Hybrid expert system neural network hierarchical architecture for classifying power system contingencies

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213501

H. Yan, J. Chow, M. Kam, R. Fischl, C.R. Sepich

引用次数: 8

Abstract

The authors present a hierarchical architecture which couples an expert system (ES) with multiple neural networks (NNs) for classifying power system contingencies. The ES performs the 'coarse' screening to decide if a contingency is potentially harmful and then determines its type of security limit violations. It uses a set of heuristic rules and a set of performance indicators to filter out the secure contingencies and direct the potentially harmful ones for further analysis in the appropriate NN. The NN's take the coarse classification outcome from the ES and perform a 'finer' screening by classifying the contingencies according to the severity of limit violations.<>

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电力系统事故分类的混合专家系统神经网络分层结构

作者提出了一种将专家系统与多个神经网络相结合的分层结构，用于电力系统事故分类。ES执行“粗”筛选，以确定突发事件是否具有潜在危害，然后确定其违反安全限制的类型。它使用一组启发式规则和一组性能指标来过滤掉安全事件，并指导潜在的有害事件在适当的神经网络中进行进一步分析。神经网络从ES中获取粗分类结果，并根据违反限制的严重程度对偶发事件进行分类，从而执行“更精细”的筛选。

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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