基于AI的混合动力电力质量控制系统用于使用单相PV-UPQC的电力铁路,并采用利亚普诺夫优化
D K Nishad1, A N Tiwari1, Saifullah Khalid2
1Department of Electrical Engineering, M. M. M. U. T, Gorakhpur, Uttar Pradesh, India.
Scientific reports
|January 21, 2025
概括
这项研究介绍了电力铁路的AI驱动的混合动力系统,提高了电力质量和效率. 这种新的方法提高了电压稳定性,并减少了25kV交流引网络中的波扭曲.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
背景情况:
- 25kV交流引网络面临重要的电源质量问题,如电压不平衡和高总波扭曲 (THD).
- 现有的电力质量管理系统难以应对动态负载变化,并有效地整合可再生能源.
研究的目的:
- 为25kV交流铁路网络开发和验证一个先进的AI驱动的混合动力电源质量管理系统.
- 解决关键的电源质量挑战,包括电压不平衡,THD,电压变化和功率因子.
- 将单相光伏统一电力质量调节器 (PV-UPQC) 与人工神经网络 (ANN) -Lyapunov控制架构集成.
主要方法:
- 实现混合系统,将单相PV-UPQC与ANN-Lyapunov控制架构相结合.
- 使用双重方法方法:基于ANN的参考信号生成和用于动态参数调节的Liapunov优化.
- 使用MATLAB/Simulink进行系统模拟和性能验证.
主要成果:
- 实现了电压不平衡的显著减少,从1.5%降至0.8%.
- 将总波扭曲 (THD) 降低到1%以下,并将功率因子修正为单位.
- 证明了40%更快的动态响应和DC链路电压调节在±2%以内.
- 保持了95%的整体系统效率.
结论:
- 拟议的AI驱动的混合系统有效地提高了电力铁路的电力质量和能源效率.
- 基于ANN的控制,利亚普诺夫优化和光伏技术的整合为现代引网络提供了强大的解决方案.
- 该系统的经过验证的性能证实了其能够管理25kV交流铁路系统中复杂的电力质量挑战的能力.
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