Power Quality Engineering Evaluation and Generalization of Deep Learning

Abstract

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in terms of application, type of data, and learning technique. This work shows that the majority of the deep learning applications to power quality are based on unrealistic synthetic data and supervised learning without proper labelling. Some applications with deep learning have already been solved by previous machine learning methods or expert systems. The main barriers to implementing deep learning to power quality are related to lack of novelty, low transparency of the deep learning methods, and lack of benchmark databases. This work also discusses that even with automatic feature extraction by deep learning methods, power quality expert knowledge is still needed to implement and analyses the results. The main research gaps identified in this work are related to the applications of semi-supervised learning, explainable deep learning and hybrid approaches combining deep learning with expert systems. Providing a stronger level of collaboration between grid stakeholders and academia to monitor power quality events, properly labeling and enlarging datasets for deep learning methods, outlining the end-to-end decision-making of deep learning methods, and offering open-access databases for comparison purposes are some suggestions for overcoming the current limitations.