从电源信号中学习:一种自动化方法来识别电力传输系统内的电气干扰
Jonathan D Boyd1, Joshua H Tyler2, Anthony M Murphy1
1Tennessee Valley Authority, Chattanooga, TN 37402, USA.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究引入了一种自动化系统,用于分析电力传输系统中的电力质量事件,达到99%的准确性. 该方法通过更快地检测和预防干扰,提高了电网可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
背景情况:
- 电力公用事业行业越来越多的电力质量问题导致干扰事件数据的激增.
- 由于数量,公用事业人员对这些数据的手动分析变得不可行.
研究的目的:
- 开发一种自动化方法来分析数字故障记录器和电力质量监测器的电力质量事件.
- 提高电力传输系统中干扰事件分类的效率和准确性.
主要方法:
- 利用基于规则的分析来处理电压和电流信号的时间和频率域特征.
- 开发了14种不同的事件类型的分析,包括故障,电机启动和仪器变压器故障.
- 实现了循环直方图的方法,用于连续的名义信号分析和微妙变化检测.
主要成果:
- 在160个信号文件中对干扰事件进行分类时达到99%的平均准确性.
- 循环组图法显示,对内存需求的减少是320倍.
- 成功识别了各种电力质量障碍和初始问题.
结论:
- 自动化方法大大节省了工程师在分类干扰事件中的时间.
- 几乎实时检测和识别干扰会提高传输系统的可靠性.
- 该系统有助于防止潜在问题在升级之前,改善整体电网稳定性.
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