Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213506

D.L. Lubkeman, C.D. Fallon, A. Girgis

引用次数: 11

Abstract

A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<>

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配电系统暂态现象检测与分类的无监督学习策略

一些公用事业公司目前正在其配电变电站安装高速数据采集设备。该设备将能够记录由于低阻抗和高阻抗故障、电容器开关和负载切换等事件而产生的瞬态波形。作者描述了应用无监督学习策略对变电站记录仪观察到的各种事件进行分类的潜力。使用模拟研究测试了几种策略，并将无监督学习的有效性与当前的分类策略以及监督学习进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems

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