使用基于改进的TD3算法和知识融合的DFIG智能减压控制来缓解次同步振荡
Ge Liu1,2, Jun Liu3, Andong Liu1
1College of Automation, Xi'an University of Technology, Xi'an, 710048, China.
Scientific reports
|June 26, 2024
概括
这项研究介绍了一种用于双输入感应发电机 (DFIG) 的智能分同步缓冲控制器 (I-SSDC),以减轻分同步振荡 (SSO). 这款新型控制器使用深度强化学习来提高电网稳定性.
科学领域:
- 电力系统工程 电力系统工程
- 整合可再生能源的整合
- 控制系统 控制系统
背景情况:
- 双供应感应发电机 (DFIG) 中的次同步振荡 (SSO) 对电网稳定性构成重大威胁.
- 传统的减压控制器在与现代动力系统的动态性质作斗争.
研究的目的:
- 为DFIGs开发一个智能分同步缓控制器 (I-SSDC),以有效地减轻SSO.
- 通过深度强化学习和综合知识提高控制器的适应性和解释性.
主要方法:
- 使用Softmax改进的双延迟深确定性政策梯度 (TD3) 算法制定I-SSDC框架.
- 通过加权线性回归和规范化构建替代模型,以确定关键的SSO影响因素.
- 将领域知识纳入代理人培训,以提高勘探效率.
主要成果:
- 拟议的I-SSDC证明了在各种操作条件下有效地抑制SSO.
- 替代模型有助于优化控制器的输出信号选择,以改善决策.
- 智能控制器的环境适应性和解释性得到了提高.
结论:
- I-SSDC有效地解决了传统控制器在基于DFIG的风力轮机中SSO缓解方面的局限性.
- 深度强化学习和知识的整合为保持电网稳定提供了强大的解决方案.
- 开发的方法在动态动力系统环境中提供了更好的性能和适应性.
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