Comparative Evaluation of Deep Learning CNN Techniques for Power Quality Disturbance Classification

Abstract

For the power system to be stable and reliable, power quality disturbances (PQDs) must be classified. In this work, deep learning was implemented for the purpose of categorizing PQDs. The transfer learning techniques such as ResNet-50, AlexNet, and GoogLeNet were compared and evaluated for the suitability of classifying PQD signals. Accuracy, classification probability, and explainability through GradCAM- an explainable AI technique was evaluated as a grading reference for the comparative analysis. Examination of the three criteria revealed ResNet-50 as the best among all the three architectures for classifying PQD signals since depending on the accuracy.