Abstract
This paper presents a novel approach based on the harmony search algorithm to optimally determine suitable wavelet functions and wavelet decomposition levels for accurate fault classification in transmission lines, unlike previous works in which only one arbitrary wavelet function is used. Discrete wavelet transform is used to extract the features in the voltage and/or current signals using the identified wavelet functions. Machine learning classifiers are then used to build a proper classification model to automate the fault classification process. The results of applying the proposed approach are presented and discussed, and conclusions are drawn.
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