Evaluation of wavelet-based feature extraction methods for detection and classification of power quality disturbances
National Technological of Mexico, Technological Institute of Morelia, Morelia, México.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2022, 07(02), 220-229.
Article DOI: 10.30574/wjaets.2022.7.2.0165
Publication history:
Received on 09 November 2022; revised on 24 December 2022; accepted on 26 December 2022
Abstract:
The appearance of Power Quality Disturbances can cause serious damage to the utility grid. Their detection and identification are two of the major problems related to the improvement of Power Quality. This paper presents an evaluation of different combinations of wavelet-based features for the detection and classification of eight types of Single Power Quality Disturbances. A set of disturbances was generated in MATLAB through their mathematical models. The detection stage was performed using Multiresolution Analysis. The extracted features were normalized by Z-score to serve as input to four different classifiers: Multilayer Perceptron, K-Nearest Neighbors, Probabilistic Neural Network, and Decision Tree. The combination of Shannon Entropy and Log-Energy Entropy was found the best with the highest accuracy in all cases. Furthermore, the normalization stage has an impact on classification as it improves accuracy regardless of the classifier used. This fact makes it possible to reduce the computational expense by using only two types of features without compromising the accuracy.
Keywords:
Power Quality Disturbances; Wavelet-Based Features; Detection; Classification
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0