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• W2805986364 abstract "The penetration of wind energy into power systems is steadily increasing; this highlights the importance of operations and maintenance, and specifically the role of condition monitoring. Wind turbine power curves based on supervisory control and data acquisition data provide a cost-effective approach to wind turbine health monitoring. This study proposes a Gaussian process (a non-parametric machine learning approach) based algorithm for condition monitoring. The standard IEC binned power curve together with individual bin probability distributions can be used to identify operational anomalies. The IEC approach can also be modified to create a form of real-time power curve. Both of these approaches will be compared with a Gaussian process model to assess both speed and accuracy of anomaly detection. Significant yaw misalignment, reflecting a yaw control error or fault, results in a loss of power. Such a fault is quite common and early detection is important to prevent loss of power generation. Yaw control error provides a useful case study to demonstrate the effectiveness of the proposed algorithms and allows the advantages and limitations of the proposed methods to be determined." @default.
• W2805986364 created "2018-06-13" @default.
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• W2805986364 date "2018-07-09" @default.
• W2805986364 modified "2023-10-16" @default.
• W2805986364 title "SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes" @default.
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• W2805986364 doi "https://doi.org/10.1049/iet-rpg.2018.0156" @default.
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