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.

To explore a new mechanism of frequency response with multi-resource coordination, a day-ahead and intra-day decentralized mutual-aid method of energy and auxiliary services is proposed. Firstly, based on the mutual-aid of energy resources, the framework of multivariate adjusting resources is established including day-ahead and intra-day energy, inertia and primary frequency regulation （PFR）. Secondly, the shared alternating direction method of multiplier（ADMM） is adopted to realize efficient solutions of the model and protect the privacy of mutual-aid subjects, in which the alternating optimization procedure （AOP） is established to address the non-convexity. Finally, a simulation example is given to verify that the proposed method can effectively motivate multiple regulatory resource suppliers to provide inertia and PFR services, which provides a reference for promoting mutual-aid of frequency regulation resources.

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.

This paper proposes a detection method for protection mismatch risk settings tailored for high-penetration renewable energy integration scenarios. Using a typical topology of high-penetration distributed renewable energy systems, the degradation mechanism of protection performance is analyzed in depth. Furthermore, the constraints on correct protection operation are revealed by investigating the root causes of protection misoperation and failure to trip. Based on this analysis, the mapping relationship between DG output and protection mismatch risk settings is quantitatively examined. The boundary of protection failure is further determined using a dual-loop iteration method based on the backbone particle swarm optimization （PSO） algorithm. Finally, a comprehensive identification procedure for protection mismatch risk settings is developed. The effectiveness of the proposed approach is validated through simulations using the PSCAD/EMTDC and Matlab/Simulink platforms.

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.

A dual-winding induction machine based inverting system is proposed in this paper. The dual winding induction machine with two electrical interfaces is utilized as an energy conversion hub between the renewable energy source and the grid. It enhances the motor characteristics of the power system while completing the renewable energy integration. In order to make it valuable for application, this paper focuses on its control strategy. This paper sets out the analysis of the operating characteristics of the dual-winding induction machine. The analysis is used to design a set of multi-condition control strategies for different operating conditions. This strategy is intended to meet the control requirements of startup, integration into the grid, grid-connected operation and off-grid operation. The experimental results verify the effectiveness of the proposed control strategy.

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 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.

In view of the characteristics of multi-regional correlation of full-network power flow in the new-type power system, an optimization method for coordinated electricity-carbon operation and a new full-network multi-regional cooperative optimization method for dynamically matching coordinated electricity-carbon operation are proposed. Firstly, according to the characteristics of cascade multi-source load-storage units in sub-regions of the new-type power system, the differences in operational regulation efficiency of various load-storage units within the region are analyzed, and a gradient cooperative optimization method for load-storage unit indicators is proposed. Then, aiming at the diversified characteristics of electricity-carbon regulation of load-storage units under different operating modes, a fine-tuning optimization method for load-storage unit control parameters based on complete state space and operational mode feedback is studied, and a 'multi-level multi-core' convolutional neural network model is established. Finally, a simulation model is constructed for validation. Simulation results show that the proposed multi-level indicator cooperative electricity-carbon power flow optimization control strategy can effectively enhance the electricity-carbon coordination capability and electricity-carbon regulation level of the new-type power system.

To achieve accurate load awareness and efficient device control, this study proposes a novel non-intrusive load decomposition （NILD） method by combining multi-modal feature selection techniques with an improved time convolutional network to enhance identification accuracy and efficiency. In this method, the load multi-scale maximum overlapping discrete wavelet transform results are introduced as input features, and the feature is combined with the mRMR algorithm to ensure the effective integration of multi-modal features. At the same time, the adaptive receptive field attention mechanism （RFAM） is added to the TCN method,improving the ability to extract features at different time scales. Finally, the algorithm is validated on datasets such as PLAID. The results show that the proposed method can achieve more accurate and efficient load decomposition with fewer features,which is higher than the regression accuracy of the traditional BiLSTM model of 5.31%,and the detection rate of new energy household equipment such as photovoltaic power has also reached over 96.6%.