposed for the online self-tuning of PID parameters in the nonlinear loading process. Finally, the proposed control strategy is applied to a seven-node static load proof test of 110.5 m wind turbine blades. The results show that the control strategy can effectively reduce the multi-node cross-link coupling, meet the requirements of coordination control of loading force, and realize stable loading of large blade static loading test.

The grid-connected system of grid-based permanent magnet wind turbines has the problems of fault current and transient instability under the condition of grid fault, and the traditional grid-based fault ride-through control strategy is difficult to apply, which makes it difficult to maintain the operation of the grid before and after the fault. To this end, a fault ride-through control strategy for grid-forming permanent magnet synchronous wind turbine grid-forming wind turbine permanent magnet synchronous generator（GFM-WTPMSG） based on active power command and virtual impedance adjustment was proposed. Firstly, a mathematical model of grid-forming permanent magnet synchronous wind turbine based on virtual synchronous control was established. Then, according to the fault current threshold and transient power angle stability constraints, the control strategy under different grid voltage drop depths was proposed based on power command optimization and adaptive virtual impedance adjustment. Finally, the effects of different power commands and virtual impedances on the transient stability were simulated, and verified based on the RT-LAB hardware-in-the-loop simulation platform, the results show that the proposed control strategy can effectively reduce the fault current to the threshold range and improve the transient stability of the grid-connected system, and realize fault ride-through under the control of the grid-based structure.

Aiming at the problems that traditional wind power forecasting methods have few considerations in terms of the close relationship between time series and spatial global features and parallel processing, and the prediction reliability is limited, an ultra-short term wind power forecasting method based on cross-attention fusion of spatial-temporal features of TCN-SENet-Transformer is proposed. Firstly, squeeze and excitation networks（SENet） are used to adjust the channel feature weights, and temporal convolutional networks（TCN） are used to capture the spatial features of the data. Meanwhile, Transformer is used to identify the long-term timing characteristics of multi-feature data. Then, cross-attention（CA） is introduced to integrate temporal and spatial features. Finally, the actual data of a wind farm in China is used to forecast ultra-short term wind power, and the comparison is made with other prediction models. The results of example analysis show that the proposed combined prediction model effectively improves the prediction accuracy.

In this work, three different artificial reefs, namely cube, trapezoidal and hemispherical artificial reefs, are studied by numerical simulation method to analyze the flow obstruction characteristics of these three artificial reefs and the influence of arrangement on the hydrodynamic characteristics around the pile foundation. The results show that the trapezoidal artificial reef is larger than the hemisphere and larger than the cube in the range of the backflow area of the three artificial reefs. With the decrease of the distance between the cube and the trapezoidal artificial reef and the cylinder, the maximum shear stress of the seabed shows an increasing trend, while the influence of the hemispheric artificial reef on the maximum shear stress of the seabed shows a decreasing trend with the decrease of the distance between the real and the monopile.

Based on dynamic blade element momentum and geometrically exact beam theory, the effect of aerodynamic coupling effect on the load characteristics of IEA-15 MW wind turbines under wind shear conditions are studied in this paper. The results show that the wind turbine loads are similar to simple harmonics under the wind shear condition, and the flexible deformation of the blades will lead to the reduction of the wind turbine loads. Due to the dynamic inflow effect, unsteady aerodynamic characteristics of airfoil and blade deformation, the delay of wind turbine load response will increase the yaw torque of wind turbine. With the increase of wind shear coefficient, the mean value and fluctuation amplitude of wind turbine power, thrust, wind turbine pitch moment and yaw moment increase, and the hysteresis of blade load response becomes more obvious.

To improve short-term wind power forecasting accuracy, a predictive model based on the Black Kite Algorithm （BKA） and Variational Mode Decomposition （VMD） was been proposed. Initially, the BKA algorithm was used to determine the VMD parameters, allowing VMD to decompose the data into multiple subsequences, which helps reduce the complexity and volatility of wind power time series. Each subsequence was then combined with essential meteorological data, creating input components for the predictive model, which is a hybrid framework involving a Temporal Convolutional Network （TCN）, Bidirectional Gated Recurrent Units （BiGRU）, and an attention mechanism. This composite model independently forecasts each input component, after which BKA optimizes the model’s hyperparameters to obtain the best combination scheme. The final wind power forecast was produced by summing and reconstructing the individual predictions. This model was validated using real wind power data from a wind farm in Xinjiang, and its performance was compared with five other combined forecasting models. Experimental results indicate that the prediction effect of the proposed model is superior to that of other predicition models, effectively enhancing the accuracy of short-term wind power forecasting.

This article proposes the analysis and mining of SCADA operation data to improve the output performance of wind turbines. By analyzing the operating status and abnormal data types of wind turbines, an improved DBSCAN clustering method is adopted to clean and detect abnormal data caused by environmental changes. By using an improved moving least squares B-spline regression model, the control characteristic parameters corresponding to the maximum unit output at different wind speeds are identified and fed back to the unit control system. On the basis of unit simulation verification, validation and analysis were carried out under actual operating conditions of real units. Actual tests show that this method significantly improved system efficiency at rated wind speed.

To investigate the effects of potassium and sodium on the propagation velocity of biomass smoldering, biomass rods and char rods were made by adding different proportions （0-1.65%） of KCl and NaCl to a mixture of pine-elm powder, smoldering propagation velocity was compared and analyzed. The results show that both KCl and NaCl enhance the smoldering propagation velocity of biomass and char rods. The smoldering velocity of biomass rod increases with the proportion of KCl, reaching a maximum of 1.5 times that of the original biomass rod. The effect of NaCl is more significant, which is 1.8 times of the original velocity at 0.33%-1.65%. For char rods,the enhancement of smoldering velocity by KCl and NaCl both firstly increases and then decreases, and the maximum smoldering velocity is obtained at 0.99% and 0.33% proportions of KCl and NaCl, respectively. The effect results are consistent with the oxygen transfer theory and the Langmuir-Hinshelwood mechanism. The more significant influence of NaCl compared to KCl is attributed to sodium’s smaller relative atomic mass and more reactive properties.

To enhance the anchoring capacity of gravity anchors, a novel perforated gravity anchor was proposed. Numerical analysis methods were employed to study the effects of foundation perforation rate （R）, number of perforations （N）, and perforation shape （S） on the anchoring capacity of gravity anchors in homogeneous saturated clay. An empirical formula was established to determine the anchoring capacity. The study found that with a fixed number of perforations N, both horizontal and uplift anchoring capacities of the foundation decrease with increasing porosity rate R. When the porosity rate R is constant, the horizontal and uplift anchoring capacity coefficients N_cH and N_cV significantly increase with the increase in N. Specifically, N_cH growth rate slows and stabilizes when N≥16, while N_cV grows slowly for 9<N<16 and remains constant when N≥16. The perforation shape has a minor effect on the anchoring capacity. The research results provide a theoretical basis for optimizing the anchoring foundation design of offshore floating photovoltaic systems, ensuring their stability and reliability in harsh marine environments.