Classification of Power Quality Disturbances Using GA Based Optimal Feature Selection

Authors:
K. R. Krishnanand;Santanu Kumar Nayak;B. K. Panigrahi;V. Ravikumar Pandi;Priyadarshini Dash
Affiliations:
Department of Electrical Engineering, Silicon Institute of Technology, Bhubaneswar, India;Department of Electrical Engineering, Silicon Institute of Technology, Bhubaneswar, India;Department of Electrical Engineering, Indian Institute of Technology, Delhi, India;Department of Electrical Engineering, Indian Institute of Technology, Delhi, India;Department of Electrical Engineering, NIT Durgapur, India
Venue:
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Year:
2009

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Abstract

This paper presents a novel technique for power quality disturbance classification. Wavelet Transform (WT) has been used to extract some useful features of the power system disturbance signal and Gray-coded Genetic Algorithm (GGA) have been used for feature dimension reduction in order to achieve high classification accuracy. Next, a Probabilistic Neural Network (PNN) has been trained using the optimal feature set selected by GGA for automatic Power Quality (PQ) disturbance classification. Considering ten types of PQ disturbances, simulations have been carried out which show that the combination of feature extraction by WT followed by feature reduction using GGA increases the testing accuracy of PNN while classifying PQ signals.