Machine Learning for PV System Operational Fault Analysis: Literature Review

EasyChair Preprint 6585

Abstract

This review paper aims to discover the research gap and assess the feasibility of a holistic approach for photovoltaic (PV) system operational fault analysis using machine learning (ML) methods. The analysis includes the detection and diagnosis of operational faults in a PV system. Even if standard protective devices are installed in PV systems, they fail to clear various faults because of low current during low mismatch levels, high impedance fault, low irradiance, etc. This failure will increase the energy loss and endanger the PV system's reliability, stability, and security. As a result of the ML method's ability to handle a non-linear relationship, distinguishing features with similar signatures, and their online application, they are getting attractive in recent years for fault detection and diagnosis (FDD) in PV systems.  In this paper, a review of literature on ML-based PV system FDD methods is provided. It is found that considering their simplicity and performance accuracy, Artificial Neural networks such as Multi-layer Perceptron are the most promising approach in finding a central PV system FDD. Besides, the review paper has identified main implementation challenges and provides recommendations for future work.

Keyphrases: PV system fault, Transfer Learning, ensemble learning, fault detection and diagnosis, machine learning

@booklet{EasyChair:6585,
  author    = {Tarikua Mekashaw Zenebe and Ole-Morten Midtgård and Steve Völler and Ümit Cali},
  title     = {Machine Learning for PV System Operational Fault Analysis: Literature Review},
  howpublished = {EasyChair Preprint 6585},
  year      = {EasyChair, 2021}}