Insulated-gate bipolar transistor （IGBT） modules, as the core power components of wind turbine power converters, are crucial for achieving high operational efficiency and cost-effectiveness. Optimizing the electrical conductivity and manufacturing cost of these modules is essential. However, the demanding operational environments often cause performance degradation, and traditional design optimization methods incur high computational costs, making rapid iterative optimization difficult. To address these limitations, this study proposes a rapid iterative design optimization method for wind turbine converter IGBTs based on an improved NSGA-Ⅱ algorithm （Non-dominated Sorting Genetic Algorithm Ⅱ）. Firstly, the optimization problem is formulated with electrical conductivity and manufacturing cost as dual objectives. A parametric thermoelectric coupling model is developed to enable accurate and efficient evaluation of conductivity under varying design conditions. To further enhance the optimization process, an improved NSGA-Ⅱ algorithm based on kernel density estimation is proposed, significantly improving computational efficiency and optimization accuracy compared to traditional optimization algorithms. Additionally, a Kriging-based surrogate model is employed to construct high-fidelity mappings between design variables and optimization objectives, thereby reducing computational burdens and enabling rapid iterative optimization. Numerical experiments confirm the effectiveness and robustness of the proposed method, demonstrating reductions in electrical losses of up to 20.00% and decreases in manufacturing costs by as much as 27.63%. This study provides a practical and efficient design framework for IGBT modules, offering valuable insights into multi-objective optimization in the field of power electronics. By integrating advanced optimization algorithms with surrogate modeling, the proposed method addresses key challenges in the design and performance enhancement of wind turbine power systems.

Because of the applicability challenges of power/torque control technology near the rated wind speed for large wind turbines, this paper proposes a fuzzy-based electrical power and load cooperative control strategy for large wind turbines in the full-generation stage. Firstly, a fuzzy algorithm is used to intelligently judge the rotation speed change trend of the generator's speed change during full-generation stage. Secondly, maximum/minimum torque coefficients and power switching coefficients are proposed, and different control strategies are flexibly selected based on the trend of rotation speed change of generat or. That achieve the organic combination of constant-power and constant-torque control method. Finally, the effectiveness of the proposed method is verified through simulation experiments. This method can not only maintain relatively stable of electrical power and ensure the operation safe of wind turbines and the grid, but also effectively reduce the fatigue loads on the major components of wind turbines, which can extend its service life.

In view of the problems that traditional crowbar protection is difficult to simultaneously suppress rotor current and DC bus voltage as well as absorb reactive power during low-voltage ride-through. This paper proposes a control strategy of multi-stage crowbar switching and reactive power compensation. During the fault period, the crowbar resistance is adjusted in real time by grading the crowbar, so as to achieve the purpose of suppressing the rotor overcurrent and DC bus overvoltage. At the same time as the multi-stage crowbar is put into operation, the GSC adopts the reactive power compensation strategy, so that the fan can provide certain reactive power support to the power grid. On this basis, the change of rotor flux linkage after the crowbars at all levels are put into operation is analyzed, analytical expressions for the stator and rotor short-circuit currents upon the activation of each crowbar stage are derived, and the resistance value for each stage is subsequently set. Then, the short-circuit current of GSC is analyzed according to the reactive power compensation strategy, and then the influence of multi-stage crowbar protection access and GSC additional reactive power compensation strategy on DFIG reactive power characteristics is further analyzed. Finally, the effectiveness of the proposed control strategy and the correctness of the short-circuit current expression are verified by simulation.

To address the issues of low efficiency, limited precision, and multiple parameters in existing PI controller identification methods for rotor-side converters （RSC） of doubly-fed induction generators （DFIG）, a hierarchical identification framework combining an improved black hole algorithm （IBHA） with the RT-LAB hardware-in-the-loop （HIL） co-simulation platform is proposed. Firstly, based on the RSC control dynamics, the operational characteristic data of the actual controller of DFIG is collected by the RT-LAB HIL co-simulation platform. Secondly, a trajectory sensitivity analysis is performed to quantify the dynamic response characteristics of each parameter, followed by the selection of optimal observables and a rigorous identifiability assessment based on persistent excitation criteria. Thirdly, a stepwise identification strategy is established based on the RT-LAB measured data and IBHA. Finally, based on the HIL test data, the identification results of several algorithms are compared and analyzed, which verifies the effectiveness of the proposed method.

Based on the catenary theory and the lumped mass method, a comparative analysis of the dynamic response characteristics of submarine cables under diverse laying modes is carried out. Special attention is given to the sensitivity analysis of pivotal parameters, including wave direction, laying depth, touchdown point distance, and laying speed during the dynamic laying process of submarine cables. Thereby, the influence mechanism of each key parameter in submarine cable laying is unveiled. By contrasting the maximum effective tension and minimum bending radius of submarine cables under different laying methods, the dynamic response traits of various laying methods are elaborated.

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.

When the grid dispatch center issues a reactive power command value, the wind farm needs to rationally allocate this command value to each wind turbine generator （WTG） to meet grid stability requirements. To achieve this objective, this paper proposes a loss-minimization-based reactive power optimization control strategy for onshore wind farms. An improved particle swarm optimization （PSO） algorithm is employed to optimize the reactive power reference value for each WTG, aiming to reduce total losses within the wind farm. These losses encompass generator losses, power converter losses, filter losses, transformer losses, and transmission line losses. To validate the effectiveness of the proposed strategy, a wind farm model with a 5×5 layout is established. Simulation studies compare the conventional reactive power control strategy with the proposed loss-minimization-based reactive power optimization control strategy under different scenarios. The simulation results demonstrate that the proposed optimization strategy significantly reduces power losses in the wind farm.

This study investigates the impact of sand-induced surface wear on the aerodynamic performance of the S809 wind turbine airfoil using a numerical approach integrating the Discrete Phase Model （DPM）, dynamic mesh, and Gaussian filtering. Results show that erosion is concentrated at the leading edge, with a maximum depth of 0.6% chord length. As the angle of attack increases （2°~12°）, the eroded region contracts on the suction side but expands on the pressure side. Surface wear causes pressure fluctuations at the leading edge and advances flow separation, shifting the separation point forward by up to 31.43% chord length at 12°. Erosion increases drag while reducing lift and aerodynamic efficiency, with a 44% drag rise, 25% lift loss, and 48% decline in lift-to-drag ratio at 12°. These findings highlight the adverse effects of surface wear on airfoil aerodynamics.

We propose a Mamba-Transformer model integrating physical constraints and multi-scale features for ultra-short-term wind power forecasting. The proposed model employs complete ensemble empirical mode decomposition with adaptive noise to capture nonlinear and nonstationary patterns in wind power data across multiple frequency components. The decomposed modal functions are then fed into the Mamba model alongside meteorological data from the wind farm to uncover local time-varying properties. The self-attention mechanism of the Transformer model is then employed to capture long-range dependencies among multi-scale features. Finally, a one-dimensional Jensen wake model is integrated to establish physical constraints, integrating the prediction results of physical models and data-driven models through an adaptive weighting mechanism. Experimental results demonstrate that compared to the Transformer model, the proposed model reduces root mean square error （RMSE） and mean absolute error （MAE） by 41.29% and 50.26%, respectively. This model enhances wind power forecasting accuracy while exhibiting strong generalization capabilities.

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.