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

• Published in: International journal of energy research Vol. 46; no. 15; pp. 21619 - 21642
• Main Authors: Afridi, Yasir Saleem, Ahmad, Kashif, Hassan, Laiq
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.12.2022
• Subjects: Algorithms; Artificial intelligence; Availability; big data; condition‐based monitoring; Depletion; Downtime; Energy; Energy security; Fault detection; Fossil fuels; Hybrid systems; hydro power; machine learning; Maintenance; Predictive maintenance; prognostics; Renewable energy; Renewable energy sources; renewable energy systems; Security; wind power
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1002/er.7100

Summary 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. In contrast to existing surveys, this paper provides an in‐depth and detailed survey of the literature on prognostic maintenance of renewable energy systems, including Hydro, Wind, and Solar Power systems, with a particular focus on approaches, challenges (such as security, interpretability, and data‐related issues), and future research directions.