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

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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