Classification of Power Quality Disturbances using Wavelet Packet Information Entropy Feature Vectors and Probabilistic Neural Network

Abstract

: This paper illustrates the automatic recognition of power quality disturbances (PQDs) using Wavelet Packet Tuned Probabilistic Neural Network ( WP-PNN ) structure. Eight statistical parameters are extracted from the WP by decomposing a signal into the fourth level. The PNN is simulated with these statistical parameters for performance appraisal. The Information Gain (IG) feature selection algorithm has been applied to rank the feature subsets based on high IG entropy. 16 significant WP-IG features are extracted from 128 features computed from the WP statistical coefficients. The objective is to discard redundant data for better accuracy and lower computational complexity. A two-stage experimental analysis has been carried out to validate the WP-IGPNN structure. Initially, t he PQ data set is procured by regularly varying the parameters of mathematical models and choosing the best features using IG to experience the highest accuracy. Finally, the coefficient values which are not chosen in the earlier case have been used to generate a new data set. The classification accuracy has been observed using the new data set with the same chosen feature set as used in the first stage. The noisy signals are also investigated to simulate the classifier to validate the proposed WP-IGPNN structure.