Effect of Data Normalization on Accuracy and Error of Fault Classification for an Electrical Distribution System

Abstract

ABSTRACT Unpredictable occurrence of electrical faults on a distribution system leads to power outages for electricity consumers. This makes accurate detection and classification of faults an important task. To isolate faulty sections, output of artificial intelligence (AI)based fault classifier is used to design optimum protection schemes for distribution systems. To enhance classifier’s performance, digital signal processing techniques like discrete wavelet transform (DWT) are implemented to extract features from fault signals. If extracted features have large variations, normalization is required to convert them on a common scale. Here, the error and accuracy in classification of line to line fault current (L-L fault) is evaluated if z-score normalized and not normalized inputs are used for backpropagation neural network (BPNN) classifier. Experiments are conducted on IEEE 13 bus test distribution system simulated in MATLAB. Results clearly show significant increase in accuracy of fault classification with considerable reduction in error to classify the faults due to normalization of input data.