To address the low efficiency and high pollution associated with fossil-energy-dominated energy use in rural industrial parks, a park-level integrated energy system （PIES） model is established based on the source-load characteristics of rural parks. To promote renewable energy accommodation and biomass utilization, a power-to-ammonia system （P2A） and a biomass anaerobic fermentation gas production system （AF） are introduced into the PIES and modeled in detail. Considering the influence of anaerobic fermentation temperature on gas production rate, the waste heat from the P2A system is delivered to the AF system to construct a P2A-AF coupled system, thereby improving system energy efficiency and economy. On this basis, detailed carbon emission modeling is carried out for the main energy-use processes of the PIES, and carbon emission costs are incorporated to establish an optimal dispatch model with the minimum total cost as the objective. The simulation results show that the proposed system can improve energy economy by 31.56%, increase the local accommodation rate of renewable energy by 34.92%, and reduce total carbon emissions by 3.58%.

To address the limitations of traditional resource allocation methods, this study analyzes the process of optimizing resource allocation strategies for offshore wind farm construction under meteorological uncertainty. A multi-objective optimization framework is proposed, incorporating four key performance indicators： minimum total working time, resource allocation optimization rate, window period utilization rate, and cost-benefit ratio. A discrete-event digital simulation-based optimization model is developed to support this framework, enabling systematic evaluation of the benefits of resource allocation strategies through statistical analysis of these indicators.The Laotian Monsoon Wind Power Project is selected as a case study for simulation. Results demonstrate that the established model can effectively simulate the entire construction process of offshore wind farms, providing real-time visualization of meteorological conditions, wind turbine unit statuses, and personnel dynamics. It also accurately quantifies critical metrics including minimum working time, resource allocation optimization rate, window period utilization rate, and cost-benefit ratio. The model also to significantly enhances resource allocation during wind farm installation, improves overall construction efficiency, and provides valuable pre-construction planning support. These outcomes collectively contribute to achieving the dual goals of cost reduction and efficiency improvement in offshore wind farm development.

Wind power forecasting is a crucial topic in the field of wind energy generation. With the increasing penetration of renewable energy in power systems, wind energy, as a rapidly developing renewable resource, necessitates accurate short-term forecasting for the energy industry. In this paper, a Gaussian Mixture Model-gated Recurrent Unit （GMM-GRU） based on Gaussian graph convolution is proposed. Firstly, the XGBoost algorithm optimized by Particle Swarm Optimization （PSO） is utilized to construct a feature selection network for identifying important features. Secondly, using the graph auto-encoder and cosine correlation fusion method, a network graph to effectively capture the potential long-distance correlations among sites and accurately describe the spatial correlation of multi-site features are built. Finally, the Gaussian Mixture Model （GMM） is employed to deeply extract the intrinsic relationships within the network graph, and the Gated Recurrent Unit （GRU） integrates its spatio-temporal correlations to address the power forecasting problem. The experiments on real wind farm data are carried out. The comparisons with other models demonstrate that the proposed model significantly enhances wind power forecasting performance.

To address the challenges of insufficient feature extraction, inadequate feature fusion mechanisms, and limited diagnostic capability under complex operating conditions in rotating machinery fault diagnosis, a novel FFT-CNN-Informer fault diagnosis method based on dual-domain feature fusion is proposed. A parallel time-frequency domain feature extraction architecture is constructed. Specifically, Fast Fourier Transform （FFT） is employed to extract frequency-domain features, a multi-scale Convolutional Neural Network （CNN） is designed to capture local temporal features, and a Probabilistic Sparse Self-Attention mechanism is introduced to capture long-range dependencies, enabling multi-dimensional feature extraction of vibration signals. Furthermore, residual learning and an adaptive feature fusion mechanism are incorporated to enhance the ability to extract fault features under varying operating conditions. Experimental results on the Case Western Reserve University bearing dataset demonstrate the superiority of the proposed method. On wind turbine bearing datasets, the proposed method outperforms baseline Transformer and LSTM models by 6.73% and 9.77% in diagnostic accuracy, respectively. Ablation studies further validate the necessity of the dual-domain feature extraction architecture and the adaptive feature fusion mechanism, confirming the effectiveness and robustness of the method in practical wind farm applications.

In order to realize efficient electrostatic adsorption dust removal for photovoltaic modules, the paper designs an aluminum needle-plate electrode type with a needle on the bottom surface. On the basis of clarifying the mechanism of electrostatic adsorption dust removal, COMSOL Multiphysics software is used to establish a coupled simulation model of electrostatic force, charge transfer and charged particle tracking, calculating the distribution of electric field strength, the amount of charge of the dust particles and the dust removal rate in the dust removal device, and setting up an experimental platform for electrostatic adsorption dust removal to verify the reliability of the simulation model. The results show that the multi-needle aluminum needle-plate electrode type significantly improves the efficiency of electrostatic adsorption dust removal, the dust removal rate can reach 96.84% after 3 s of device operation, and the power generation efficiency of photovoltaic modules can reach 93.89% under the state of no dust. Compared with the ordinary aluminum plate, the use of needle-plate electrode type can make the small-sized particles adhered to the surface of the photovoltaic module significantly reduced.

