Detection and power quality disturbance classification based on wavelet packet decomposition and modified immune optimization algorithm

In this paper, a new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed. In this paper, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition. Automatic pattern recognition is carried out through Modified Immune Optimization Algorithm (MIOA) based Reformulated Fuzzy C-means Algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by wavelet packet decomposition (WPD). Various non-stationary power signal waveforms are processed through wavelet packet decomposition to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Reformulated Fuzzy C-means Algorithm and finally the algorithm is extended using Artificial Immune and Modified Immune Optimization Algorithm (MIOA) respectively to refine the cluster centers. Results of simulation and analysis demonstrate that the proposed MIOA method achieves higher classification rate, better convergence property.