Application of Signal Processing and Machine learning on Power Quality Disturbance with RE Penetration: A Review

Abstract

As more source of renewable energy are incorporated into the traditional system to fulfil global energy demand and the decarbonization goal, there is growing worry over power excellence. Due to the variable output of renewable energy sources (RES) as well as the interfacing converters, the power quality (PQ) disturbance is observed to be more prevalent as the addition of renewable energy into the network increases. In orderto deliver clean power to end users, it is necessary to notice and reduce power quality disturbance (PQD). In this article, various methods for identifying and categorizing PQ instabilities caused by the penetration of RES in the system are evaluated. This review paper's primary goal is to describe various approachesfor the feature removal and categorization of PQ instabilities, as well as additional strategies for PQ disturbance reduction, such as forecasting for renewable energy sources.