A hybrid ant colony optimization technique for power signal pattern classification

This paper presents a novel clustering and pattern classification of power signal disturbances using a variant of S-transform, which is termed as a phase corrected wavelet transform. This variant is obtained by taking the inverse Fourier transform of S-transform and is known as time-time transform (TT-transform). The output from the TT-transform based power signal processing is a set of relevant features that is used for visual localization, detection, and disturbance pattern classification. The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time-frequency distribution, the generalized S-transform. The extracted features are the input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree for power signal disturbance pattern classification. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using a hybrid ant colony optimization technique (HACO). Further a comparative assessment of power signal disturbance pattern classification accuracy for different population based optimization approach like the genetic algorithm (GA) and particle swarm optimization technique are presented in this paper. The various computational simulations presented in this paper reveal significant improvement in the pattern classification accuracy, the average number of function evaluations and processing time, etc. (c) 2010 Elsevier Ltd. All rights reserved.