使用智能计量系统进行电力质量分析的异常检测
Gabriele Patrizi1, Cristian Garzon Alfonso1, Leandro Calandroni1
1Department of Information Engineering, University of Florence, Via di Santa Marta, 3, 50139 Florence, Italy.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究介绍了机器学习,用于快速检测电源质量异常,使用一个类支持向量机 (OCSVM),隔离森林 (IF) 和基于角度的异常检测 (ABOD). 这些方法有效地在线识别信号异常,改善系统可用性并降低维护成本.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 电力质量分析对于复杂的系统和大型工厂至关重要.
- 在电压和电流信号中快速检测异常对于系统可用性和成本有效的维护至关重要.
- 现有的方法可能缺乏在线检测所需的速度和效率.
研究的目的:
- 实施和评估基于机器学习的异常检测算法,用于在线电源质量监测.
- 评估一类支持向量机 (OCSVM),隔离森林 (IF) 和基于角度的异常值检测 (ABOD) 的有效性,以快速识别异常.
- 为了证明将这些算法直接部署在传感器节点上的可行性,以实现低复杂性,高效的信号处理.
主要方法:
- 使用的机器学习算法:一个类支持向量机 (OCSVM),隔离森林 (IF) 和基于角度的异常检测 (ABOD).
- 应用算法用于电压和电流信号的在线聚类和异常检测.
- 建立了用于方法评估的实验平台,使用一致的超参数和主要组件分析 (PCA) 来处理数据.
主要成果:
- 提出的异常检测算法证明了快速有效地识别信号异常.
- 模型实现了高性能指标,包括100%的回忆和高达92%的F1得分.
- 计算的简单性允许在传感器节点上直接实现.
结论:
- 机器学习算法,特别是OCSVM,IF和ABOD,是线上电源质量异常检测的有效工具.
- 拟议的解决方案提供了一种低复杂度,高效的方法来提高系统可用性和减少维护.
- 一旦初始算法检测到异常,可以使用进一步的分类算法进行深入调查.
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