An intelligent algorithm for autorecognition of power system faults using superlets

Abstract A time-frequency resolution technique based recognition of power system faults is proposed in the present work. Superlet transformation, a modified and super-resolution form of the wavelet transform, has been used to recognize the type of power system faults occurring in an interconnected power system. The Superlets were tested for sample power system disturbances and found advantageous. An IEEE-9 Bus system has been used to implement the proposed technique wherein it was observed that unique signatures were obtained for each type of fault. Further, to automatically recognize the type of power system faults, a Support Vector Machine (SVM) classifier has been introduced. The SVM is provided with the inputs from the extracted parameters of the proposed Superlets technique. It was found that the proposed methodology of Superlets with SVM has excelled in comparison with other techniques and provided 100% accuracy in the classification of all the events considered.

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