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Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future

Chatterjee, Joyjit; Dethlefs, Nina

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Abstract

Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artificial intelligence (AI) techniques, especially deep learning in 2020. In this article, we utilise statistical computing to present a scientometric review of the conceptual and thematic evolution of AI in the wind energy sector, providing evidence-based insights into present strengths and limitations of data-driven decision making in the wind industry. We provide a perspective into the future and on current key challenges in data availability and quality, lack of transparency in black box-natured AI models, and prevailing issues in deploying models for real-time decision support, along with possible strategies to overcome these problems. We hope that a systematic analysis of the past, present and future of CBM and performance assessment can encourage more organisations to adopt data-driven decision making techniques in O&M towards making wind energy sources more reliable, contributing to the global efforts of tackling climate change.

Citation

Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable & sustainable energy reviews, 144, Article 111051. https://doi.org/10.1016/j.rser.2021.111051

Journal Article Type Review
Acceptance Date Mar 25, 2021
Online Publication Date Apr 10, 2021
Publication Date 2021-07
Deposit Date Apr 10, 2021
Publicly Available Date Mar 28, 2024
Journal Renewable and Sustainable Energy Reviews
Print ISSN 1364-0321
Electronic ISSN 1879-0690
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 144
Article Number 111051
DOI https://doi.org/10.1016/j.rser.2021.111051
Keywords Wind turbines; Operations & maintenance; SCADA; Scientometric review; Artificial intelligence; Machine learning; Condition-based monitoring
Public URL https://hull-repository.worktribe.com/output/3750901

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