使用深度神经网络和全球终端滑动模式控制的DC微电网的混合控制与指数式到达法
Mohamed A Sharaf1, Hammad Armghan2, Naghmash Ali2
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
一个新的两步控制器结合了深度神经网络和指数级达到基于法律的全球终端滑动模式控制,提高了直流 (DC) 微电网的性能. 这种方法提高了可再生能源整合的稳定性和稳定性.
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:
- 直流 (DC) 微电网对于无碳能源至关重要,集成各种可再生能源如风能和光伏 (PV) 系统.
- 精确的控制对于 DC 微电网的稳定性和效率至关重要,这些微电网具有波动的能源产生和储存.
- 现有的控制方法经常与聊天和缓慢的响应时间扎,限制了它们的有效性.
研究的目的:
- 为复杂的直流微电网系统设计和评估一种新的两步混合控制策略.
- 提高可再生能源的最大功率点跟踪 (MPPT) 效率.
- 为了提高直流微电网控制器的稳定性,强度和响应速度.
主要方法:
- 一种两步控制方法,将深度神经网络 (DNN) 集成为MPPT参考值生成和指数式达到基于法律的全球终端滑动模式控制 (ERL-GTSMC) 进行参考跟踪.
- 对DC微电网动态的数学建模,包括风力轮机,光伏系统,储能单元 (ESU) 和住宅负载.
- 使用Lyapunov标准进行稳定性分析,然后通过MATLAB模拟和硬件在循环 (HIL) 实验进行验证.
主要成果:
- 拟议的ERL-GTSMC有效地消除了滑动模式控制中常见的喋喋不休现象.
- 混合控制器表现出快速到达时间和优越的跟踪性能,与传统的滑动模式和终端滑动模式控制相比.
- 在各种环境条件和系统不确定性下,MATLAB模拟和HIL测试证实了控制器的稳定性和有效性.
结论:
- 开发的混合DNN-ERL-GTSMC控制器为DC微电网控制提供了显著的进步,提高了稳定性和效率.
- 这种方法为集成多种可再生能源和储能系统提供了强大而高性能的解决方案.
- 通过模拟和HIL实验验证实的有效性支持其在未来智能电网中的实际应用.
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