Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

TLDR

The study proposes an S‑Transform based probabilistic neural network classifier to recognize power quality disturbances. The approach extracts S‑Transform features from PQ signals, trains a probabilistic neural network, and uses a reduced feature set—requiring less memory and training time—while evaluating eleven disturbance types against feedforward multilayer and learning vector quantization networks. Simulations show that the S‑Transform/PNN combination accurately detects and classifies PQ events, outperforming both feedforward multilayer and learning vector quantization neural networks.

Abstract

This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.