Classification and Detection of Power Quality Disturbances using Support Vector Machine

This paper explores the classification of power quality disturbances using machine learning models. It is started by generating signals that represent different types of disturbances and then processing them using Discrete Wavelet Transform with Daubechies wavelets to extract important features. A dataset has been created from these processed signals and used for training and testing with machine learning models. Among various techniques, Support Vector Machines (SVM) is opted for classification due to its accuracy and efficiency. Since power quality disturbances may lead to serious issues like system instability and equipment failure, having a reliable classification system is crucial. Here, SVM Kernel performs well while categorizing power quality disturbances.