Machine Learning for Photovoltaic Systems Condition Monitoring:A Review

Berghout, Tarek and Benbouzid, Mohamed and Ma, Xiandong and Durovic, Sinisa and Mouss, Leïla-Hayet (2021) Machine Learning for Photovoltaic Systems Condition Monitoring:A Review. In: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE. ISBN 9781665402569

[img]
Text (TF-022608)
TF_022608.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (256kB)

Abstract

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
163010
Deposited By:
Deposited On:
08 Dec 2021 12:06
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Jan 2022 07:41