在基于ANN的核心和条件下恢复电流变压器的二次电流
Ismoil Odinaev1, Abdel-Haleem Abdel-Aty2, Andrey Pazderin1
1Department of Automated Electrical Systems, Ural Federal University, 620002, Yekaterinburg, Russia.
Heliyon
|September 26, 2024
概括
电流变压器核心和会扭曲电信号,影响电力系统的保护. 一种新的人工神经网络方法有效地恢复扭曲的电流信号,将继电器保护的准确性提高了1.25倍.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 继电保护和应急自动化对于电力系统至关重要.
- 算法依赖于仪表变压器的电流和电压数据.
- 在故障条件下,当前变压器核心和会扭曲信号,导致错误操作.
研究的目的:
- 解决电流变压器核心和问题及其对继电器保护的影响.
- 提出和评估一种用于恢复扭曲电流信号的新方法.
主要方法:
- 对处理电流变压器和现有方法的比较分析.
- 制定和问题作为回归任务.
- 开发和应用基于人工神经网络 (ANN) 的方法.
- 实施实时数据再缩放技术用于信号恢复.
主要成果:
- 与现有技术相比,拟议的基于ANN的方法显示出更高的性能.
- 发现,拟议方法的效率是现有最有效方法的1.25倍.
- 通过计算实验的验证证实了拟议方法的有效性.
结论:
- 人工神经网络为减轻当前变压器核心和效应提供了一个有希望的解决方案.
- 开发的方法提高了继电器保护和应急自动化系统的可靠性.
- 实时数据再缩放是一种可行的技术,可以恢复扭曲的电流波形.
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