Deep Learning for Power Quality Event Detection and Classification Based on Measured Grid Data

Abstract

Energy consumption has increased over the years, and, due to the dependency on fossil energy, alternative and renewable energy sources have been integrated to address environmental concerns. However, it is important to maintain the efficiency, reliability, and safety of the power grid amid the integration of different energy sources. IEEE and IEC standards regulate power quality (PQ) and define thresholds for PQ events that traditionally have been detected through specialized algorithms. With machine learning, it is possible to detect and classify those events using deep-learning (DL) techniques that teach systems to learn by example, providing a more scalable approach to classification. Published studies in PQ with DL algorithms to detect disturbances rely only on simulated signals or imposed disturbances. In this article, a DL neural network is trained and used to detect and classify PQ events from a database built with real electrical power grid signals measured with monitoring devices.