| Summary: | The Supervisory Control and Data Acquisition (SCADA) system in wind turbines generates substantial data that remains underutilized in terms of wind farm operation and maintenance (O&M). Numerous fault detection methods leveraging SCADA data are being extensively researched to reduce O&M costs. The detection methods are revolutionizing wind farm O&M strategies, shifting from scheduled passive detection to predictive active detection, with the potential to significantly reduce spare parts and labor costs. This paper presents a systematic review of wind turbine fault detection methods based on concept drift and distance metrics, employing the PRISMA methodology. The selected literature is analyzed from three perspectives: fault components, modeling methods, and data sources. Additionally, this review addresses research questions related to current trends, concept drift applications, and distance metric utilization in wind turbine fault detection. Lastly, it provides valuable insights for researchers and industry practitioners in wind energy engineering to explore future research and development in fault detection techniques for enhancing the reliability and efficiency of wind turbine operations.
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