智能电源控制使用深度神经网络和船舶微电网的规范化学习.
Wenhua Deng1, Kaixia Lu2, Xinxin Li3
1Wuhan Railway Vocational College of Technology, Wuhan, 430205, Hubei, China.
Scientific reports
|October 29, 2025
概括
本研究介绍了一种适应性数据驱动控制器,用于带有混合能源存储单元 (HESU) 的船上微电网 (SHMGs). 新型控制器提高了电压稳定性和性能,在模拟中表现优于现有方法.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 整合可再生能源的整合.
背景情况:
- 船舶微电网 (SHMG) 集成可再生能源和储能,以提高效率.
- 在SHMGs中,直流电压稳定性受到负载变化,可再生能源和未建模的动态的挑战.
- 先进的控制策略对于SHMGs中强大的电压调节至关重要.
研究的目的:
- 提出一个可适应的数据驱动控制器,用于在带有混合能源存储单元 (HESU) 的SHMGs中进行强大的电压调节.
- 在动态运营条件下增强SHMGs的稳定性和性能.
- 通过硬件在循环 (HiL) 模拟来验证控制器的有效性.
主要方法:
- 开发了一种两级数据驱动的电压调节器.
- 阶段1:超局部模型控制 (ULMC) 通过规范化演员-关键 (RAC) 深度神经网络稳定.
- 第二阶段:非整数扩展状态观察员 (NIESO) 估计了未建模的动态和干扰.
主要成果:
- 提出的基于RAC的控制器显示了显著的性能改进.
- 与模糊逻辑控制器相比,实现了44.08%的改进,与模型预测控制器 (MPC) 相比,提高了36.85%.
- 硬件在循环 (HiL) 模拟证实了在现实的SHMG操作下可行性和适用性.
结论:
- 适应性数据驱动控制器确保了SHMGs中强大的电压调节.
- 该框架有效地估计和补偿未知的非线性干扰和未建模的动态.
- 与传统方法相比,拟议的控制器为SHMG电压稳定提供了优越的解决方案.
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