Self Organizing Map (SOM) approach for classification of Power Quality events

Abstract

In this work, Self Organizing Map (SOM) is used in order to classify the types of defections in electrical systems, known as Power Quality (PQ) events. The features for classifications are extracted from real time voltage waveform within a sliding time window and a signature vector is formed. The signature vector consists of different types of features such as local wavelet transform extrema at various decomposition levels, spectral harmonic ratios and local extrema of higher order statistical parameters. Before the classification, the clustering has been achieved using SOM in order to define codebook vectors, then LVQ3 (Learning Vector Quantizer) algorithm is applied to find exact classification borders. The k-means algorithm with Davies-Boulding clustering index method is applied to figure out the classification regions. Here it has been observed that, successful classification of two major PQ event types corresponding to arcing faults and motor start-up events for different load conditions has been achieved