通过自适应模型预测控制和新型COA-jDE优化强化学习来提高网格形成逆变器的频率稳定性
Muhammad Zubair Yameen1,2, Zhigang Lu3,4, Fayez F M El-Sousy5
1School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China. zubbairyamin@gmail.com.
Scientific reports
|May 13, 2025
概括
适应型预测控制 (AMPC) 提高了低惯性电力系统中的电网形成逆变器 (GFI) 性能. 这种先进的控制策略通过自适应地调整虚拟惯性和减压来确保强大的频率稳定性,优于传统方法.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 控制理论 控制理论
背景情况:
- 随着可再生能源集成的增加,低惯性电力系统需要对电网形成逆变器 (GFI) 进行先进的控制.
- 传统模型预测控制 (MPC) 由于依赖静态模型,难以应对动态电网条件和频率稳定性.
- 虚拟同步机 (VSM) 模式对于GFI运行至关重要,但在适应电网不确定性方面面临挑战.
研究的目的:
- 开发一个自适应模型预测控制 (AMPC) 框架,以提高VSM模式中的GFM性能.
- 在动态和不确定的电网条件下,在低惯性电力系统中确保强大的频率稳定性.
- 解决传统MPC在适应快速变化的电网环境方面的局限性.
主要方法:
- 实施了一个AMPC框架,将线下强化学习与使用软约束的在线MPC结合起来.
- 采用了一种新的混合鱼优化和自适应差异演化算法 (COA-jDE) 来实现线下成本函数最小化和最佳参数 (Q,R) 导出.
- 在16MW风力发电DFIG微电网上使用模拟来评估AMPC框架的性能.
主要成果:
- 与传统的MPC和VSM方法相比,AMPC在电网干扰,故障,岛屿和负载转移期间表现优越.
- 该AMPC框架实现了虚拟惯性和减压的自适应性调整,提高了GFM的性能.
- 模拟证实了AMPC的计算效率,因为局限的离线调.
结论:
- 拟议的AMPC框架为现代低惯性电网提供了一个灵活和有弹性的控制策略.
- AMPC显著提高了频率稳定性和符合电网规范 (例如,GC0137,IEEE 1547) 的标准.
- 这种适应性控制方法对于可再生能源的可靠整合至关重要.
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