The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Motion Estimation and Machine Learning-based Wind Turbine Monitoring System

움직임 추정 및 머신 러닝 기반 풍력 발전기 모니터링 시스템

  • Received : 2017.07.10
  • Accepted : 2017.09.20
  • Published : 2017.10.01
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Abstract

We propose a novel monitoring system for diagnosing crack faults of the wind turbine using image information. The proposed method classifies a normal state and a abnormal state for the blade parts of the wind turbine. Specifically, the images are input to the proposed system in various states of wind turbine rotation. according to the blade condition. Then, the video of rotating blades on the wind turbine is divided into several image frames. Motion vectors are estimated using the previous and current images using the motion estimation, and the change of the motion vectors is analyzed according to the blade state. Finally, we determine the final blade state using the Support Vector Machine (SVM) classifier. In SVM, features are constructed using the area information of the blades and the motion vector values. The experimental results showed that the proposed method had high classification performance and its $F_1$ score was 0.9790.

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References

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