先进的人工智能和可再生能源用于统一的转子角度稳定性控制
Chengpeng He1, Xueying Wang2, Li Shu2
1Kunming University of Science and Technology, YunNan, KunMing, 650500, China.
Heliyon
|September 17, 2024
概括
本研究介绍了一种使用Lazy Deep Q Networks (LDQNs) 和分布式能量存储的统一转子角度稳定性 (RAS) 控制系统. 这种新的AI驱动的方法提高了电网的弹性和效率,以应对可再生能源整合的挑战.
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:
- 整合多种能源,特别是可再生能源,对电网稳定性构成挑战.
- 传统的电力网络需要创新的解决方案来提高稳定性和安全性.
- 旋转器角度稳定性 (RAS) 对于保持可靠的电力供应至关重要.
研究的目的:
- 为动态,瞬态和静态RAS控制提出一个统一的架构.
- 通过先进的控制方法提高电网效率和弹性.
- 为应对日益增长的可再生能源透带来的稳定性挑战.
主要方法:
- 开发了惰深度Q网络 (LDQNs),一种用于RAS控制的新强化学习方法.
- 集成的LDQN与大规模分布式能源存储系统.
- 将动态,瞬态和静态RAS控制统一到一个单一的系统架构中.
主要成果:
- 与传统的RAS方法相比,拟议的LDQN集成系统表现出卓越的性能和适应性.
- 来自LDQN的实时转子角度指令使精确有效的稳定性管理成为可能.
- 大规模分布式储能有效减轻了波动,提高了整体系统的响应能力和灵活性.
结论:
- 统一的RAS框架有效地提高了电网的稳定性和弹性.
- 人工智能驱动的方法,特别是LDQNs,在RAS控制中提供了显著的优势.
- 拟议的系统为管理现代复杂电网的稳定性提供了强大的解决方案.
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