摘要：A unified approach to power system security assessment and contingency analysis suitable foron-line applications is proposed. The severity of the contingency is measured by two scalarPerformance Indices (PIs): Voltage-reactive power performance index, PIVQ and line MVAperformance index, PIMVA. In this paper, a two stage cascade neural network is developed:Stage I employs Multi-Layer Perceptron (MLP) neural network trained by back propagationalgorithm for estimating PIs and Stage II utilizes Kohonen’s Self Organizing Feature Map(KSOFM) for contingency screening and ranking. The effectiveness of proposed methodologyis tested on IEEE 39-bus New England system at different loading conditions corresponding tosingle line outage. The overall accuracy of the test results highlights the suitability of theapproach for on-line applications to fast and accurate security assessment and contingencyanalysis.
关键词：Cascade Neural Network; Contingency Analysis; Multi-Layer Perceptron; Kohonen’s;Self Organizing Feature Map; Performance Index; Power System Security Assessment.
