access icon free Robust fault detection approach for wind farms considering missing data tolerance and recovery

© The Institution of Engineering and Technology
Received 15/05/2020, Accepted 03/12/2020, Revised 30/11/2020, Published 18/12/2020

The advancement in sensing technologies and infrastructure allows real-time condition monitoring on wind turbines (WTs), which helps improve the power generation efficiency, lower the maintenance costs of wind farms (WFs). Practically, the real-time measurements could be unavailable at the Supervisory Control and Data Acquisition end due to unintended events such as sensor faults and communication loss, which significantly depreciates the condition monitoring and fault detection performance. Aiming to mitigate the missing data impact on data-driven WF applications, this study develops a robust anomaly detection approach for WT fault detection using a denoising variational autoencoder. In presence of missing measurements, the proposed approach can not only sustain high fault detection performance but also recover the missing data as an auxiliary function. The proposed approach is tested on a realistic offshore WF and compared with other autoencoder variants and traditional anomaly detection methods. The testing results verify the outstanding robustness of the proposed approach against missing data events and demonstrate its great potential in missing data recovery.

Inspec keywords: condition monitoring; wind power plants; wind turbines; fault diagnosis; data acquisition; sensors; offshore installations

Other keywords: realistic offshore WF; Supervisory Control; sensing technologies; missing measurements; lower the maintenance costs; data recovery; robust anomaly detection approach; denoising variational autoencoder; Data Acquisition end; wind farms; robust fault detection approach; power generation efficiency; high fault detection performance; outstanding robustness; unintended events; missing data tolerance; communication loss; wind turbines; real-time condition monitoring; traditional anomaly detection methods; WT fault detection; missing data events; sensor faults; missing data impact; real-time measurements

Subjects: Other topics in statistics; Computer vision and image processing techniques; Optical, image and video signal processing; Knowledge engineering techniques; Wind power plants

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