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

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge-Based and Intelligent Engineering Systems IOS Press

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