Abstract Details
34 / 2023-08-29 11:23:54
A Novel Combinatorial Network for Anomaly Monitoring of Wind Turbines
The supervisory control and data acquisition (SCADA); Anomaly detection; Wind turbine; Temporal convolutional network (TCN); Transformer
Final Paper
Effective anomaly monitoring is crucial for ensuring the safe operation of wind turbines, necessitating advanced monitoring and data collection techniques. The supervisory control and data acquisition (SCADA) technology, recording a range of parameters relevant to wind turbine operation, is fundamental in this regard. In this paper, a combinatorial network named temporal convolutional network-transformer (TCN-Transformer) is proposed to extract multidirectional features of SCADA data for wind turbine condition monitoring. Firstly, the data is cleaned and parameters with greater relevance to the prediction target are filtered out as input parameters of the model. Then, TCN model is used to extract the temporal features of the input data, and the Transformer model is used to extract the correlation features between the input parameters. Finally, by analyzing the SCADA data of the actual wind farm, it is proved that this proposed method can be accurately and reliably applied to the anomaly detection of wind turbines.
Important Date
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Conference Date
Nov 02
2023
toNov 04
2023
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Dec 15 2023
Draft paper submission deadline
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Dec 20 2023
Registration deadline
Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
Xidian University
Contact Information
Previous Conferences
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2022-11-30 China Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2021-10-21 China Nanjing
2021 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence -
2020-10-15 China Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
