In this study, the effects of pyrolysis temperature and lignocellulosic biomass/microalgae blending ratios on the yield, elemental composition, surface morphology, thermal stability, pore structure, surface functional group distribution and carbon skeleton structure of biochar derived from lignocellulosic biomass and microalgae co-pyrolysis were systematically investigated, and the synergistic effect and reaction mechanism during the co-pyrolysis process were further revealed. The results show that pyrolysis temperature and biomass/microalgae blending ratios had significant influence on the yield and physicochemical structure of co-pyrolysis biochar. The biochar yield decreases with rising pyrolysis temperature, and increases with higher microalgae blending ratios. The yield of co-pyrolysis biochar can reach as high as 41.51%, which is 23.5% higher than that of individual biomass biochar. The co-pyrolysis process of microalgae and lignocellulosic biomass exhibits a significant synergistic effect, which can enhance the yield of biochar and significantly promote the enrichment of C and N elements in biochar. The increase of pyrolysis temperature facilitates the conversion of pyridine-N to quaternary-N in biochar. With the increase of the microalgae blending ratios, the disorder degree and aromaticity of biochar is enhanced. The nitrogen-containing volatile produced from microalgae can react with the oxygen-containing functional groups on biomass through Maillard reaction, which further experience cyclization reactions and polycondensation reactions to form nitrogen-rich biochar.

To achieve a simple and low-cost real-time measurement of sea waves, this article proposes a non-contact wave parameter measurement method based on shape-from-shading （SFS）. Initially, a single camera is used to capture images of the wave tank and floating reference objects. Then, the three-dimensional shapes of the wave surface and reference objects are reconstructed using the SFS method. A size transformation coefficient is introduced into the 3D reconstruction results to accurately calculate wave parameters. The proposed method's wave parameter measurements are compared with actual measurements. Results indicate that the relative errors for average wave height and wavelength are less than 5%, the relative error for the average period is less than 1%, and the deviation in wave direction is within 5°. This method demonstrates high stability and accuracy.

This paper presents a hydrodynamic performance prediction method based on Joukowski transformation and Gaussian process regression for rapid airfoil design of small-scale tidal stream turbines operating in low-velocity conditions. A set of Joukowski airfoils were selected as initial profiles and fitted using CST parameterization method. The lift and drag forces under low-velocity conditions were obtained through Fluent numerical simulation. A hydrodynamic performance prediction model was established by combining lift, drag, CST parameters with Gaussian process regression based on machine learning. Experimental results show that the average error rates of lift and drag prediction models are below 0.2%, with root mean square errors less than 3%.

Based on long-term wave measurements in the northern sea area of Jiangsu Province, this study statistically analyzed four typhoon events and six cold wave processes to investigate wave characteristics under extreme weather conditions. The results demonstrate that the maximum wave heights induced by typhoons and cold waves are comparable, with significant wave heights reaching 4.59 and 4.73 m respectively. The typhoon waves generated by landfall typhoons are much greater than those of non-landfall typhoons. A positive correlation is observed between wave height and period during extreme wave events, and the wave spectrum is mainly single-peak spectrum, and both wind waves and swell waves exist.

In order to improve the steady-state performance of the bus voltage of the energy-storage flexible multi-state switch（E-FMSS）DC side and suppress the DC voltage fluctuation when the connected feeder fails, this paper proposes a PID controller with improved fuzzy linear self-disturbance resistance controller（LADRC） instead of the outer ring of DC voltage. The improved fuzzy LADRC, based on the derivation model, adds an additional proportional link in parallel in the integral link of the disturbance estimation of the linear extended state observer （LESO） to enhance the accuracy of the disturbance estimation, and considers the LESO for system total disturbance estimation error compensation. The online optimization of fuzzy LADRC parameters is achieved by combining the active anti-disturbance parameter tuning method of the improved beetle antennae search. Simulation results show that when the feeder connected by E-FMSS fails, the strategy proposed in this paper can significantly suppress the DC voltage fluctuation of E-FMSS, the active power of each port is smooth transition, the system can quickly recover stability, and has good disturbance resistance.

In order to further improve the power system ramping flexibility, this paper puts forward the method of ramping flexibility collaborative planning of high proportion of renewable energy power system considering lifespan dynamic investment of multi-types energy storage. Firstly, considering that the maximum charging and discharging power and state of charge （SOC） characteristics of energy storage shows obvious deterioration over their service lifespan, additional capacity needs to be installed each year during subsequent operation, therefore, this paper established a lifespan deterioration model for energy storage based on experimental data and proposed a dynamic investment allocation method that takes into account the additional investment during the lifespan. Secondly, based on the classification of energy storage by charging and discharging speed and unit capacity investment, a multi-type energy storage and coal fired power plant transformation integrated ramping flexibility coordinated planning and configuration model was established for high proportion of new energy systems. Thirdly, based on the ramping requirements of different types of energy storage and coal fired power plant transformation, the large-scale multi-variable model was divided into multiple stages of small-scale solution processes, in which the unknown variables were solved in each stage. Finally, the IEEE 30-node system was used as an example to verify the correctness and effectiveness of the proposed model.

For the interleaved parallel structure of resonant modular multilevel DC-DC converter （MMDC）, a continuous control set model predictive control （CCS-MPC） is proposed for the current inner loop control. The working mode of the converter in boost mode is analyzed, and the inductance current prediction model in boost mode is established. Compared with the traditional PI control, it avoids the tedious parameter adjustment and improves the dynamic performance of the system. Finally, the simulation and experimental results of the two kinds of control are analyzed and compared through the simulation and physical experiment platform, which proves the feasibility and effectiveness of the proposed method.

In order to improve the low-carbon economy of the distribution system, this paper first constructs the collaborative optimization scheduling framework of multi microgrid shared energy storage system. At the same time, in order to realize the flexible adjustment and fine characterization of emission reduction incentives on the basis of fully considering the supply-demand relationship of the carbon trading market, a refined carbon trading cost calculation method based on the dynamic carbon price and carbon quota supply-demand relationship is proposed; Then, in order to improve the timeliness of power decision-making in the real-time phase of the multi microgrid shared energy storage system and enhance the ability of the decision-making model to perceive the time-series information of wind and solar output with uncertain factors, a reinforcement learning algorithm combining short-term and long-term memory network and multi-agent near end strategy optimization method is proposed; Finally, in IEEE 33 bus system, the improvement effect of the proposed model and method on the low-carbon operation ability, operation economy and decision timeliness of the system is verified by simulation.

Aiming at energy coupling problem existing in the coordination and interaction among multiple virtual power plants （MVPP）, a quantitative evaluation function of service quality for shared energy storage （SES） is established. Combined with the evaluation function, a bi-level optimization scheduling strategy of energy sharing among MVPP is proposed. In the upper level, taking into account both the target of economic benefits and service quality of SES, the capacity allocation is optimized. In the lower level, the units output within MVPP and transaction power among them are optimized to minimize the operation cost. To address the uncertainty of RE, based on chance constraint, an optimization model is constructed, where the KKT condition and the Big-M method are used to transform bi-level model into an equivalent single-level model. Simulation verifies the rationality of the proposed model and cases analysis are employed to demonstrate the impact of SES service quality and confidence level on energy sharing and energy storage configuration of MVPP. In addition, results show that with the consideration of service quality for SES, the enthusiasm of MVPP to participate in power sharing is enhanced effectively, realizing the rational utilization of resources.

The power market's frequency regulation is being gradually influenced by flywheel energy storage's high frequency modulation performance with the development of new power systems. To address the issue that the traditional marginal clearing method overly focuses on frequency modulation performance and neglects cost-effectiveness, a market clearing strategy for modulating frequency of flywheel energy storage using Stackelberg game is proposed. A cost model for the charge and discharge of flywheel energy storage is developed firstly. To optimize the quote and minimize frequency regulation and auxiliary expenses, the Stackelberg game method is used with consideration of the operational constraints of flywheel energy storage, thermal power units and wind farms. Finally, according to the real-time frequency modulation demand, the clearing is carried out according to the marginal price ordering. The simulation results show that the proposed mechanism ensures a reasonable winning rate and revenue for flywheel energy storage in the frequency modulation market, this aids in lowering the total cost of frequency modulation and enhancing the system's overall performance. Compared with the traditional method, this method improves the frequency modulation income of the unit and effectively promotes the consumption of new energy.

To improve the control accuracy of permanent magnet synchronous machine in the flywheel energy storage system （FESS）, an improved model predictive control strategy is proposed. Firstly, by alternately evaluating the cost functions of torque and flux, the weighting factor is eliminated and there is no need to determine the priority of control objectives in this proposed method. The proposed method effectively balances the control performance of torque and flux. Subsequently, to reduce the torque and flux fluctuations of the FESS, a double-vector synthesis method is designed and the duty cycle calculation process is simplified. In addition, to improve the parameter robustness of the system, the least squares method is used to identify the machine parameters and update the controller parameters in real time. Finally, the feasibility and effectiveness of the proposed control strategy in FESS charging and discharging conditions are experimentally verified.

To enhance the accuracy of RUL predictions, we proposed a novel lithium battery RUL prediction model that integrates multi-scale decomposition with multi-model fusion, effectively addressing noise and local fluctuations in capacity degradation data. Firstly, complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） is employed to decompose the original capacity data into several components. The high-frequency components primarily reflect short-term local variations and noise, while the low-frequency components capture the long-term degradation trends. Secondly, bidirectional long short-term memory （BiLSTM） networks and Gaussian process regression （GPR） are applied to model the decomposed high-frequency and low-frequency components, respectively, capturing the complex patterns and dependencies in the time series data. To further enhance predictive performance, the model parameters are optimized using an adaptive particle swarm optimization （APSO） algorithm. Finally, the individual predictions from each component are aggregated to compute the overall battery RUL. To evaluation on public datasets includes a comprehensive suite of experiments, such as comparison, ablation, and generalization studies. The results demonstrate that the proposed model achieves minimum AE, MAE and RMSE values of 0, 0.15%, and 0.18%, respectively, for the RUL prediction task. These findings highlight the model’s excellent generalization ability and high prediction accuracy, establishing its effectiveness for lithium battery RUL prediction.

