Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

1.College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China;2.Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China

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    The operating conditions of wind turbines (WTs) in the same wind farm (WF) may share similarities due to their shared manufacturing process, control strategy, and operating environment. However, the similarities of WTs are seldom considered in WT anomaly detection, resulting in the disregard of useful information. This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition (SCADA) data of multiple WTs in the same WF. First, a similarity assessment method based on a comparison of different observation time series is proposed, which objectively quantifies the similarities of WT operating conditions. Then, the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory (LSTM) algorithm. LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT. Finally, an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies. The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

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  • Received:November 22,2022
  • Revised:March 06,2023
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  • Online: May 20,2024
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