Time frequency analysis and power signal disturbance classification using support vector machine and differential evolution algorithm

Authors:
B. Biswal;M. K. Biswal;P. K. Dash;S. Mishra
Affiliations:
GMR Institute of Technology, Rajam, Srikakulam Dist., India;Silicon Institute of Technology, Bhubaneswar, India;S'O'A University, Bhubaneswar, India;Indian Institute of Technology, Delhi, India
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2012

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Abstract

The paper proposes a new approach for Time frequency analysis using modified time-time transform TT-transform for recognizing non-stationary power signal disturbance patterns. The TT-transform is derived from the well known S-transform ST and uses a new window function with its width inversely proportional to the frequency raised to a power 'c', varying between 0 and 1. The power disturbance signals after being processed by the TT-transform yields features, which are used for automatic recognition of disturbances; with the help of kernel based support vector machine SVM algorithm. Further to improve the classification performance of the TT-SVM based pattern recognizer, a differential evolution optimization algorithm DEOA is used. Several test cases are provided to prove the significant improvement in recognition, accuracy and drastic reduction of support vectors.