A robust MPC for three-phase grid-connected inverter system is proposed to address the high dependence on system model of traditional MPC in this paper. The dynamic sliding mode disturbance observer is designed to observe the centralized disturbance caused by the parameter mismatch of three-phase grid-connected inductor, and the disturbance observation is compensated to the system predictive model. At the same time, the active current and reactive current output control of the three-phase grid-connected inverter are incorporated into the cost function of MPC. The cost function values are evaluated for all valid switch states, then the optimal switch state that minimizes the cost function will be selected to trigger the grid-connected inverter. Finally, a 10 kW three-phase grid-connected inverter experimental platform is built to verify the feasibility and effectiveness of the proposed robust MPC.

In order to explore the hydrogen diffusion law after hydrogen refueling station leakage and formulate corresponding protective measures, taking a planned hydrogen refueling station in Ningbo city as the research object, the CFD simulation software Ansys Fluent was used to simulate the hydrogen leakage diffusion under various external environmental conditions. Results indicate that wind has a significant impact on the leakage and diffusion of hydrogen: higher wind speeds correspond to smaller volume of hydrogen cloud formed and faster attainment of steady state. Structural elements such as buildings and equipment can redirect wind and reduce its speed to some extent. In such cases, the volume of formed hydrogen cloud is slightly larger but still smaller than under windless conditions. Hydrogen tends to accumulate at areas such as the junction of the station and the roof, as well as above the hydrogen dispenser. Placement of hydrogen sensors near these locations enables effective leak detection, reducing potential risks at hydrogen refueling station. Based on the constructed model, a layout scheme for hydrogen sensors is proposed, ensuring successful alarms in all studied scenarios. When wind direction is perpendicular to the direction of the leak, the volume of hydrogen cloud formed is small and detection may be relatively challenging, potentially leading to delayed alarms. Hence, it is suggested to increase personnel patrols or deploy additional mobile alarm devices in these areas.

To tackle the non-uniform distribution of alkaline electrolyte across the corrugated bipolar plates during alkaline water electrolysis process for hydrogen production, the present work undertakes a structural optimization of the bipolar plates in a 5 Nm³/h alkaline electrolyzer. Computational fluid dynamics was employed to resolve the electrolyte velocity field under representative operating conditions. Numerical results indicate that the high-velocity zone is concentrated along the central axis of the existing corrugated plates, undermining flow uniformity. Two complementary remedies are therefore proposed. First, inclined guide grooves are introduced at the electrolyte inlet to laterally disperse the incoming flow. Second, the conventional spherical protrusions and recesses are replaced by ellipsoidal corrugations that alternately split and refocus the electrolyte stream. Simulations demonstrate that the combination of slanted inlet grooves and ellipsoidal surface topography markedly improves flow uniformity across the entire bipolar plate.

This study developed a three-dimensional model of a composite flow field in proton exchange membrane fuel cells （PEMFCs） to investigate the effects of operating conditions and geometric parameters of the flow field on cell performance. The findings reveal that oxygen distribution and current density in the inclined flow field are more uniform compared to the parallel and serpentine flow fields. The cell’s power density output is improved by 7.43% and 12.05% in the inclined flow field relative to the serpentine and parallel configurations, respectively. Higher operating pressures increase oxygen concentration within the channels, leading to enhanced cell output performance, while temperature shows a relatively minor influence due to the structural limitations of the flow field. A reduced number of channels in the flow field leads to uneven oxygen distribution, thereby diminishing the cell's performance. Increasing the rib-to-channel width ratio improves output performance, while changes in the channel aspect ratio show minimal impact on performance.

To address the issue of low efficiency in hydrogen production from large-scale fluctuating wind power, we proposed an optimized control strategy for an electrolyzer array in a wind-hydrogen system combined with grid power purchases. Firstly, we stuolied the hydrogen production efficiency curve of proton exchange membrane electrolyzer. Based on the efficiency characteristics, we proposed a segmented regulation strategy considering hydrogen production efficiency for the start-stop control and power distribution of the electrolyzer array. Secondly, aiming to minimize the total system cost, we constructed an intraday optimization scheduling model for the wind-hydrogen system based on the artificial bee colony algorithm, using actual data from Ningbo for computational analysis. The results show that the proposed strategy increases hydrogen production by an average of 5.93% per hour compared to simple start-stop control, reduces the average daily start-stop times from 18 to 2, and improves the electrolyzer utilization rate from 64.58% to 98.75%. Furthermore, when the electricity price decreases by 47.27% and the hydrogen price increases by 14.29%, hydrogen production and power purchase increase by 15.56% and 227.79%, respectively, but the total cost decreases by 5.91% and 12.78%, indicating that the hydrogen price has a greater impact on the system’s economic performance.

In order to meet the development needs of hydrogen energy industry in China, the number of domestic hydrogen refueling stations has been increasing year by year, now ranking first in the world. However, the lack of a safety management system for hydrogen refueling stations poses hidden dangers to their safe and healthy development in China. It is urgent to establish a safety management system suitable for hydrogen refueling stations in China. Based on the accumulated experience of China special equipment inspection and research institute in petrochemical plant integrity management and asset integrity management over the years, a hydrogen station integrity management system of “system documents + technical system+information management system” has been established. This paper first describes the components of the integrity management system for hydrogen refueling stations and then outlines the construction procedure. It also presents the practical implementation and effects of the system and, finally, offers several suggestions for the future development of the integrity management system for hydrogen refueling stations.

Based on the traditional serpentine flow channel, several non-uniform flow channel structures with alternating, gradient, trumpet, and dispersed types were proposed in this paper. By establishing a three-dimensional isothermal steady-state PEMFC simulation model, the effects of non-uniform flow channels on the mass transfer characteristics and output performance of PEMFCs were analyzed. The results show that compared to the conventional serpentine flow channel, non-uniform designs can effectively improve the transport and distribution of reactant gases and products within the flow channel, enhance the electrochemical reaction process, and achieve higher membrane current densities. Furthermore, these designs can better balance transport performance and pressure loss matching, effectively improving the energy utilization efficiency of PEMFCs. Specifically, the MD-Ⅵ flow channel increases the peak current density of PEMFCs by 10.2% and the peak power density by 14.2%. The non-uniform designs are expected to provide more ideas for the innovation and optimization of PEMFC flow channel structures.

This paper constructed a full-process hydrogen utilization model that encompasses hydrogen production from renewable energy generation, hydrogen storage and transportation, and power generation using hydrogen fuel cells. Aimed at the entire hydrogen chain of “production-storage-transportation-utilization”, a multi-dimensional comprehensive evaluation system was proposed based on the analytic hierarchy process, entropy weight method , and TOPSIS. This system established multiple evaluation metrics from three dimensions: economy, energy conservation, environmental protection, and technology, to analyze the optimal storage and transportation scheme under different hydrogen demand scenarios. The effectiveness of the constructed model was verified through a specific case study, and the evaluation system was applied to comprehensively rank hydrogen storage and transportation options under various transportation distances. The results demonstrate that the proposed method can provide a theoretical basis for the planning and decision-making of hydrogen energy systems, offering valuable insights for promoting the integration of hydrogen energy into new-type power systems.

Aiming at the problem of detecting bird droppings and shading defects as well as light spot defects formed by diode damage in photovoltaic modules under aerial high-resolution images, a detection algorithm combining the improves deep learning model YOLOv8n with slice-assisted super-reasoning is proposed. The PV module dataset is constructed by slicing 2667 visible and thermal imaging images and labelling them with three kinds of defects： bird droppings, shading and light spots. Firstly, on the backbone network of YOLOv8n, a small target detection layer is constructed by adding a 3-layer CBS module to enhance the transmission of small target feature information. Secondly, a global attention mechanism is added to the Backbone part, adopting the framework of channel and spatial attention, so that the model can better capture the global feature information. Based on the above two improvements to the network structure to design its ablation experiments, the experimental results show that the improved model improves the mAP50 value and mAP50：95 by 4.3% and 1.5%, respectively, compared with the base model; to design the comparison experiments between the improved model and the other target detection models, and the experimental results show that the improved model's accuracy is better than that of the other detection models. Finally, combined with slice-assisted hyper-reasoning for slicing before detection, the overall model comparison experiments are designed with the addition of slice-assisted hyper-reasoning, and the experimental results show that the improved YOLOv8-PG+SAHI model is optimal for detecting small target defects in high-resolution photovoltaic module images, and the accuracy rate can reach 88.73%. The above experiments show that the improved model is more suitable for small target detection of PV modules under aerial high-resolution images.

This work summarizes the main sources of dust deposition on the surface of photovoltaic （PV） modules, sorting out the domestic foreign research progress on dust accumulation, and introducing the current commonly used PV cleaning technologies in detail. Analyzing and comparing the advantages and disadvantages, applicability in eastern and western PV power, and economy of various technologies, and finally looking forward to the future development direction and research trends of PV dust deposition mechanism and cleaning technology.

This paper proposes a distributed voltage control algorithm for distribution networks based on an improved model-free adaptive control method. The proposed algorithm leverages real-time sampling data from distributed PV units and estimates the dynamic linearization parameters that characterize the voltage control characteristics of the distribution network online through adjacent communication between the PV units. This data-driven, iterative approach achieves coordinated reactive power-voltage control without relying on a detailed network model. The stability of the algorithm is proven mathematically. Finally, a multi-scenario simulation is conducted using the modified IEEE 33-bus distribution system to demonstrate the effectiveness and advantages of the proposed control algorithm.

