Cited by 140Reliability and Maintenance OptimizationMachine Fault Diagnosis TechniquesPower System Reliability and Maintenance

Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

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Afridi, Yasir Saleem, Ahmad, Kashif, Hassan, Laiq·2022·International Journal of Energy Research

Abstract

Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end-users is strongly dependent on an effective and fault-resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data-related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.

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  1. Jay Lee, Ramzi Abujamra, Andrew KS Jardine, Daming Lin, and Dra- gan Banjevic. An integrated platform for diagnostics, prognostics and maintenance optimization. Proceedings of the intelligent maintenance systems, pages 15-27, 2004.
  2. LR Kinclaid. Evolution of condition monitoring and the management of maintenance. In Proceedings of International Conference on Condition Monitoring, volume 1, pages 13-21, 1987.
  3. EJ Wiggelinkhuizen, TW Verbruggen, H Braam, LWMM Rademakers, J Xiang, S Watson, G Giebel, and E Norton. Condition monitoring for offshore wind farms. In This report has been presented at the EWEC2007 conference, 2007.
  4. PJ Tavner, Fabio Spinato, GJW Van Bussel, and Efstathios Koutoulakos. Reliability of different wind turbine concepts with rele- vance to offshore application. In European Wind Energy Conference, page 9, 2008.
  5. Wenxian Yang, PJ Tavner, and CJ Crabtree. An intelligent approach to the condition monitoring of large scale wind turbines. In Proceedings of the European Wind Energy Conference and Exhibition (EWEC2009), Marseille, France, pages 16-19, 2009.
  6. Mengyan Nie and Ling Wang. Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox. Procedia Cirp, 11:287-290, 2013.
  7. B Hahn, M Durstewitz, and K Rohrig. Reliability of wind turbines, experiences of 15 years with 1,500 wts, in 'wind energy. In Proceedings of the Euromech Colloquium, pages 329-332. Springer-Verlag Berlin Heidelberg, 2007.
  8. BCP Lau and EWM Ma. Pecht., m.(2012). review of offshore wind turbine failures and fault prognostic methods. Proceedings of the IEEE, pages 1-5, 2012.

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Authors
Afridi, Y. S., Ahmad, K., & Hassan, L.
Affiliations
  • University of Engineering and Technology Peshawar (PK)
  • Hamad bin Khalifa University (QA)
Corresponding author
Afridi, Yasir Saleem
Venue
International Journal of Energy Research
Publisher
Wiley
Published
2021-07-31
Volume
46
Issue
15
Pages
21619-21642
ISSN
  • 0363-907X (ISSN-L)
  • 1099-114X
OpenAlex
W3157656588
Cited by
140
References
133
Citations by year
2026: 43 · 2025: 54 · 2024: 24 · 2023: 13 · 2022: 3 · 2021: 3
FWCI
8.02
Type
journal article
Language
en
Open access
Locations
  • International Journal of Energy Research (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Power System Reliability and Maintenance
Keywords
predictive maintenance, renewable energy systems, artificial intelligence, machine learning, fault detection, operation and maintenance, hybrid generation system, data quality, data auditing, feature engineering, interpretability, data security

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