基于人工智能的UPQC控制技术,用于优化铁路运输系统的功率质量
D K Nishad1, A N Tiwari1, Saifullah Khalid2
1Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India.
Scientific reports
|August 2, 2024
概括
人工智能控制通过减少波扭曲,显著提高地铁列车的功率质量. 人工神经网络 (ANN) 控制器在提高电力系统稳定性方面表现最好.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 地铁列车表现出非线性负载特征,导致像波和膨胀这样的功率扭曲.
- 这些电源质量问题可能会扰乱火车运行并影响系统效率.
研究的目的:
- 调查人工智能 (AI) 驱动的控制方法对缓解地铁系统中的电力质量问题的有效性.
- 通过使用统一电源质量调节器 (UPQC) 来比较不同AI控制器在提高电源质量的性能.
主要方法:
- 开发并模拟了三种先进的AI控制策略:ANN控制器,NARMA-L2控制器和一个通过自适应算法增强的PI控制器.
- 使用MATLAB Simulink进行系统建模和性能评估.
- 根据减少源电流的总波扭曲 (THD) 来评估控制器的性能.
主要成果:
- 这三种基于人工智能的控制方法在提高电源质量方面都明显优于不受控制的系统.
- 在THD中,ANN控制器实现了最大的降低,其次是NARMA-L2控制器.
- 与基线系统相比,通过自适应算法增强的PI控制器也显示出了显著的改进.
结论:
- 人工智能驱动的控制方法在提高地铁系统的电力质量方面非常有效.
- 该ANN控制器提供了最有前途的解决方案,以确保电力运输网络的顺利和高效运行.
- 这项研究有助于为现代交通基础设施开发先进的电力质量解决方案.
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