In order to study the impact of complex mountainous terrain on the regional wind field of photovoltaic （PV） farms, an actual PV site is taken as the research object, and the complex terrain is quickly modeled based on Blender+GIS.Numerical simulation of computational fluid dynamics is carried out using ANSYS finite element software, and by setting two different calculation ranges, quantitatively determines the wind field characteristics of the complex mountainous terrain under different wind conditions as well as the wind speed correction coefficients that should be taken into account in the design of photovoltaic mounts.The results show that the distribution of the near-surface wind field in complex mountainous terrain is highly non-uniform.The maximum wind speed correction factor for the area in which each site is located occurs primarily when the wind is from the northwest （315°） and is generally consistent with the weather report data. The wind farm data obtained in the numerical simulation are applied Python automated processing, and finally the most unfavorable wind direction and the corresponding wind speed correction coefficient suggested values for the actual construction site are obtained.

In order to achieve accurate fault diagnosis and improve the power generation efficiency of photovoltaic array systems, this paper proposes a SV-CBDE diagnosis model that integrates a hybrid model of convolutional neural network and bidirectional gated recurrent unit network, and a hybrid model of deep belief network and extreme learning machine with a soft voting method. Taking the P-V curve data of the photovoltaic array as input, the two models are used to diagnose the samples separately, and then the probabilities of the diagnostic results output from the two diagnosis models are weighted and averaged to derive the final diagnostic results and thus the type of faults are determined, so as to improve the accuracy of the photovoltaic array fault diagnosis. The proposed model is evaluated on the same dataset against four fault diagnosis models （CNN, CNN-LSTM, CNN-BiGRU, and DBN-ELM）. The results indicate that the integrated model proposed in this paper exhibits higher accuracy, recall, precision, and F1-score compared to the other four models in diagnosing inter-string open circuits, locally generated shadows in components, inter-component short circuits, photovoltaic array aging, and three types of concurrent faults, demonstrating its superior fault identification capability.

In order to assess environmental impact throughout the full life cycle of the monocrystalline silicon photovoltaic system, a life cycle assessment （LCA） method was used to analyze and evaluate the energy consumption and carbon emissions of the production, transportation, operation and recycling stages of the 1 kW monocrystalline silicon photovoltaic system. The results show that the whole life cycle energy consumption of the 1 kW monocrystalline silicon photovoltaic system installed in Kunming is 992.08 kWh, with carbon emissions of 2033.24 kg. The energy payback period and carbon payback period are 0.90 and 16.96 years, respectively, both of which are shorter than the system’s operational lifespan. Energy consumption and carbon emissions are primarily concentrated in the production stage, with the production of solar-grade polysilicon having the largest share, followed by the production of balance-of-system components and monocrystalline silicon wafers. Improving the production technologies for solar-grade polysilicon, balance-of-system components, and monocrystalline silicon wafers, as well as enhancing the recycling efficiency of discarded photovoltaic systems, is key to reducing energy consumption and carbon emissions. Additionally, transportation distance and installation location can impact the energy payback period and carbon payback period.

The progress of concentrating photovoltaic technology is regulated the temperature of photovoltaic modules and improving the power generation efficiency of photovoltaic systems have become the core issues to ensure the stable operation of the system. In this work, a heat dissipation technology combined with pulsating heat pipe （PHP） and fin was proposed to reduce the operational temperature of photovoltaic panel. And the experiment tested the operating characteristics and heat dissipation efficiency of the combined device. The results found that the combined heat dissipation structure of the PHP and fin can efficiently and quickly reduce the working temperature of photovoltaic panels. Meanwhile, the high heat dissipation demand of photovoltaic panels could lead to a large temperature difference between the two ends of the PHP, as well as accelerating the working fluid in the pipe. Thus, it could achieve the better startup characteristics and higher heat transfer efficiency of the PHP. Besides, the tested results indicated that the fins could effectively reduce the thermal resistance of the PHP, which could further improve its heat transfer efficiency during the operation. Finally, based on the measured results, a comprehensive evaluation was conducted on the enhanced effect of the combined heat dissipation structure of the PHP and fins. It also discussed the application scopes and advantages of this technology, which provides technical support for further promoting the application of distributed photovoltaic power generation technology.

In response to the limitations of traditional meteorological weather typing and considering the scenario-matching degree of optimization algorithms, RIME algorithm convolutional neural network long short term memory attention（RIME-CNN-LSTM-Attention）photovoltaic output combination prediction model based on generalized scene segmentation（GSS） is proposed. Firstly, guided by scientific and typical principles, a generalized scenario segmentation method based on a generalized scenario segmentation indicator is introduced, providing solution ideas for solving the daily weather similarity and coupling problems existing in traditional classification and aggregation methods, effectively supporting the construction of high-precision prediction models. Secondly, a foundational photovoltaic power output prediction model is constructed by combining the temporal feature extraction capabilities of long short-term memory （LSTM） networks with the spatial feature extraction advantages of convolutional neural networks （CNN） and introducing an attention mechanism. This approach enhances the model’s ability to capture critical information and effectively improves the overall accuracy of the predictions. Additionally, considering the nonlinear characteristics of photovoltaic output under conditions of strong uncertainty, an optimization algorithm based on the physical phenomenon of frost ice is introduced, significantly enhancing the optimization algorithm’s adaptability to complex scenarios. Simulation results show that the GSS-based RIME-CNN-LSTM-Attention combination prediction model can effectively improve the accuracy of photovoltaic output predictions, demonstrating high predictive accuracy and practicality in scenarios where photovoltaic output is highly volatile.

The nearshore floating photovoltaic （PV） power systems effectively address the contradiction between the need of PV power development and limited land resources, offering significant economic and social benefits. However, such systems are still in the early exploration stage and lack systematic research. This study utilizes PVsyst and SolarPV simulation software along with AQWA hydrodynamic simulation results to establish models of floating PV systems under various typical daily time scales in complicated nearshore environments. It comprehensively explored various key factors influencing system performance. A linear regression numerical model was developed based on simulation results, and path analysis was used to compare their impact on system performance gains. The results indicate that sea surface reflectance （37.9%） has the most significant impact on system performance, followed by average tilt angle during floating （30.6%）, installation tilt angle or front-to-rear spacing （17.5%）, and height above sea level （14%）.

This paper theoretically analyzes the current characteristics after a fault occurs in the transmission line of large-scale photovoltaic power generation, and proposes a large-scale photovoltaic transmission line pilot protection method based on the Hellinger distance. The current sampling values after the fault are discretized into probability distributions, and the similarity of the sampling value distribution on both sides is quantified using the Hellinger distance algorithm. The internal and external faults are judged based on the calculation results. The simulation verification shows that the proposed method can reliably respond to the internal and external faults of the protected line, and has good anti-noise and anti-synchronization error capabilities. Compared with the existing similarity protection and traditional pilot current differential protection, the proposed protection principle is more reliable.

A mathematical model of the PV-thermoelectric coupling system is established based on thermal resistance theory. The improved multi-objective particle swarm optimization （MOPSO） algorithm is adopted, with maximum efficiency and output power as the optimization objectives. Simultaneously, the design variables include the cross-sectional area of the hot and cold sides of the thermoelectric element, the height of the thermoelectric element, the load resistance, and the concentrator ratio. The effects of different flow rates of nanofluids on the optimization results are also discussed. The results indicate that maximum efficiency and output power constrain each other and exhibit a non-linear relationship. Furthermore, the optimized performance of the coupled system surpasses that of a single photovoltaic system significantly, with efficiency and output power being maximally improved by 22.44% and 12.33% respectively compared to a single photovoltaic system. Moreover, the impact of increasing the nanofluid flow rate on system performance is not consistently beneficial. The coupled system performance is significantly influenced by the thermoelectric structure. An element height of approximately 1.073 mm is more compatible with this system. The system performance of non-equal cross-section thermoelectric elements is significantly better than that of equal cross-section elements. Maximum efficiency is achieved when S equals 2.27. TOPSIS effectively balances the efficiency and output power of the coupled system. The output power of the optimal compromise scheme increases by 283.01% compared to the efficiency maximization scheme, with only a 14.36% reduction in efficiency.

When using drones to capture infrared images of photovoltaic modules for detection, the images often exhibit complex backgrounds, leading to challenges in detecting small target hot spots, which may result in information loss, false positives, or false negatives. In response to these issues, a novel fusion network（HCF-YOLO） is proposed by integrating the HCF-Net with the YOLOv8n network for detecting hot spot faults in infrared images of photovoltaic modules. This fusion network incorporates a Parallelized Patch-Aware Attention Module（PPA） to enhance the representation of small targets through hierarchical feature fusion and attention mechanisms, ensuring the retention of crucial hot spot information after multiple downsampling steps. Additionally, a Dimension-Aware Selective Integration Module（DASI） is employed to adaptively select and finely integrate high-dimensional and low-dimensional features, thereby enhancing the saliency of small targets. The Powerful IoU（PIOU） is adopted as the loss function for HCF-YOLO, guiding the early-stage regression of predicted boxes along effective paths to improve detection speed. The HCF-YOLO algorithm demonstrates a significant enhancement in detection accuracy, with the average precision at an intersection over union of 50%（AP50） increasing from 89.27% to 97.28% compared to the original YOLOv8n algorithm, achieving a detection speed of 217.33 frame/s. According to the experimental results, the effectiveness of the model is validated.

The complex background interference in electroluminescence（EL） images of photovoltaic modules poses challenges for accurate defect identification. To address this, an improved deep learning model YOLOV5-PMD based on YOLOv5 is proposed. This model utilizes a lightweight convolution GSConv module to replace the Conv part of the Neck, enhancing accuracy while reducing the number of parameters. In addition, the convolutional CBAM attention mechanism is combined to improve the feature perception ability, and the EIoU loss function is replaced by the CIoU loss function to achieve more accurate defect location. On the public SolarMonocrystal dataset, the improved model reduces the training parameters from 7.025×10⁶ to 6.695×10⁶, and the average accuracy of mAP50 reaches 89.8%, which is 2.6% higher than that of the original model.

In order to solve the problem of significant forecasting errors in distributed photovoltaic（PV） output caused by complex urban micro-meteorological condition, a global optimization approach is proposed for the deployment of micro-meteorological monitoring devices, with considering of a comprehensive node criticality metric. Firstly, an integrated electrical-meteorological node criticality index is developed by combining distribution network voltage characteristics with urban micro-meteorological influences. Then, a global optimization model for monitoring device placement is constructed and solved using a genetic algorithm. Case study results demonstrate that the proposed method effectively improves the optimal operation capability of urban power grids with high penetration of distributed PV based on the observability, flexibility, and cost-efficiency of micro-meteorological monitoring systems.

Firstly, four experiments were conducted to assess the properties of acetic acid-resistant ethylene-vinyl acetate copolymer（EVA） film： acid concentration test, high temperature stripping test, volume resistivity test, and electroluminescence（EL） test of small double-glass modules encapsulated with a combination of acetic acid-resistant EVA and ethylene vinyl acetate/polyolefin elastomer（EPE）. Secondly, the reliability of N-type TOPCon solar cells' double-glass photovoltaic modules——encapsulated with acetic acid-resistant EVA and EPE, conventional EPE and EPE, as well as combinations of acetic acid-resistant EVA and EVA——is meticulously examined. The results indicate that the acetic acid concentration in acetic acid-resistant EVA is lower than that in conventional EVA after the PCT test. Furthermore, there is no significant black discoloration observed due to corrosion on the small modules' EL after both the PCT144 h and DH1000 h tests. This suggests that acetic acid-resistant EVA exhibits minimal corrosion effects on TOPCon solar cells under these conditions. The volume resistivity of acetic acid-resistant EVA exceeds 10¹⁵ Ω·cm, thereby meeting the insulation requirements for photovoltaic （PV） modules. The acetic acid-resistant EVA exhibits a stable adhesion to glass at a temperature of 80 ℃, thereby mitigating the risk of delamination under high-temperature conditions. The power degradation of double-glass PV modules encapsulated by acetic acid-resistant EPE and EVA combination and double EPE combination is less than 4% after DH3000 h, PID288 h and TC600 test, which can meet the International Electrotechnical Commission（IEC） standard. Therefore, the combination of acetic acid-resistant EPE and EVA can be utilized to encapsulate double-glass modules, serving as a replacement for the conventional EPE and EVA combination. This approach effectively meets the demand for encapsulation film particles while contributing to cost reduction.

The Solar-Tracking Astrophysics Theory is presented as an extension and refinement of the author’s earlier work, “Physics Theory of Active Dua-Shaft Tracking”, published in Acta Energiae Solaris Sinica （November 2018）. This article suggests that solar-tracking photovoltaic power stations in geosynchronous orbit may, within the next two to three decades, independently meet the entire global demand for electricity.

The layout methods and optical efficiency calculation theory of the heliostat field in the solar power tower plant were investigated. A new optimization and design method based on no-blocking radial stagger layout was proposed. An optical model of heliostat field was established and validated. The heliostat field of the Gemasolar power plant in Spain was redesigned by usingthe new optimization method The optical performance of the heliostat field was analyzed.Comparing to the original heliostat field, the annual average optical efficiency of the new heliostat field is increased from 61.62% to 63.14%, and the annual maximum optical efficiency is increased from 66.87% to 68.45%. Furthermore, the heliostat field density is improved from 20.42% to 27.36%. The optical performance of the heliostat field is enhanced. The feasibility of the new optimization and design method is validated.

This study explores the distinct forms in which water molecules exist within hydrogel structures and discusses the design of solar evaporators that enhance water evaporation efficiency by modifying the surface morphology of hydrogels. Finally, the research progress on solar evaporators that combine various photothermal media with hydrogels is systematically reviewed, and prospects for their future development are discussed.

Aiming at the climate characteristics of extreme hot and humid climate region, this study constructed a solar solution dehumidification system （LDDS-SC） with a self-circulation structure of dehumidification/regeneration. The coupling relationship between system dehumidification and solar light/thermal area was comprehensively considered, and the numerical solution was carried out by NTU-Le model. The system dehumidification amount per unit solar collector/PV panel area under different dehumidification and regeneration self-circulation reflux ratio （R_d,R_r） matching conditions is obtained. The results show that under typical working conditions （m_a=1800 m³/h,m_s=4.0 kg/s）, the maximum dehumidification capacity, the system dehumidification capacity per unit collector area and the system dehumidification capacity per unit photovoltaic panel area are about 2.5 times, 15.1 times and 6.2 times of the conventional system, respectively. The optimal dehumidification and regeneration self-circulation reflux ratio of the system is obtained by TOPSIS multi-objective decision-making method as （R_d,R_r） is （0.9,0.1）, and the system dehumidification amount is 4.61 g/s and the system dehumidification amount per unit total area is 0.28 g/（s·m²）. LDDS-SC system shows better dehumidification and energy saving potential than conventional solution dehumidification system.

Taking the complementary power generation system of a 660 MW coal-fired unit assisted by solar thermal energy as the research object, a dynamic simulation model was established for the solar hybrid coal power generation system. Based on this, the impacts of the molten salt storage and solar energy integration on the dynamic response of the solar hybrid coal power generation system were discussed through 6 typical operational schemes. The results show that： under safe operating conditions, comparing the three load reduction schemes, the peaking capacity and load changing rate of solar hybrid coal power generations system are the largest under the operation condition of main steam being extracted and exhaust steam entering the condenser, which can reach 56.5 MW and 6.7%P_e/min respectively; comparing the two system load increase schemes, the molten salt heating feedwater bypass scheme demonstrates superior peaking capacity （up to 32.4 MW）, while the molten salt heating condensate bypass scheme exhibits a higher load changing rate （up to 8.45%P_e/min）. Comparing the change of standard coal consumption rate under three different solar heat introduction complementary system schemes, the use of molten salt heating boiler feedwater coal saving effect is the best, the standard coal consumption rate can be reduced by 25.47 g/kWh at most, and the higher generated power of solar hybrid coal power generation system, the greater the coal saving rate.

Based on the theory of random sampling, a ray-tracing model for predicting the wind-induced vibration of the mirror surface and its concentrating distribution is developed. A transient multi-physics approach is employed, integrating the flow field, structure, and optics. The model takes the solar position, heliostat elevation angle, and azimuth angle as variables, and analyzes the variation in the concentrating distribution of the mirror surface under pulsating wind loads. Pulsation, time-history, and Gaussian characteristics of key physical quantities such as concentrating efficiency, net wind load, and mirror displacement are also examined. The results show that wind-induced vibrations cause the heliostat’s concentrating efficiency to decrease by 8.26% to 19.48%, with the maximum efficiency loss occurring at noon. For every 1 mm increase in mirror displacement, the concentrating efficiency loss increases by about 2.87%. As wind speed increases, the net wind pressure and mean displacement exhibit a positive correlation, while concentrating efficiency follows a negative correlation. The fluctuation patterns of net wind pressure, mean displacement, and mean concentrating efficiency display instantaneous synchronization.

On the basis of evaluating the potential of offshore wind, solar and wave energy resources in China, the complementarity assessing index for multiple renewable energy resources are defined and applied to assess the complementarity of wind, solar and wave energy resources. Meanwhile, the installed capacity ratios are optimized to obtain the optimum complementarity effects. Analysis results reveal that the wind, solar, and wave energy resources are abundant over sea areas in China and show complementarity potential. In most areas of Bohai Sea, Yellow Sea and South China Sea, the complementarity of wind-solar-wave hybrid energy system is larger than the complementarity between two renewable energy resources. Compared with wind-solar, wind-wave, and solar-wave hybrid energy system, the synthetic fluctuation index of power output of wind-solar-wave hybrid energy system is reduced by 86.0%, 38.5%, and 10.4%, respectively.

In order to solve the problem of voltage fluctuation caused by high proportion of new energy connected to the power grid, the paper introduces a flexible on-load voltage regulating transformer（FOVRT） topology. Through FOVRT, the coordination of voltage regulation and reactive power can realize the efficient utilization of reactive power regulation equipment in the power grid. Firstly, the working principle and mathematical model of FOVRT are analyzed, and then a multi-time scale voltage reactive power dynamic optimization model with FOVRT is proposed. According to the forecast data of distributed power supply and load, the optimal adjustment scheme of discrete compensation equipment in different time periods is obtained. Combined with FOVRT continuous stepless voltage regulation capability, minute level reactive power optimization is carried out considering distributed power supply and FOVRT reactive power output in intra-day stage to achieve the optimal network loss and node voltage deviation. Finally, the improved IEEE 33-node distribution network is simulated, and the simulation results verify the validity and rationality of the proposed model and method.

To solve the collaborative optimization problem of source-side energy supply coupling conversion and load-side energy demand transfer substitution under various energy sources, this paper proposes a source-load collaborative optimal scheduling model for an electricity-heat-gas-hydrogen-storage system under a tiered carbon trading mechanism and multi-energy demand response conditions. Firstly, based on the energy flow model, a flexible response model is proposed on the source side, constructing a hydrogen-mixed combined heat and power unit model with an adjustable heat-to-power ratio, and utilizing a two-stage power-to-gas process for dual-layer energy optimization. Secondly, on the load side, considering the characteristics of flexible loads, a spatio-temporal two-dimensional multi-energy demand response model is established under a tiered carbon trading mechanism. Finally, by handling uncertainties using the Frank-SJC-Copula, renewable energy output and multi-energy load forecasting curves are obtained. A day-ahead scheduling model is established with the objective of minimizing the total operating cost of the system, and the feasibility of scheduling under multiple scenarios and multi-energy configurations is verified through case studies based on time-of-use electricity and gas prices. The results show that the proposed electricity-heat-gas-hydrogen-storage collaborative optimization model can effectively optimize the system’s low-carbon and economic performance and reduce wind and solar curtailment.

Considering the influence of DC-link voltage control（DVC）, the stable operation and synchronization control strategy of grid-following converters under weak grid conditions are studied. Firstly, a large disturbance model of grid-following converters with consideration of DVC is established. Based on this, by analogy with the rotor angle oscillation equation of traditional synchronous generators, the damping ratio of the system is derived, reflecting the transient instability mechanism and influencing factors under large disturbances. The existence and stability of the system equilibrium point are analyzed by drawing the power-current curve of the system. Then, a transient stability enhancement method for GFL-VSC based on phase-locked loop adaptive parameter, power angle compensation, and variable angular velocity is proposed. Finally, the simulation results of Matlab/Simulink prove the accuracy of the transient stability analysis and the effectiveness of the proposed control strategy.

This paper proposes a constant voltage and frequency （VF） control strategy for the photovoltaic-storage-charging islanded microgrid under communication network. This strategy is a master-slave control structure,the master controller adopts super twisting sliding mode control （STSMC） to maintain voltage stability. The slave controller adopts sliding mode control （SMC） to transmit and share current data using CAN and ZigBee networks for power distribution. The maximum allowable delay bound （MADB） is calculated using a bisection iterative algorithm, and the effect of time delay greater than MADB on the system is analyzed. The simulation results show that STSMC has smaller control error, shorter response time and stronger anti-delay ability than PI and SMC.

To minimize the comprehensive power generation cost of microgrids, this article undertakes a study focusing on the optimization configuration of microgrid capacity, employing the improved sparrow search algorithm （ISSA）. A capacity optimization model is formulated, with the annual comprehensive cost of microgrids serving as the objective function. This model incorporates various factors, such as price-based demand response, the levelized cost of energy storage systems, and time-of-use electricity pricing. Addressing the challenges of inadequate convergence accuracy and susceptibility to local optima in the traditional sparrow search algorithm, this study introduces an enhanced version of the algorithm. Initially, a chaotic reverse learning strategy is adopted to ensure a uniform distribution of the initial population. Subsequently, the golden sine formula is integrated to enhance the algorithm's convergence accuracy. Additionally, dynamic weight factors are introduced to improve the algorithm's dynamic performance and bolster its global search capabilities. Lastly, a mixed mutation perturbation strategy and a greedy strategy are employed to facilitate escaping from local optima. To validate the proposed ISSA, a comparative test is conducted against the sparrow search algorithm, grey wolf algorithm, and whale algorithm. The results demonstrate that ISSA exhibits superior convergence accuracy and faster convergence speed. Utilizing actual data from a specific region for numerical simulation, the calculated comprehensive power generation cost is reduced by 6.39%, 7.35%, and 11.68% when compared to the other three algorithms, respectively. This underscores the effectiveness and superiority of the ISSA in optimizing microgrid capacity configuration.

To address the challenges of energy consumption and operational safety in grain storage buildings, a solar energy-ground source heat pump system model was developed using TRNSYS, with the China Grain Reserves Ganzhou Depot selected as a case study. The accuracy of the model was validated against actual operational data from the storage facility. Based on the simulated performance of the solar energy-ground source heat pump system during the cooling season, comprehensive analyses of the system’s electrical, thermal, and exergy characteristics were conducted. The results show that, under the most unfavorable operating conditions, the photovoltaic panels and the refrigeration unit exhibit the highest exergy losses among all system components, accounting for 54% and 36% of the total exergy loss, respectively. The fan coil units demonstrate the highest exergy efficiency at 69.64%, while the refrigeration unit shows the lowest efficiency at 26.37%. The thermodynamic perfection of the fan coil units reached a maximum of 0.96, whereas the photovoltaic panels exhibit the lowest value of 0.47. These findings indicate that the photovoltaic panels and the refrigeration unit have substantial potential for performance optimization. The overall exergy efficiency of the system is affected to be influenced by variations in outdoor air temperature. During cooling operation, the solar energy-ground source heat pump system enables the grain storage warehouse to achieve near-zero energy consumption.

The impedance model of LCL single-phase grid-connected inverter is established, and the mechanism of phase reduction of inverter output impedance caused by PLL is analyzed. In order to improve the influence of phase-locked loop on system stability, a multi-objective constrained power grid voltage feedforward control strategy is proposed. Firstly, a positive impedance is obtained by designing the transfer function of the voltage feedforward channel at the common coupling point to offset the negative impedance introduced by the phase-locked loop. Secondly, in order to reduce the difficulty of engineering implementation of feedforward control strategy, the original feedforward transfer function is simplified by establishing multi-objective constraints from the aspects of amplitude-frequency deviation and phase-frequency deviation in basic frequency band and middle frequency band, and an approximate equivalent feedforward transfer function is designed. And the stability analysis is presented. Simulation and experiment verify that the proposed method effectively enhances the adaptability of grid-connected inverter in weak grid.

This paper proposes a multi-agent benefit distribution method for virtual power plants, incorporating two major improvements to the traditional Shapley value approach. Firstly, a correction factor is introduced to adjust the classical Shapley value by comprehensively accounting for each agent’s resource input, risk exposure, and contribution to emission reduction within the coalition. Secondly, a reinforcement learning-based sampling method is employed to approximate benefit distribution, effectively addressing the “combinatorial explosion” problem caused by a large number of participating agents. The proposed model is applied to a case study of a virtual power plant, demonstrating enhanced computational efficiency without compromising accuracy.

In this paper, the problem of high communication burden in the secondary control of islanded microgrids is investigated, and a new dynamic event triggering mechanism is proposed to schedule the communication between microgrids not only to regulate the voltage and frequency that cannot be controlled by the drop control to the desired values, but also to avoid the use of global information. Compared with the existing research results, the linear matrix inequality is used to design the ideal triggering mechanism and control protocol, and the proposed triggering mechanism involves internal dynamic variables, which dynamically adjusts the triggering thresholds, enlarges the lower limit of the event triggering interval, and drastically reduces the number of communication times among the controllers. Since the control scheme is fully distributed, there is information exchange between each generating unit only with its neighbours, which reduces the requirements of the communication network and improves the stability of the system. These internal dynamic variables play an important role in excluding Zeno behaviour. Finally, the argumentation is obtained through simulation.

Considering the demands of clean and efficient heating with grid peak shaving and valley filling, a novel building heating system combined with photovoltaic/thermal, heat pump and heat storage using off-peak electricity （PV/T-HP-VEHSH） was proposed. The optimization design method for PV/T-HP-VEHSH was developed targeting optimal heating reliability, minimal carbon emissions and initial investment based on the pattern search algorithm. The operating performance of PV/T-HP-VEHSH was studied by TRNSYS software and the performance improvement was also investigated in comparison with the photovoltaic-air source heat pump heating system and the heating system without thermal storage by off-peak electricity. The results show that the PV/T-HP-VEHSH has the optimal comprehensive benefits at the ratio of flow rate of the thermal collection water pump to PV/T area of 13.8 （kg/h）/m², the ratio of volume of thermal collecting tank to PV/T area of 0.1 m³/m², and the ratio of electric boiler capacity to volume of thermal storage tank of 1.2 kW/m³. The average monthly COP of PV/T-HP-VEHSH varies from 2.8 to 3.2 and the comprehensive photoelectric and photothermal efficiencies of PV/T are higher than 56% during the heating season in cold zones. The operation cost of PV/T-HP-VEHSH is reduced by 3.3 ¥/m² due to the thermal storage using off-peak electricity.

As the basis of transformer range lean management, the precision of distribution （for short of 400V distribution） user-branch-distribution transformer topology draws more attention. Based on the characteristics of distribution tree topology, the principles and defects of current distribution topology identification methods were analyzed with the correlation among electrical parameters. A novel distribution topology identification method based on time-frequency domain data fusion was proposed, which consists of two links： 1） Forward link. Spectrum clustering algorithm was modified to cluster node voltages including fundamental and harmonic domains level by level.Furthermore, in order to calculate the node voltage vector corresponding to the upper level topology branches, a Confined-field Neural Network was devised, which is able to discriminate the topology type （radiation or trunk） via cluster node transformation group features. 2） Backward link. In the solution space compressed by the forward link, the active power balance principle was utilized to check and correct the suspicious nodes. Such closed loop identification framework possesses promoted result precision and sophisticated topology applicability. Finally,3 residential/commercial and industrial pilot transformer range with typical topology in State Grid Zhejiang Company were selected as examples. The proposed method was paralleled with several current distribution topology identification methods, with its advantages verified.

Based on the PEM hydrogen production model, develop models for both PV and PEM hydrogen production systems, and construct experimental setups for photovoltaic and photothermal PEM water electrolysis （PVPT-PEM）with various coupling configurations to explore the operational strategies of PV and photothermal-coupled PEM hydrogen production systems. Simulation and experimental results show that the hydrogen production rate of the direct-coupled PV system varies with solar irradiance. Under the same operating conditions, this system shows lower losses but suffers from reduced hydrogen production stability. In contrast, the indirect-coupled system is less affected by weather conditions, allowing for stable and continuous hydrogen production, although with increased system complexity. The independent photothermal system more effectively maintains the electrolyte temperature within the range of 50-80 ℃.

