Comparative Analysis of Convolutional Neural Network and Multilayer Perceptron on Power Quality Classification

Abstract

Power quality classification is essential for identifying and categorizing various power quality issues within electrical systems. With the increasing integration of electronic devices into power grids, including converters, rectifiers, and inverters, the significance of real-time monitoring and fault prediction has grown. This paper explores the application of image classification techniques, specifically Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP), in addressing power quality issues. Using a dataset consisting of signals representing different power quality conditions, models were trained and evaluated based on accuracy and loss metrics. The results indicate that while CNN achieved the highest accuracy and lowest loss, MLP models demonstrated efficiency in terms of computational resources. Additionally, the study discusses the potential of image classification technology in enhancing power quality monitoring and management, with implications for improving energy utilization efficiency and supply quality. The findings highlight the importance of leveraging deep learning techniques for addressing complex power quality challenges and paving the way for the intelligent and automated development of power systems.