This paper takes the IEA 15 MW wind turbine blade as the research object, establishes the dynamic model of the aeroelastic system for the blades, analyzes the aeroelastic response during flutter at high tip speed ratio, and conducts in-depth research on the multi-degree-of-freedom coupling characteristics therein. The results show that at high tip speed ratio, the long and flexible blade of this wind turbine undergoes a coupled bending-torsion flutter of pitch-torsion. The vibrations in the three degrees of freedom all contain the natural frequencies of pitch and torsion. However, the maintenance of flutter is sustained by the aerodynamic power work in the pitch and torsion degrees of freedom. There is an obvious coupling phenomenon in the development of the instantaneous aerodynamic power of pitch and torsion. In the direction of yaw, there is basically no energy contribution. There is only weak aerodynamic power throughout the process, and it only undergoes forced vibrations due to the excitation of pitch and torsion because of the coupling characteristics of the blade structure.

This study investigates the influence of second-order wave forces on the dynamic response of a semi-submersible floating wind turbines, using the "Guoneng Sharing" as a case study. A comparative analysis of the differences between far-field and near-field methods for calculating the second-order mean wave force transfer functions （QTFs） is presented. A coupled time-domain model of aerodynamic, hydrodynamic, structural, and servo dynamics was established to analyze the dynamic responses of the floating wind turbine under four scenarios： neglecting second-order wave forces, considering second-order mean wave forces, considering second-order difference wave forces, and considering both second-order difference and sum wave forces. The results indicate that the QTFs calculated by the far-field and near-field methods are nearly identical when the angular frequency is less than 1.1 rad/s. However, as the angular frequency increases, the far-field method generally yields larger values than that of the near-field method. The second-order wave forces significantly impact the motion response of the semi-submersible platform, with the primary contributing components being the second-order mean wave forces or second-order difference wave forces, while the second-order sum wave forces exhibit negligible influence. Notably, the second-order difference frequency and sum frequency wave forces significantly affect the acceleration of the semi-submersible platform and the load at the top of the tower, whereas the second-order mean wave forces have no discernible effect on either. The high-frequency response of the tower top load is excited under the influence of second-order difference and sum frequency wave forces and must be taken into account in calculations. Furthermore, second-order wave forces markedly affect the tension and fatigue life of the mooring anchor chains, not only increasing the fatigue damage but also influencing the location of maximum fatigue damage occurrence.

Wind turbine gearboxes are subjected to contact loads during operation, resulting in contact fatigue damage in the gears, which significantly affects their service life. To accurately predict the contact fatigue life of gears, this study takes a 6 MW wind turbine gearbox operating at Dabancheng Wind Farm, Xinjiang, as the research object. A gearbox dynamic model considering both internal and external excitations is established to calculate the dynamic meshing forces of each helical gear pair. The analysis incorporates local material properties and residual stresses in the gears. By applying the Dang Van multiaxial fatigue criterion combined with S-N curves, the fatigue damage distribution in the helical gears under contact loading is analyzed. Fatigue hotspots are identified, and critical operating conditions are extracted to develop a surrogate model, which quantifies the contact fatigue damage and estimates the contact fatigue life of each gear over a 20-year design period. The results indicate that the engage-in and recess points of external meshing pairs in helical gears are the primary fatigue hotspots, making them more susceptible to fatigue failure. Among all gears, the sun gears exhibit the most severe contact fatigue damage, with estimated contact fatigue life of 8 years for the high-speed sun gear and 21 years for the low-speed sun gear. Conversely, the planetary gears in both stages show relatively lower fatigue damage, with their contact fatigue life exceeding 30 years. The ring gear experiences the least fatigue damage, resulting in an exceptionally long service life.

To address the limitations and one-sidedness issues in unidimensional analysis of wind turbine three-row cylindrical roller bearing simulations under single-variable operating conditions, this study establishes a comprehensive rigid multibody contact dynamics simulation framework for spindle bearing systems under operational loading. A comparative analysis is conducted between models incorporating and omitting the transmission path considerations. Systematic investigations are performed on cage-roller kinematic behavior through theoretical velocity computations, simulated rotational dynamics, and acceleration response characterizations. The model is verified through rigorous verification procedures, enabling detailed examination of interfacial force characteristics between rolling elements and raceways in three-row cylindrical roller bearing configurations. Key findings demonstrate that transmission path integration significantly enhances simulation fidelity to actual service environments. Distinct load-bearing and non-load-bearing zones emerge in radial roller arrays, contrasting with the full-row load alternation pattern observed in thrust rollers. The contact force magnitude at the radial roller-inner race interface exceeds distal thrust roller interactions by 5.67 times, while distal thrust roller forces maintain approximately twice the magnitude of proximal counterparts. When the roller is in the non-load-bearing zone, its self-rotation speed gradually decreases, and the degree of decrease is directly proportional to the movement time in the non-load-bearing zone. The rotational speed and contact force of the thrust roller and thrust cage have the same alternating changes as the axial alternating load. The rotational speed of the radial cage is more stable. When the thrust roller is not under load, the corresponding thrust cage will be driven to rotate by the inner ring, and the rotational speed will increase.

A load reduction method was provided by optimizing the aerodynamic configuration and structural design of the blades in this paper. The GH-Bladed computing platform was used to verify the blade performance and turbine load based on 6.25 MW wind turbine. The study finds that reducing the blade chord length has the most significant effect on reducing the blade root load, while the effect of reducing thickness on load reduction is not prominent. However, reducing blade thickness is a convenient method to improve aerodynamic performance while also weakening load. In addition, the laying thickness of the spar cap structure can change the blade flapwise stiffness to affect flapwise deformation. Expanding the blade flapwise deformation by adjusting the thickness of the blade spar cap to release concentrated stress is a novel method to promote the reduction of blade load generation.