Towards Efficient Operation and Maintenance of Wind Farms: Leveraging AI for Minimizing Human Error
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Abstract
To effectively compete with other renewable energy sources, there remains a critical need to further decrease the Levelized Cost of Energy of Wind Farms (WFs). A promising way to achieve this objective is by minimizing the downtime of wind turbines (WTs) through effective Inspection and Maintenance (I&M) activities. Conventionally, I&M plans have predominantly relied on CM/SCADA data obtained from the physical components of turbines, with data analytics and machine learning (ML) techniques being employed to predict their performance and maintenance needs. However, statistics indicate that nearly 40% of WT failures can be traced back to HFs. These include aspects such as skills, knowledge, communication, and even the broader organizational culture. This paper delves into the importance of integrating HFs in the I&M of WFs to optimize turbine performance, enhance safety, and reduce downtime.
Firstly, we briefly discussed various Human Reliability Analysis (HRA) methods with special emphasis on Performance Shape Factors (PSFs). We then identify key human factors (HFs) that are vital for performing O&M tasks. For this, we have prepared a questionnaire to get qualitative input from technicians and also done a thorough literature review. E.g., some of the HFs that stand out include the ergonomics of tools and workspace designs tailored to technicians' needs, the cognitive load placed on operators during system monitoring and diagnostics, continuous training to handle evolving challenges, effective communication channels, and safety protocols designed with human behavior in mind. We then propose a novel framework for developing a computer vision-based recommendation system that can guide the technicians to perform the maintenance effectively thus minimizing the HE.
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Offshore Wind Farm, Human Factors, Computer Vision
https://assets.kpmg.com/content/dam/kpmg/dk/pdf/DK2019/11/The-socioeconomic-impacts-of-windenergy_compressed.pdf
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