In this paper, visual detection and classification of the non-stationary power signals are demonstrated by well-known transform called generalized synchrosqueezing transform. The wavelet based time frequency represent...
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ISBN:
(纸本)9788132222026;9788132222019
In this paper, visual detection and classification of the non-stationary power signals are demonstrated by well-known transform called generalized synchrosqueezing transform. The wavelet based time frequency representation gives poor quality and understandability, hence the proposed synchrosqueezing transform is an effective method to get better quality and readability of the wavelet-based TFR by summarizing along the frequency axis. Different feature vectors have been extracted from the frequency contour of the generalized synchrosqueezing transform and these feature vectors applied as input to the reformulated fuzzy c-means algorithm for automaticclassification.
This paper presents a new approach for processing various non-stationary power quality waveforms through a Fast S-Transform with modified Gaussian window to generate time-frequency contours for extracting relevant fea...
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This paper presents a new approach for processing various non-stationary power quality waveforms through a Fast S-Transform with modified Gaussian window to generate time-frequency contours for extracting relevant feature vectors for automatic disturbance pattern classification. The extracted features are then clustered using Bacterial Foraging Optimization algorithm (BFOA) based fuzzy decision tree to give improved classification accuracy in comparison to the fuzzy decision tree alone. To circumvent the problem of premature convergence of BFOA and to improve classification accuracy further, a hybridization of BFOA (Bacterial Foraging Optimization algorithm) with another very popular optimization technique of current interest called Differential Evolution (DE) is presented in this paper. For robustness the mutation loop of the DE algorithm has been made variable in a stochastic fashion. This hybrid algorithm (chemotactic Differential Evolution algorithm (cDEA)) is shown to overcome the problems of slow and premature convergence of BFOA and provide significant improvement in power signal pattern classification. (c) 2012 Elsevier B.V. All rights reserved.
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 si...
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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.
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