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The aim of this paper is to propose a new approach for the patternrecognition of power quality (PQ) disturbances based on Empirical modedecomposition (EMD) and $k$ Nearest Neighbor ($k$-NN) classifier. Since EMDdecomposes a signal into intrinsic mode functions (IMF) in time-domain withsame length of the original signal, it preserves the information that is hiddenin Fourier domain or in wavelet coefficients. In this proposed method, powersignals are decomposed into IMFs in EMD domain. Due to the presence ofnon-linearity and noise on the original signal, it is hard to analyze them bysecond order statistics. Thus, an effective feature set is developedconsidering higher order statistics (HOS) like variance, skewness, and kurtosisfrom the decomposed first three IMFs. This feature vector is fed into differentclassifiers like $k$-NN, probabilistic neural network (PNN), and radial basisfunction (RBF). Among all the classifiers, $k$-NN showed higher classificationaccuracy and robustness both in training and testing to detect the PQdisturbance events. Simulation results evaluated that the proposed HOS-EMDbased method along with $k$-NN classifier outperformed in terms ofclassification accuracy and computational efficiency in comparison to the otherstate-of-art methods both in clean and noisy environment.

A Machine Learning Based Classification Approach for Power Quality Disturbances Exploiting Higher Order Statistics in the EMD Domain