强化学习驱动的自适应性主动频率漂移,用于快速可靠的岛屿检测
Ahmed G Abo-Khalil1,2, Khairy Sayed3,4, Nsilulu T Mbungu5,6
1Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, United Arab Emirates. aabokhalil@sharjah.ac.ae.
Scientific reports
|January 7, 2026
概括
本研究介绍了一种人工智能驱动的自适应方法,用于在光伏系统中隔离检测,显著减少非检测区,并提高检测速度,以提高电网稳定性和安全性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
背景情况:
- 岛屿检测对于连接到电网的光伏系统至关重要,以确保电力质量,安全性和稳定性.
- 传统的主动频率漂移 (AFD) 方法有局限性,包括一个大的非检测区 (NDZ) 和不适合动态电网条件的固定参数.
- 对于响应实时电网动态的AFD方法的自适应性扰动参数调整存在重大研究缺口.
研究的目的:
- 为光伏系统开发一种新的AI驱动的自适应AFD方法.
- 通过动态优化扰动参数来消除NDZ并提高系统稳定性.
- 解决现有的AFD方法在适应不断变化的电网和负载条件方面的局限性.
主要方法:
- 实施了强化学习 (RL) 方法来优化切割分数 (Cf) 和增强的校正因数 (Cr).
- 该RL代理被训练使用基于奖励的战略快速和准确的岛屿检测.
- 根据频率变化速率 (df/dt) 和Cf进行了自适应更新,以最大限度地减少NDZ.
主要成果:
- 实现了0.12-0.17秒的岛屿检测时间,比标准AFD (0.2-0.5秒) 显著改善.
- 将NDZ降低到1%以下,而传统AFD方法的NDZ为10-15%.
- 保持总波扭曲 (THD) 在≤2%,确保高功率质量.
结论:
- 拟议的AI驱动的自适应AFD方法有效地消除了NDZ,并提高了光伏系统的检测速度和稳定性.
- 在各种光伏配置的实验验证证证了该方法的可扩展性和可靠性.
- 这种技术为智能电网提供了一个有前途的解决方案,确保符合IEEE Std. 929 在保持电源质量和稳定性的同时,对岛屿检测的要求.
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