使用基于人工神经网络的非线性后退控制器提高风力驱动PMSG的性能
Abdelfattah Dani1, Zineb Mekrini1, Mhamed El Mrabet1
1Industrial Systems Engineering and Energy Conversion Team, FSTT, Abdelmalek Essaadi University, Tetouan, Morocco.
PloS one
|September 10, 2025
概括
本研究介绍了使用永磁同步发电机 (PMSG) 的风能转换系统 (WECS) 的混合控制策略. 与传统方法相比,这种新的方法提高了能量收集,并减少了电流/扭矩波纹.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 控制理论 控制理论
背景情况:
- 越来越多的风能需求需要先进的控制策略来有效发电.
- 永磁同步发电机 (PMSG) 是现代风能转换系统 (WECS) 的关键组件.
- 优化对WECS的控制对于电网稳定性和最大限度地提高能源产量至关重要.
研究的目的:
- 为基于PMSG的WECS提出一种新的混合控制策略.
- 在波动的风速下提高能源采集效率.
- 为了减少电流和扭矩的波动,并改善PMSG电流的总波扭曲 (THD).
主要方法:
- 一种混合控制策略,结合非线性倒退方法和人工神经网络.
- 对于系统稳定性的利亚普诺夫理论.
- 在 MATLAB/Simulink 中实现和模拟以验证性能.
主要成果:
- 电磁扭矩波动比率显著减少,从32.95%降至19.43%.
- 定子电流的总波扭曲率 (THD) 从20.87%大幅降低至14.88%.
- 与传统的面向现场控制 (FOC) 相比,其表现优越.
结论:
- 拟议的混合控制策略为WECS提供了更好的可靠性和效率.
- 这种先进的控制方法有效地减轻PMSG电流中的波纹和THD.
- 这些发现支持在现实世界WECS中实际应用这一策略.
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