Journal Article

Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Ouassaid, Mohammed, Oubrahim, Zakarya, Amirat, Yassine, Benbouzid, Mohamed

2023

Overview

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

• Publisher: MDPI AG,MDPI
• Subject: Alternative energy sources / Artificial intelligence / Classification / detection / disturbances characterization / Efficiency / Electric power systems / Energy management systems / Engineering Sciences / estimation / Feature selection / Machine learning / Methods / Morocco / Object recognition (Computers) / Optimization algorithms / Pattern recognition / power quality monitoring / Quality standards / Renewable resources / Signal processing / smart grid