  The aim of this paper is to propose a new approach for the pattern
recognition of power quality (PQ) disturbances based on Empirical mode
decomposition (EMD) and $k$ Nearest Neighbor ($k$-NN) classifier. Since EMD
decomposes a signal into intrinsic mode functions (IMF) in time-domain with
same length of the original signal, it preserves the information that is hidden
in Fourier domain or in wavelet coefficients. In this proposed method, power
signals are decomposed into IMFs in EMD domain. Due to the presence of
non-linearity and noise on the original signal, it is hard to analyze them by
second order statistics. Thus, an effective feature set is developed
considering higher order statistics (HOS) like variance, skewness, and kurtosis
from the decomposed first three IMFs. This feature vector is fed into different
classifiers like $k$-NN, probabilistic neural network (PNN), and radial basis
function (RBF). Among all the classifiers, $k$-NN showed higher classification
accuracy and robustness both in training and testing to detect the PQ
disturbance events. Simulation results evaluated that the proposed HOS-EMD
based method along with $k$-NN classifier outperformed in terms of
classification accuracy and computational efficiency in comparison to the other
state-of-art methods both in clean and noisy environment.