Consequently, a distribution network false data injection attack detection method based on attention mechanism and bidirectional long short-term memory network is proposed. Initially, the measurement data gathered by power system is broken down through variational mode decomposition, introducing the idea of signal energy to identify the most suitable mode number for variational mode decomposition. Subsequently, a small disturbance attack vector is constructed. Reconstruction of the measurement data using the Pearson correlation coefficient to filter out the high-frequency noise in the measurement data. Ultimately, a bidirectional long short-term memory network detection model based on the attention mechanism is designed. The reconstructed time series measurement data and attack vector are used as training set input and verified using the test set. The proposed method is verified by comparative experiments. The results show that this method can effectively improve the detection accuracy of false data injection attacks in distribution networks. This helps enhance the ability of the distribution network to resist attacks and ensures the safe operation of the power system.

To achieve mutual benefit between integrated energy systems （IES） and Vehicle-to-Grid（V2G） charging stations, a bilevel optimization method based on a leader-follower game under uncertain factors is proposed. Firstly, the IES is set as the upper-level leader and the V2G charging station as the lower-level follower. The objective of the upper level is to minimize the operating cost of the IES by formulating time-varying charging and discharging prices to guide the lower-level users in adjusting the charging and discharging power of the V2G charging station. The lower level aims to minimize the total charging cost of the V2G charging station and responsed based on the price information set by the upper level, thus constructing a leader-follower game model. Then, based on the multi-scenario method, the conditional value-at-risk （CVaR） is introduced to measure the risk loss caused by the uncertainty of wind and solar power output on system operation. Using the Karush-Kuhn-Tucker conditions and strong duality theorem, the original bilevel game model is transformed into a single-level mixed-integer linear programming problem to compute the Nash equilibrium solution, thereby obtaining the optimal operation and pricing scheme. Results show that the proposed method can balance the economic interests of different entities within the IES and V2G charging stations, achieve a balance between operating costs and risk costs, ensure the consumption of renewable energy and promote carbon reduction.

To address the issues of the inability of a single electricity price strategy to meet the problem of adequate dispatch of multiple flexible loads, this paper proposed a multi-load master-slave game optimization strategy considering peak shaving rewards and fine load regulation. firstly, according to the maximum peak cutting contribution of each flexible load, differentiated ladder reward and punishment mechanism was established. secondly, a multiple flexible master-slave game optimization model was constructed in which the microgrid operator is taken as the leader, and the customer aggregator, and the air conditioner aggregator with fine temperature control, and the electric vehicle aggregator with travel stochasticity are taken as the follower. finally, a numerical example shows that the proposed strategy can effectively tap the peaking potential of a variety of flexible loads, and based on this, achieve multi-benefit improvement for microgrid and a variety of flexible loads.

A model-free predictive control strategy based on fuzzy current observation is proposed to address the problem that parameter mismatch can lead to bus voltage deviation when applying model predictive control algorithms to a dual active bridge DC-DC converter （DAB） in a DC microgrid. Firstly, the periodicity of sinusoidal function is used to design a sliding mode observer with multiple stable points for fuzzy observation of output current, which effectively improves the response speed of the observer. Then, the observed value of the sliding mode observer is substituted for the current value in the predictive model of the converter, and it is found through parameter sensitivity analysis that the proposed method can eliminate the steady state error caused by the parameter mismatch of the predictive control model. Finally, a system simulation model and a prototype platform are built to verify the effectiveness of the proposed method.

This paper investigates the impact of photovoltaic power optimizers on building integrated photovoltaics（BIPV） systems under shading mismatch conditions. First, three different string shading scenarios were designed. Based on the shading ratios on the module surfaces are known, simulations were conducted to compare the power generation with and without the optimizers. Then, an experimental platform was set up to compare the simulated data with the measured data, verifying the accuracy of the simulation model. Finally, the BIPV glass curtain wall of the Antuoshan Photovoltaic Building in Shenzhen was taken as the study object. A comprehensive shading analysis was performed on each module over the course of a year （8,760 hours）. The system power generation was discussed under three conditions： without optimizers, with optimizers installed only in shaded areas, and with optimizers installed on all modules. The results indicate that the power optimizer's impact on the power generation of building photovoltaic systems is influenced by shading. In months when the strings are unevenly shaded, installing power optimizers can increase power generation by up to 9.48%. Additionally, when all modules are equipped with optimizers, the overall system power generation increases by 1.13%.

In order to improve the utilization efficiency of solar energy by traditional collector-heat storage walls in cold regions, this study takes a village house in Xinjiang Shihezi region as the research object, builds a set of photovoltaic collector-heat storage wall system, and tests the thermoelectric performance of the photovoltaic collector-heat storage wall system in the village house in cold regions under the winter climate. The results show that compared with the traditional collector-heat storage wall, the heat collection effect of the PV collector-heat storage wall system is obviously better than that of the traditional one, the average heat gain of the room of the PV collector-heat storage wall system is 51.41 W/m², and that of the room of the traditional collector-heat storage wall is 47.32 W/m², and the average heat gain of the room of the PV collector-heat storage wall system is 4.09 W/m² higher than that of the traditional collector-heat storage wall, which is the same as that of the traditional collector-heat storage wall. 4.09 W/m²; the electric efficiency of the PV collector-heat storage wall system fluctuates between 6% and 11%, the energy efficiency fluctuates between 7% and 26%, and the exergy efficiency fluctuates between 5% and 11%. The results of the experimental research in this paper have certain guiding significance for the application of photovoltaic collector-thermal storage wall system in the severe cold region.

This paper conducts a comprehensive investigation into both small-disturbance and large-disturbance stability of the GFM-DFIG system, focusing on the influence of short-circuit ratio （SCR） and key system parameters. Firstly, a full-order small-signal model of the GFM-DFIG is developed, and root locus analysis is employed to reveal the impacts of the grid-forming link, current control link, and SCR on the system’s small-disturbance stability. Based on this full-order model, a reduced second-order model is then derived to analyze large-disturbance stability. The equal-area criterion is used to rapidly determine the critical clearing conditions and evaluate how variations in SCR and system parameters affect the large-disturbance stability margin. Finally, simulation studies are conducted to validate the theoretical analysis. The results confirm that the identified stability characteristics provide valuable insights for parameter tuning and optimal design of GFM-DFIG systems.

To address the issue of inadequate long-term flexible adjustment capacity caused by the frequent occurrence of wind power ramping events in the new power system, a modeling method for the annual output sequence of wind power considering the ramping characteristics is proposed. This method enhances the precision of system flexibility assessment over medium- and long-term time horizons, thereby providing guidance for the planning and development of flexibility resources. Initially, wind power ramping segments are meticulously filtered and extracted using the Spinning Door algorithm and the Four-Point method. Subsequently, the daily wind power output ramping characteristic indices are identified by analyzing the morphology and distribution properties of these ramping segments. With these indices in hand, the K-medoids clustering algorithm and kernel density estimation are employed to establish a probabilistic distribution model for the daily wind power output ramping features. This enables the generation of a daily wind power output sequence that encapsulates both ramping characteristics and time series attributes, achieved through Monte Carlo sampling and sequence optimization techniques. In the final step, the Markov Chain Monte Carlo （MCMC） method is utilized to simulate the impact of meteorological conditions on the transition characteristics between daily wind power output sequences, thereby creating annual wind power output sequences that accurately reflect both the stochastic nature and ramping characteristics of wind power. The simulation is grounded in wind power output data from a province in Northwest China. The resultant annual wind power output sequences are then applied to assess the long-term flexibility of the system, validating the efficacy and practical applicability of the proposed methodology.

The monopile foundation of offshore wind turbines is subjected to cyclic loads such as wind, waves and currents for a long time, resulting in excessive cumulative deformation of the pile foundation. The monopile foundation is strengthened in the shallow layer with cement-soil to form a composite pile, so as to achieve the purpose of reducing the cyclic cumulative deformation. To research the cyclic behavior of monopile in cement-improved clay, a series of model tests on the lateral bearing capacity of monopile in cement-improved clay under lateral cyclic loading were carried out. The variation characteristics of monopile bearing capacity, pile bending moment, accumulated deformation, cyclic unloading stiffness and cyclic bending moment before and after reinforcement were analyzed. The test results show that the initial stiffness and ultimate bearing capacity of single pile are significantly strengthened by cement-improved soil, and the maximum bending moment of pile is reduced, which improves the overall load performance of pile. Regardless of the reinforced or the unreinforced pile, with the increase of cycle times, the cumulative displacement of pile top cycle increases rapidly initially, and then gradually stabilizes. Cement-soil reinforcement can effectively improve the horizontal stiffness of pile foundation, but it cannot change the law of attenuation with the increase of circulating load amplitude. There is a significant accumulation of the bending moment of the unreinforced pile, but the cumulative effect of the decrease of the reinforced pile can be ignored.

This article establishes a new type of life prediction model to address the issue of large prediction errors in the remaining life of wind turbine bearings. This model considers the randomness of the failure threshold for life prediction, uses the maximum likelihood method to estimate the parameters in the model, and updates the parameters based on Bayesian theory. At the same time, considering that the errors of the prediction model itself will accumulate over time, which will affect the accuracy of life prediction, an error correction model is established, and the distribution of its remaining service life was solved. The prediction model established in this article was used to predict the remaining life of wind turbine bearings, verifying the effectiveness of the proposed strategy.

