Classification of power quality disturbances by 2D-Riesz Transform, multi-objective grey wolf optimizer and machine learning methods

Abstract

In this study, a new method combined with two-dimensional Riesz Transform (RT) in feature extraction stage and Multi-Objective Grey Wolf Optimizer (MOGWO) with k-Nearest Neighbor (KNN) algorithm in the feature selection stage is introduced to classify Power Quality (PQ) disturbances. After determining the most suitable feature group, classification models are created by using machine learning approaches. Although one-dimensional (1D) signal processing methods by nature are used in the classification stage of PQ disturbances, it is observed that studies have developed in the literature including two-dimensional (2D) signal processing. 2D signal processing approach is used because it gives good feature diversity and leads to creation a good model. In this study, firstly PQ disturbances events data is collected synthetically and experimentally. 1D signals are converted to 2D signals to apply 2D-RT. In 2D-RT, it is obtained 12 sub bands matrices to find better features for one 2D matrix. 15 statistical and image-based features are calculated for each band. Totally 180 features are obtained for one sub bands matrix. At this point, with the MOGWO-KNN method, it is aimed to create a simple classification model with high performance by selecting the most suitable features obtained by 2D-RT. The models based on KNN, SVM, MLP and ensemble learner methods are created to investigate if there is a better classification accuracy or not. The simulation study is also done for data consists of noisy (40 dB, 30 dB, 20 dB noise levels) and multiple events. The model can classify 9 types of multiple disturbances in 18 PQ disturbances. At the same time, a robust model that classify even noisy situations is created. It is showed that the proposed PQ disturbances classification method gives high performance compared to the methods in the literature for both simulations and real data.