Wind Turbine Anomaly Detection Based on SCADA Data

Francisco Bilendo (), Hamed Badihi () and Ningyun Lu ()
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Francisco Bilendo: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
Hamed Badihi: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
Ningyun Lu: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics

A chapter in Handbook of Smart Energy Systems, 2023, pp 2279-2302 from Springer

Abstract: Abstract Wind turbine condition monitoring based on supervisory control and data acquisition (SCADA) has attracted considerable research interest in recent years. Frequently reported challenges include the fact that most wind turbine SCADA parameters are highly dependent on the operating conditions, such as wind speed and environmental temperature, along with the control actions imposed on the wind turbine. Anomaly detection, therefore, plays a pivotal role in mitigating the impacts of these operating conditions upon the real wind turbine condition parameters for condition monitoring purpose; in several cases, it is employed as the main approach to investigate impending faults or to ensure whether the wind turbine or its components are functioning according to the norm. This chapter provides a survey on the state of the art of condition monitoring of wind turbines; in particular, we investigate and systematically review the most recent innovations mainly in the subfield of anomaly detection.

Keywords: Wind turbines; Condition monitoring; SCADA; Anomaly detection (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_35

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DOI: 10.1007/978-3-030-97940-9_35

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