Field data monitoring and analysis were carried out for a 4 MW offshore wind turbine at a nearshore wind farm in Jiangsu Province. An integrated model of the wind turbine-tower-foundation was established by finite element software and compared with the base-tower model. The models’ reliability was verified based on long-term monitoring data for the project. The effects of structural dimensions, foundation conditions, and wind turbine operating states on the natural frequency of the structure were analyzed based on the integrated model. The results show that the natural frequency of the wind turbine obtained from the integrated model is closer to the actual monitoring values compared to a simplified model, and simplifying the upper wind turbine structure leads to an overestimation of the natural frequency. The monitoring results of the natural frequency of the structure are not significantly affected by the monitoring elevation and can be used to assess the wind turbine's operational status based on the consistency of natural frequencies at different elevations. As the foundation embedment depth, pile diameter, soil elastic modulus, and internal friction angle increase, the natural frequency of the structure also increases. With an increase in the tower height, the natural frequency of the structure linearly decreases, and an increase in the tower wall thickness leads to a parabolic increase in the natural frequency of the structure. Changes in the friction coefficient between piles and soil have minimal impact on the natural frequency of the structure. Wind turbine operation increases the low-order natural frequencies of the structure, with the most significant impact on the first two natural frequencies.

In order to further investigate the influence between the tooth root crack excitation and the dynamic characteristics of the wind turbine gear-bearing coupling system, a quantitative calculation of the time varying meshing stiffness（TVMS） of cracked gears and a coupled system vibration solution method considering the slicing coupling effect are proposed. Taking the high-speed stage of a certain type of wind power gearbox in an enterprise as the research object, and comprehensively considering time-varying factors such as the dynamic meshing force of gear pairs and the dynamic supporting force of rolling bearings, a dynamic model of crack faults in the wind power gear-bearing coupling system was established. The influence of tooth root cracks on the TVMS of gears and the dynamic characteristics of the system was analyzed, and the correctness of the model was verified through bench tests. The results show that： the gear TVMS correction algorithm considering the slicing coupling effect can better characterise the dynamic change of the gear meshing stiffness under crack failure, the tooth root crack causes the gear TVMS to decrease, and ignoring the coupling effect between the slicing gear teeth may overestimate the effect of the tooth root crack depth on the gear TVMS. There is a certain correlation law between the root crack excitation and the dynamic contact load of the rolling body in the bearing, with the increase of the root crack depth, the amplitude of the dynamic contact load of the rolling body also increases, and the range of the bearing load carrying area tends to decrease; When cracked gears are involved in meshing, the reduction of TVMS will cause the vibration displacement of the coupled gear-bearing system to produce a large cyclic shock response, and produce the phenomenon of side bands near the meshing frequency, ignoring the coupling effect between the sliced gears and teeth, or simplifying bearings to stiffness damping matrix may underestimate the vibration response of the coupled system due to gear crack excitation.

In order to accurately characterize the winding asymmetry fault in a doubly-fed induction generator, this study investigates the effect of spatio-temporal wind speed variations （caused by wind shear and tower shadow） based on actual wind conditions across the rotor swept area. Firstly an equivalent wind speed mode that accounts for the spatio-temporal variations of wind speed was develop-ed, and investigates the corresponding stator current characteristics based on this model was investigated. It then analyzes the impact of key wind speed distribution parameters, including the wind shear exponent, tower radius, and hub offset, on the stator current behavior. Also the influence of wind speed distribution difference parameters （wind shear index, tower radius and center distance） on the stator current characteristics was analyzed. Subsequently, simulation verification was conducted using the MATLAB/Simulink platform, and experimental verification was performed on a doubly-fed wind turbine fault simulation platform. The results show that, compared with the existing fault characteristics based on the average wind speed at the hub, the modulation frequencies of 3kP （k is an integer, P is the rotation frequency） appear on both sides of the stator current fundamental frequency and its third harmonic when wind speed spatio-temporal distribution differences are considered. Different wind speed distribution parameters affect these characteristic frequencies to varying degrees. The research results improve the fault diagnosis theory system of doubly-fed wind turbines and have a certain theoretical significance for practical fault diagnosis.

Bucket foundation has become one of the important foundation forms for offshore wind power in China due to its advantages of low cost, short construction period, and relatively small disturbance to the foundation. There is still a lack of strict theoretical solutions and comprehensive calculation methods for the vertical ultimate bearing capacity of bucket foundations in layered soil foundations. This article uses the finite element limit analysis method to establish a lower bound solution program for the vertical bearing capacity of bucket foundations in layered foundations and verify its accuracy. Conduct in-depth research on the failure mode and bearing characteristics of bucket foundations in layered soil foundations. The results indicate that the ultimate bearing capacity of the foundation is related to the relative thickness of the sand layer H/D, the clay layer su, and the sand layer φ There is a positive correlation between the length to diameter ratio d/D of the bucket foundation. The critical thickness of sand Hcri varies with the upper layer of sand φ and increases with the decveast of the mderlying clay su.

To address the issue of calculating negative skin friction for wind turbine pile foundations in collapsible loess areas, this paper conducts large-scale indoor model tests and compiles field test data from similar projects. After investigating the relationships between the collapsible settlement depth of loess after complete immersion, the depth of the neutral point, the depth of maximum negative skin friction, and the pile length-to-diameter ratio, expressions for the depth of the neutral point and the depth of maximum negative skin friction in terms of collapsible settlement depth and pile length-to-diameter ratio are derived through linear fitting. On this basis, a method for calculating negative skin friction of wind turbine pile foundations in collapsible loess areas is established. The results calculated by the proposed method and the method specified in the pile foundation code are compared with the measured results respectively. The findings indicate that the downward pull load calculated by the method in this paper is closer to the actual situation than that obtained using the pile foundation code. This achievement can provide a reference for the design of similar pile foundation projects.

In this paper, a newly coupled meso-micro-scale technique is constructed based on the mesoscale Weather Research and Forecasting （WRF） model and Computational Fluid Dynamics （CFD） technology, which is applicable to the simulation of unsteady flow in wind farms in complex terrain. In particular, due to the different terrain resolutions used in mesoscale and microscale simulations, the paper proposes a meso-microscale terrain blending method to ensure high fidelity in the cross-scale transfer of boundary data. Taking two wind farms in Shanxi and Yunnan as examples, the impact of the terrain blending method on mesoscale data transmission is firstly analyzed, then the impact of the terrain on the wind profile in complex mountain wind farms is investigated, and finally, the strategy of wind turbine layout in wind farms is analyzed. The results show that the proposed method can effectively reflect the influence of mesoscale atmospheric conditions and microscale topography on wind resource distribution; compared with the WRF simulation method, the WRF-CFD method can control the mean absolute error of wind speed simulation within 1 m/s. The method can be further utilized for the load evaluation and operation control of wind turbines in complex terrain.

In order to understand the actual fatigue damage and remaining service life of wind turbines, it is crucial to conduct fatigue assessment of wind turbines based on vibration monitoring. Through long-term monitoring of tower bottom strain of a 1.5MW wind turbine, a new method for real-time prediction of fatigue damage was proposed based on the traditional four-point rainflow cycle counting algorithm. Traditional methods only considered stress cycles within subsets and ignored transition cycles between subsets. The new method proposed in this article predicted the current fatigue damage by concatenating residue series that could not form complete cycles in the past stresses with newly measured stresses. The new residue series were then stored and combined with subsequent measured stresses to evaluate the fatigue damage of wind turbines in real time. The fatigue damage obtained by using this method was identical to the results obtained by using known data as a continuous sequence, proving the accuracy of this method. At the same time, in this method only the residue series of historical data need to stored, which occupy less memory, this article also proveds that the accuracy of this method is not affected by the calculation window length. This provides a solution for online real-time prediction of fatigue damage in wind turbine structures.

To enhance the load-bearing capacity and operational stability of wind turbine gearbox, this study investigates influence of multiple errors on the load sharing of compound planetary gearbox. A dynamic load sharing performance uncertainty analysis method for compound planetary gearbox of wind turbine under the influence of multiple errors is proposed in this paper. Firstly, the lumped parameter method is adopted to establish the dynamic load sharing performance analysis model of the compound planetary gearbox. Specifically, the mapping relationship between the multiple errors and the load sharing performance is apprehended. Secondly, the second-order derived λ-PDF is applied to model the uncertainty of the aforementioned errors, enabling a unified measurement of multiple errors under different distributions. Thirdly, the dimension reduction integration method is employed to solve the high-dimensional dynamic load sharing performance analysis model of the compound planetary gearbox. Finally, the second-order derived λ-PDF is used to precisely quantify the uncertainty of the load sharing performance of compound planetary gearbox. The example results indicate that the proposed method can effectively decompose the dynamic load sharing performance analysis problem containing multiple uncertain parameters into the dynamic load sharing performance analysis problem with multiple single uncertain parameters. Compared to the Monte Carlo method involving the large-sample statistical simulation, the proposed approach reduces the computational complexity of dynamic load sharing performance analysis while ensuring computational accuracy.

Gearbox is one of the important element for wind turbine transmission system. To analysis the overall reliability of the gearbox transmission system, the gearbox transmission system of a 2 MW wind turbine which operate in the Dabancheng wind farm in Xinjiang was studied. A stochastic wind speed model as the external excitation was established by using the annual wind speed data of the wind farm. A gear-axle-bearing dynamics model of the transmission system was referenced to solves the meshing force of gear pairs by taking time-varying mesh stiffness and transmission error as the internal excitation. The Gamma strength degradation model and correlation function of each gear were established by considering the failure modes of contact and bending. Based on introducing the influence of time variable into the basic Copula model and selecting multiple Copula functions, the corresponding mixed Copula model was developed according to the joint distribution of different gear performance functions. In order to obtain the dynamic reliability of each gear part in the transmission system, a genetic algorithm was used to solve the unknown parameters in the mixed Copula model. The results of study has significant instruction for the maintenance of wind turbines.

