Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future

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.

arXiv commentThis is a preprint version of the accepted manuscript in the Renewable and Sustainable Energy Reviews journal, shared under a CC-BY-NC-ND license. The final published version can be found at: https://doi.org/10.1016/j.rser.2021.111051

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Cited by in corpus (4)

• An XAI framework for robust and transparent data-driven wind turbine power curve models
• Transfer learning applications for anomaly detection in wind turbines
• A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast
• Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures