Electromagnetic torque ripple comparison.

With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid control strategy for wind power systems directly coupled to a permanent-magnet synchronous generator (PMSG). The contribution of this work is to propose a control strategy design based on a combination of the nonlinear Backstepping approach for system stabilization according to Lyapunov theory and the application of artificial neural network to maximize energy harvesting regardless of wind speed fluctuations. The hybrid control strategy, which is highly efficient in reducing current/torque ripples, as well as the THD ratio of PMSG currents, was applied to achieve good system performance. The overall system is implemented in MATLAB/Simulink to verify the effectiveness of the proposed control technique under varying wind conditions. Analysis of the simulation results for the proposed control versus field-oriented control (FOC) shows that the proposed control strategy exhibits less ripples in the electromagnetic torque, with the ripple ratio decreasing significantly from 32.95% to 19.43%. In addition, the THDs of the stator current decreased from 20.87% to 14.88%, proving the reliability and efficiency of the proposed control strategy compared to FOC. Meanwhile, these results are valuable for the application of the proposed control strategy in a real WECS system.

随着电力发电行业对风能需求的持续增长，为风能转换系统（WECS）优化鲁棒且高效的控制策略已成为必要之举。有鉴于此，本研究针对直接耦合永磁同步发电机（PMSG）的风电系统，提出一种新型混合控制策略。本研究的核心贡献在于，提出一种融合两类方法的控制策略设计方案：其一为基于李雅普诺夫理论实现系统稳定的非线性反步控制法，其二为可在风速波动条件下最大化能量捕获效率的人工神经网络应用方案。该混合控制策略可有效抑制电流与转矩脉动，同时降低PMSG电流的总谐波畸变率（THD），借此实现优异的系统性能。为验证所提控制技术在变风速工况下的有效性，整体系统通过MATLAB/Simulink平台进行建模实现并开展仿真。将所提控制策略与磁场定向控制（FOC）的仿真结果进行对比分析后可见：所提控制策略的电磁转矩脉动得到显著抑制，脉动占比从32.95%大幅降至19.43%；同时定子电流的总谐波畸变率从20.87%降至14.88%，证实了相较于FOC，所提控制策略具备更高的可靠性与运行效率。上述研究结果对于将该控制策略应用于实际风能转换系统（WECS）具有重要的参考价值。