Dynamic stall induced by flow separation during the rotation of wind turbine blades can make the aerodynamic forces deteriorate drastically. In order to improve the dynamic stall characteristics of the airfoil, an aerodynamic optimization framework of the airfoil based on a surrogate model is established. The S809 airfoil is taken as the research object. First, the airfoil is parametrically characterized, and the input vectors of the model training samples are obtained by Latin hypercube sampling, and the corresponding response values are solved by CFD method. Secondly, the surrogate model of aerodynamic shape and aerodynamic force of the airfoil is constructed by Gaussian process regression, and the model accuracy is improved by the infill point criterion. Finally, with the objective of reducing the aerodynamic fluctuations, the aerodynamic forces of one pitch cycle are optimized and designed by combining the global optimization algorithm. The results show that compared with the baseline airfoil, the change of aerodynamic shape of the optimized airfoil significantly reduces the drag and moment fluctuations, and effectively suppresses the dynamic stall in the airfoil leading edge during the pitch-down phase.

In response to the lack of predictive accuracy and error processing of the existing predictive model when dealing with the severe fluctuations of wind power, this work proposes a mixed wind power prediction method based on error -based modification, combining the experience modus of the time -based filter to decompose TVFEMD, TVFEMD, and Support vector return to SVR and two -way long -term memory neural network BiLSTM. First of all, the original wind power data is decomposed into this modulus function IMFS by applying TVFEMD to achieve the purpose of eliminating its complexity and uncertainty. Then, the improved grid search algorithm and cross -verification algorithm （GridsearchCV） optimize the support vector regression model, and use the model to predict the decomposed IMFS. Secondly, use a improved sparrow search algorithm （SSA） to optimize the BiLSTM network construction error correction model, predict the prediction error of support vector regression, and superimpose the prediction results with those of support vector regression to obtain a more accurate final prediction result. This method not only improves the accuracy of wind power prediction, but also provides a more reliable basis for the fields of wind power power generation. Compared with the results of the prediction model constructed under other optimization methods, this model has higher predition accuracy and stability.

To explore the reasons for frequent tower cylinder buckling phenomena in the wind power industry, this article takes the European EC3 shell structure code, which is the most commonly used calculation method for tower buckling, as the research object. It analyzes the similarities and differences between the new and old versions of this code in terms of their methodologies for calculating buckling capacity. A quantitative analysis is conducted on key parameters such as manufacturing quality and buckling reduction factors used in the calculation methods outlined in both codes. The study reveals significant differences in the formulas for calculating the meridional buckling capacity between the new and old EC3 Shell Structures Codes. Furthermore, it highlights that manufacturing quality has a substantial impact on the buckling capacity of wind turbine towers. Notably, when designing towers using the new version of the code, careful consideration should be given to the selection of manufacturing quality parameters to ensure accurate and reliable buckling capacity calculations.

To improve the accuracy of reliability prediction for wind turbines, a dynamic reliability prediction model optimized through Bayesian parameter learning is developed. Firstly, a data processing method that integrates a pure reliability Bayesian network with a convolutional neural network was proposed to address the parameter uncertainty and feature extraction of "multivariate heterogeneous" state information. Secondly, a Bayesian improvement method based on parameter learning optimization was introduced to handle parameter uncertainty, enhancing the model’s accuracy in predicting future reliability levels. Finally, the constructed dynamic reliability prediction model, based on Bayesian parameter learning optimization, can precisely predict the reliability trend of wind turbine units over a certain time scale. Case study results show that, compared to other benchmark models, the proposed prediction model exhibits superior performance in predicting the reliability trend of wind turbine units, further validating its effectiveness.

Subject to the unique networking operation mode, the contribution of wind turbines to the system inertia is almost zero. The high proportion of grid-connected wind power seriously threatens the system frequency stability, and the need for wind power to participate in system frequency regulation is increasingly urgent. Firstly, the influence of high wind power grid integration on frequency stability based on system inertia and frequency regulation coefficient is analyzed. Subsequently, the fast frequency support strategies of wind turbine generators are introduced in detail, including rotor kinetic energy control, de-load control, and the participating rapid frequence support of energy storage system, as well as the mechanism of action of the system of multiple control strategies. The advantages and disadvantages of various methods and the adaptation sceneries are compared. Finally, the future research prospects in this field are summarized in view of the power system and social levels.

Based on the basic principle of piezoresistive effect, a self-sensing gasket and its signal transmission system were designed and fabricated. In addition, the single-bolt loosening identification test was used to verify the feasibility of self-sensing gasket detection bolt loosening identification, and the relationship between resistance, conductivity and bolt loosening torque was established. On this basis, a method for rapid detection of bolt group loosening by using self-sensing gasket parallel connection was proposed, and the feasibility of the rapid detection method was verified by bolt group loosening test. The results show that the self-sensing gasket has good loosening recognition effect and stability. Compared with the traditional detection method, the rapid detection method can effectively improve the detection efficiency of bolt group loosening.

To address the operational challenge of offshore wind turbines in the high-temperature and high-humidity environment of the southeastern coast, which are prone to overheating faults, a key temperature measurement point virtual perception and application method based on finite difference domain-deep neural networks （FDD-DNN） is proposed. Firstly, a high-dimensional abnormal operational data identification method integrating mechanism analysis and Isolation Forest algorithm, as well as a high-dimensional similar condition-based missing data imputation method, is proposed. Then, a finite difference regression vector that can characterize the operational conditions and time-delay characteristics of the wind turbine is defined, and a soft margin support vector machine （SSVM） is introduced to partition the differential dynamic operational domain. On this basis, a domain-based temporal dynamic modeling method based on DNN is proposed to achieve refined characterization and virtual perception of key temperature measurement points under full operational conditions. Finally, an engineering method for the deployment and application of the virtual perception model is proposed. Taking the main shaft temperature of offshore wind turbines as an example, the results show that the proposed method improves all evaluation metrics.

This paper proposes a hybrid model for ultra-short term wind speed prediction based on UAV-based wind measurement. The model firstly integrates Time2Vec temporal embedding layer to extract the complex non-linear temporal information, and secondly adopts deep convolutional neural networks with wide first-layer kernels（WDCNN） to accurately extract the wind speed features while further reducing the influence of high-frequency noise, and on the basis of the accurate feature extraction, the accurate prediction of short-term wind speed is achiered by combining bi-directional gating unit （BiGRU） and the attention mechanism to achieve the accurate prediction of short-term wind speed. The accuracy of the hybrid model proposed in this paper is superior to the comparative method for wind speed prediction at all altitude layers when wind speed data acquired by a UAV mounted at 5-25 m is used for validation. Furthermore, at the 25 m altitude layer, where wind speed volatility is the greatest, the model's wind speed prediction demonstrates a lower mean absolute error （E_MAE）, root mean square error （E_RMSE）, and mean square error （E_MSE） than that of the comparative method. The values are 0.1455, 0.4124, and 0.1700 m/s, respectively, which are lower than those of the comparison model by 45%, 25%, and 43%, respectively. These values have been reduced by more than 45%, 25%, and 43%, respectively, in comparison with the values obtained from the comparative model.

Aiming at the current problem of insufficient accuracy and reliability of wind turbine bearing fault warning, a wind turbine bearing fault warning method based on parallel architecture network with improved dynamic fault detection-k-nearest neighbor （FD-KNN） is proposed. Firstly, the correlation analysis of supervisory control and data acquisition （SCADA） of wind turbine is carried out to screen out the variables that are highly correlated with the key variables of wind turbine bearings, and the key variables are decomposed by using the Ensemble empirical mode decomposition （EEMD）, to dig deeply the characteristics of the key variables at different time scales and the potential interactions between key variables and highly correlated covariates. Then, a novel parallel architecture network that combines Self-Attention LSTM and an improved Transformer model is developed to accurately and reliably predict the future states of key variables. Based on the prediction results, the residuals are calculated, and the FD-KNN algorithm is dynamically optimized using the real-time state of wind turbine bearings. This optimization includes adjusting the scale of the nearest neighbors and setting dynamic warning thresholds and conditions to achieve more accurate and reliable fault warnings. Finally, the method is validated using actual SCADA data, and the results demonstrate that it can identify wind turbine bearing faults in advance, excelling in both accuracy and reliability.

A Moving Window-Kernel Entropy Component Analysis （MW-KECA） approach was used to monitor the vibration data related to the units during the gearbox degradation process and diagnose gearbox faults in response to the frequent failures in gearboxes during the production process of wind power generation.This was done while considering the characteristics of industrial processes, such as multiple variables, high dimensionality, non-linearity, high data correlation, and slow deterioration of abnormal data.This method improves sample overfitting.Additionally,the Whale Optimization Algorithm （WOA） was introduced to address the issues of stochasticity and randomness in kernel parameter selection inherent in the algorithm.Simulation studies were conducted using gearbox datasets, and accurate identification and verification of different fault types were achieved in fault diagnosis on a rolling bearing test rig.Comparative analyses reveals that the adoption of MW-KECA combined with WOA results in more efficient and accurate fault time prediction, with a reduced false alarm rate.

This paper proposes an improved centralized damping method： applying a damping matrix at the equivalent rotational center of the monopile. The equivalent rotational center serves as a "decoupling point" for the monopile foundation, addressing the coupling issue in the stiffness matrix at the mudline that complicates rotational damping coefficient calculations based on apparent rotational stiffness. First, the formulas for calculating the damping ratio of the pile-soil interaction system and the position of the equivalent rotational center are derived. Then, this improved method is incorporated into the integrated time-domain analysis of offshore wind turbines by developing the SoilDyn module in OpenFAST. Finally, the validity of the improved centralized damping method is verified by comparing it with distributed damping results under the same load conditions.