Multiple Power Quality Event Detection and Classification using Wavelet Transform and Random Forest Classifier

TL;DR

This paper presents a novel method combining wavelet transform and Random Forest classifier for detecting and classifying multiple power quality events, demonstrating superior accuracy over other machine learning methods in simulated MATLAB environments.

Contribution

The paper introduces a new technique integrating wavelet transform with Random Forest for power quality event detection and classification, tested on multiple event types.

Findings

Random Forest outperforms SVM and KNN in accuracy.

Wavelet features effectively distinguish different PQ events.

Method successfully classifies single and multi-stage PQ events.

Abstract

In this paper a technique for detection of multiple power quality (PQ) events is illustrated. An algorithm based on wavelet transform and Random Forest based classifier is proposed in this paper. The developed technique is implemented on 11 different power quality events consisting of single stage power quality events such as sag, swell, flicker, interruption and multi stage power quality events such as harmonics combined with sag, swell, flicker, interruption. PQ events are simulated in MATLAB using standard IEEE-1159 standard. Significant features of PQ events are extracted using wavelet transform and used to train random forest based classifier. The efficiency of Random Forest Based classifier is compared with other widely used machine learning algorithms such as K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). From confusion matrix of different algorithms it is concluded that Random Forest shows superior classification accuracy as compared to SVM and KNN.