Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization

This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized$S$-transform with modified Gaussian window to generate time–frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm, and finally, the algorithm is extended using either particle swarm optimization or genetic algorithm to refine the cluster centers.