A review of convolutional neural networks for classifying power quality problems using Keras API

Abstract

The major causes of electric power quality (PQ) problems are mainly due to the increased utilization of nonlinear loads, capacitor and load switching events, transformer energization, and occurrence of assorted faults at the distribution corridor. The problems often introduce harmonics and other waveform anomalies like voltage sags, voltage swells and interruptions along the power systems. A timely classification of such problems is important in understanding their impact on costly power system economy. The paper explores comprehensive review of PQ issues, operational concept of convolutional neural network (CNN) and its utilization in solving PQ problems. Novel deep learning (DL) approach using variant of DenseNet CNN technique in Keras API platform is deployed to extract the features of, and classify PQ problems. The proposed technique improves classification performance with an accuracy of 99.96%. It shows remarkable improvement over the traditional techniques in the literature which were 73.53% to 99.92% accurate for a period from 2018 to 2023. The most promising part of the method is the improvement shown in the classification performance when compared with that obtained in the literature. The technique can also be applied in real time to cater for real PQ problems.