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.

在这项研究中，引入了在特征提取阶段结合二维Riesz变换（RT）和在特征选择阶段结合k最近邻（KNN）算法的多目标灰狼优化器（MOGWO）进行分类的新方法质量（PQ）干扰。确定最合适的特征组后，使用机器学习方法创建分类模型。尽管在PQ干扰的分类阶段本质上使用了一维（1D）信号处理方法，但是可以观察到文献中已经进行了包括二维（2D）信号处理的研究。之所以使用2D信号处理方法，是因为它具有良好的特征多样性并可以创建一个好的模型。在这项研究中，首先，PQ干扰事件数据是通过综合和实验方式收集的。一维信号被转换为二维信号以应用2D-RT。在2D-RT中，可以获得12个子带矩阵，以找到一个2D矩阵的更好特征。为每个波段计算15个统计和基于图像的特征。一个子带矩阵共获得180个特征。此时，通过MOGWO-KNN方法，其目的是通过选择2D-RT获得的最合适的特征来创建具有高性能的简单分类模型。建立了基于KNN，SVM，MLP和集成学习器方法的模型，以研究分类精度是否更高。还对包含噪声（40 dB，30 dB，20 dB噪声水平）和多个事件的数据进行了仿真研究。该模型可以将18种PQ干扰中的9种多重干扰分类。同时，创建了一个可以对嘈杂情况进行分类的健壮模型。结果表明，与文献中的方法相比，所提出的PQ扰动分类方法在仿真和真实数据方面均具有较高的性能。