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

## TL;DR

This paper introduces a novel pattern recognition method for power quality disturbances using Empirical Mode Decomposition and higher order statistics, combined with a k-NN classifier, achieving high accuracy and robustness.

## Contribution

It proposes a new feature extraction approach using HOS in the EMD domain and demonstrates its effectiveness with k-NN for power disturbance classification.

## Key findings

- k-NN achieved highest accuracy among classifiers.
- The method outperformed existing techniques in noisy environments.
- The approach is computationally efficient and robust.

## Abstract

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.