Aiming at the problems of frequent overshoot and slow response time in space vector direct torque control system of permanent magnet synchronous motor, a control strategy based on a fast-super-twisting algorithm was proposed. Based on the traditional super-twisting algorithm, the fast terminal sliding mode reaching law (FTSMRL) is introduced in the strategy, which improves the algorithm's robustness and convergence speed, suppresses the sliding mode chattering. The stability of the system is proved by using a quasi-quadratic Lyapunov function. Simulation results show that the proposed strategy can effectively improve the response speed and robustness of the system, and significantly reduce the system overshoot and sliding mode chattering.

Aiming at the problems of severe chattering and difficulty in low‐speed operation of the surface‐mounted permanent magnet synchronous motor (SPMSM) sensor‐less speed control system based on the traditional sliding mode observer (SMO), this paper proposes a sensor‐less control strategy of supertwisting sliding mode observer based on adaptive feedback gain (AFG‐STA‐SMO). This strategy combines the supertwisting algorithm (STA) with the equivalent feedback principle and designs an adaptive law to compensate for the rotor position error by adjusting the feedback gain coefficient online. Secondly, considering the ripple component in the back electromotive force (back‐EMF), the Kalman filter gets a smoother back‐EMF signal, further improving the rotor position estimation accuracy. The stability of the system is proved by using the Lyapunov function. Finally, the feasibility and effectiveness of the proposed control strategy are verified by MATLAB/Simulink simulation.

To achieve the high‐performance control of the surface‐mounted permanent magnet synchronous motor (SPMSM) speed control system, this paper proposes a high‐order sliding mode control strategy based on a new super twisting algorithm (NSTA). This strategy introduces an adaptive term in its proportional term based on the original super twisting algorithm, which solves low reaching speed and poor antidisturbance ability due to the square root calculation of proportional term in the original super twisting algorithm. The simulation results show that the proposed strategy can effectively improve the system’s response speed and antidisturbance and greatly suppress the chattering phenomenon of traditional sliding mode control.

To address the challenges of the strong randomness and intermittency of wind power generation that affect wind power grid integration, power system scheduling, and the safe and stable operation of the system, an improved Dung Beetle Optimization Algorithm (MSADBO) is proposed to optimize the hyperparameters of the Long Short-Term Memory neural network (LSTM) for ultra-short-term wind power forecasting. By applying Bernoulli mapping for population initialization, the model’s sensitivity to wind power fluctuations is reduced, which accelerates the algorithm’s convergence speed. Incorporating an improved Sine Algorithm (MSA) into the forecasting model for this nonlinear problem significantly improves the position update strategy of the Dung Beetle Optimization Algorithm (DBO), which tends to be overly random and prone to local optima. This enhancement boosts the algorithm’s exploration capabilities both locally and globally, improving the rapid responsiveness of ultra-short-term wind power forecasting. Furthermore, an adaptive Gaussian–Cauchy mixture perturbation is introduced to interfere with individuals, increasing population diversity, escaping local optima, and enabling the continued exploration of other areas of the solution space until the global optimum is ultimately found. By optimizing three hyperparameters of the LSTM using the MSADBO algorithm, the prediction accuracy of the model is greatly enhanced. After simulation validation, taking winter as an example, the MSADBO-LSTM predictive model achieved a reduction in the MAE metric of 40.6% compared to LSTM, 20.12% compared to PSO-LSTM, and 3.82% compared to DBO-LSTM. The MSE decreased by 45.4% compared to LSTM, 40.78% compared to PSO-LSTM, and 16.62% compared to DBO-LSTM. The RMSE was reduced by 26.11% compared to LSTM, 23.05% compared to PSO-LSTM, and 8.69% compared to DBO-LSTM. Finally, the MAPE declined by 79.83% compared to LSTM, 31.88% compared to PSO-LSTM, and 29.62% compared to DBO-LSTM. This indicates that the predictive model can effectively enhance the accuracy of wind power forecasting.

Aiming at the shortcomings of the analytic hierarchy process in the comprehensive evaluation of wind power system operation status with strong subjectivity in determining weights, this thesis proposes an analytic hierarchy process (AHP) based on particle swarm optimization (PSO) and constructs an analytic hierarchy process combined with a particle swarm optimization algorithm (PSO + AHP) model to optimize the weights. It overcomes the disadvantage that once given the judgment matrix in the AHP method, the weight values and consistency cannot be improved. In this article, the comparison chart of the consistency indexes calculated according to this method shows that the one-time indexes of C a as well as C 1 − C 7 are reduced to different degrees, so a weight value with a relatively high degree of consistency can be obtained by this method. Second, for the situation that there are several judgment indexes in the sub-project layer that deviate seriously at the same time, introduce the degradation index, and apply the fuzzy comprehensive judgment method to establish the model of wind power system operation status assessment. Finally, based on the actual monitoring data of a wind farm over a period of time, its operational status was evaluated using the proposed PSO-AHP model based on FCE, and a score that can indicate the operational status can be obtained by calculation. In this article, the evaluation score of a wind farm is 0.556, indicating that the staff needs to carry out maintenance at this time. The comparative analysis shows that compared to the traditional AHP-FCE evaluation method, the assessment results proposed in this article are relatively good and have practical value and significance for improving the real-time reliability of grid-connected operation of wind turbines, optimizing the maintenance strategy of wind turbines, and reducing the cost of wind power generation.