Performance Analysis of Machine Learning Algorithms on Power Quality Disturbances Classification

The importance of the concept of power quality in electrical power systems is increasing. This situation causes the acceleration of work on detecting and eliminating power quality events. It is possible to automatically detect and classify these events using signal processing techniques and machine learning systems. In this study, the performances of Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naive Bayes (NB) classification algorithms were compared among themselves and with the literature. Signals containing 15 single and multiple power quality events were used for classification. Discrete Wavelet Transform (DWT) technique was preferred to extract the features of the signals. The data sets obtained by feature extraction were divided into test data and training data with a ratio of 20%, 30%, 40%. Thus, the classifiers were trained and tested with data sets of different sizes and their performances were evaluated. The highest accuracy rate was obtained as 99.75% with DT when 20% of the data set of noiseless signals was used as test data. The lowest accuracy rate was obtained as 30.6% with KNN when 30% of the data set of signals with 10dB noise was used as test data. In addition, the performances of the classifiers were examined by performing a 5-fold cross validation test and the average accuracy rates obtained from this were compared with other studies in the literature.