基于图形的拓嵌入和深度强化学习用于电力系统中自主电压控制.
Hongtao Wei1, Siyu Chang1, Jiaming Zhang1
1College of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了一种新的深度强化学习 (DRL) 方法,使用图形卷积网络 (GCN) 和软演员-关键 (SAC) 来通过减负来增强电网电压控制,提高稳定性和效率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 电力系统的复杂性和分布式能源的增加挑战了传统的电压控制方法.
- 现有的深度强化学习 (DRL) 方法在利用拓数据和电网控制的计算效率方面存在局限性.
研究的目的:
- 为智能电网开发基于DRL的先进电压控制策略.
- 通过结合电网拓来增强状态表示,并优化负载减散以改善电压稳定性.
主要方法:
- 一种混合DRL方法,将图形卷积网络 (GCNs) 结合起来,用于拓特征提取和软演员-关键 (SAC) 进行连续行动空间优化.
- GCN处理高阶网格拓信息以丰富状态表示.
- SAC算法优化了减负策略,以平衡经济成本和电压稳定性.
主要成果:
- 拟议的GCN-SAC方法显著降低了所需的减负量.
- 在干扰后观察到改善的电压恢复水平.
- 在IEEE 39总线系统中表现出强大的控制性能和对复杂干扰和拓变化的稳定性.
结论:
- 集成的GCN-SAC方法为现代智能电网中的电压控制提供了创新和有效的解决方案.
- 该方法通过智能管理负载减排来提高电网稳定性和运营效率.
- 强调了将图形神经网络与先进的DRL相结合的潜力,以实现复杂的电力系统管理.
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