Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques

Abstract

Nowadays, to meet the vision of employing 100% renewable-based electricity generation, the conventional power system is evolving into power electronics-dominated grid (PEDG). This transition leads to an amplified complexity and significance for device and system-level control schemes to maintain resiliency, reliability, and operational stability. Recently, in various fields of engineering and science, the machine learning (ML)-based schemes have exhibited outstanding performance. Considering abundance of data in the PEDG, MLbased approaches illustrate promising potential to be adopted in this new energy paradigm. Similarly, the MLinspired approaches have been attracting many researchers in power electronics and power systems fields, who are trying to solve the challenges posed by the PEDG concept. This article presents cutting-edge ML applications in the PEDG and provides a futuristic research roadmap.