Non-stationary power signal processing for pattern recognition using HS-transform |
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| Authors: | B Biswal PK Dash BK Panigrahi |
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| Affiliation: | 1. Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar 751024, Orissa, India;2. Center for Electrical Sciences, Bhubaneswar, India;3. Indian Institute of Technology, New Delhi, India;1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China;2. School of Information Engineering, Hubei University for Nationalities, Enshi, China;1. Malaviya National Institute of Technology, Department of Electronics and Communication, Jaipur, India;2. Maharaja Surajmal Institute of Technology, Department of Electronics and Communication, Delhi, India;1. Department of Neurology, University Hospital of Geneva, Switzerland;2. Department of Neurosurgery, Inselspital, Bern, Switzerland;1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Electrical Engineering, College of Electrical Engineering & Computer Science, National Cheng Kung University, Tainan City 70101, Taiwan;1. Institute of Electrical Engineering, Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, China;2. School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Hebei, China;3. Institute of Mechanical Engineering, Yanshan University, Hebei, China |
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| Abstract: | A new approach to time-frequency transform and pattern recognition of non-stationary power signals is presented in this paper. In the proposed work visual localization, detection and classification of non-stationary power signals are achieved using hyperbolic S-transform known as HS-transform and automatic pattern recognition is carried out using GA based Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by HS-transform. Various non-stationary power signal waveforms are processed through HS-transform with hyperbolic 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 optimized using genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95.75% using Fuzzy C-means and genetic based Fuzzy C-means algorithm, respectively. |
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