Power quality disturbance recognition using hybrid signal processing and machine intelligence techniques

Abstract

This paper presents two novel approaches for power quality (PQ) event classification. It is a two stage system in which optimal features that characterise the complete signal behaviour are extracted in the first stage and in second stage, based on these features various disturbance waveforms are classified. In the first classifier, a hybrid approach between S-transform and decision tree (DT) is presented. In the second classifier, the S-transform (ST) technique is integrated with neural network (NN) model with multilayer perceptron. Power system suffers from different PQ events such as sag, swell, momentary interruptions, impulsive transients, notch, spike, harmonics and also combination of the above with noise. The above-mentioned events comprise high-frequency and low-frequency components. Thus, it is difficult to classify these PQ events using traditional approaches. Both the classification methods derive various statistical parameters of eight types of single power disturbance and two types of complex power disturbance using generalised S-transform. After the required features are extracted, the neural network and the decision tree are used for power quality event detection. The analysis and simulation results show that the proposed classifiers can effectively classify with higher degree of accuracy to recognise the different PQ disturbances even under noise contamination.