使用DDPG增强剂增强转子角度稳定性,使用大猩猩部队优化输入缩放因子
Ahmed H Yakout1, Ahmed E B Abu-Elanien2, Hany M Hasanien3,4
1Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt.
Scientific reports
|March 24, 2025
概括
本研究介绍了一种基于强化学习 (RL) 的新型动力系统稳定器 (PSS),使用深度决定性策略梯度 (DDPG) 来增强转子角度稳定性. 与传统方法相比,RL-PSS表现出优越的短暂稳定性性能.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 转子角度稳定对于动力系统可靠性至关重要.
- 传统的电力系统稳定器在适应复杂的电网动态方面存在局限性.
- 强化学习 (RL) 为适应性控制提供了一个有前途的方法.
研究的目的:
- 开发和评估基于强化学习 (RL) 的动力系统稳定器 (PSS),以提高转子角度稳定性.
- 为了优化RL代理的性能,使用大猩猩部队优化 (GTO) 算法.
- 通过各种电力系统测试案例验证拟议的PSS.
主要方法:
- 使用深度决定性政策梯度 (DDPG) 算法为PSS训练RL代理.
- 输入特征包括缩放式发电机加速功率,其导数和整数,以及实力.
- 使用大猩猩部队优化 (GTO) 算法来优化输入观测的缩放因子.
- 一个离散的奖励功能,专注于发电机加速功率低于一个值.
主要成果:
- 与基于多带dw速度的pss (mb-pss),基于dw速度的pss (dw-pss) 和基于加速功率的pss (dpa-pss) 相比,拟议的基于RL的pss在模拟中表现出更好的性能.
- 稳定器表现出增强的短暂稳定性能力,即使在长期故障条件下.
- 在单机无限总线 (SMIB),Kundur的四机系统和IEEE 39总线的十机系统上进行了模拟.
结论:
- 开发的基于RL的PSS与DDPG和GTO优化有效地提高了转子角度稳定性.
- 拟议的方法为电力系统稳定提供了强大而适应性的解决方案.
- 这种方法在过渡稳定性方面比传统的PSS设计显著改进.
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