In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as laser imaging detection and ranging to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine supervisory control and data acquisition data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce the total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.
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Data-driven yaw misalignment correction for utility-scale wind turbines
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November 2021
Research Article|
November 10 2021
Data-driven yaw misalignment correction for utility-scale wind turbines
Linyue Gao (高琳越)
;
Linyue Gao (高琳越)
1
St. Anthony Falls Laboratory, University of Minnesota
, Minneapolis, Minnesota 55414, USA
2
California State University, Sacramento
, Sacramento, California 95819, USA
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Jiarong Hong (洪家荣)
Jiarong Hong (洪家荣)
a)
1
St. Anthony Falls Laboratory, University of Minnesota
, Minneapolis, Minnesota 55414, USA
3
Department of Mechanical Engineering, University of Minnesota
, Minneapolis, Minnesota 55455, USA
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
1
St. Anthony Falls Laboratory, University of Minnesota
, Minneapolis, Minnesota 55414, USA
2
California State University, Sacramento
, Sacramento, California 95819, USA
3
Department of Mechanical Engineering, University of Minnesota
, Minneapolis, Minnesota 55455, USA
a)Author to whom correspondence should be addressed: [email protected]
J. Renewable Sustainable Energy 13, 063302 (2021)
Article history
Received:
May 12 2021
Accepted:
September 18 2021
Citation
Linyue Gao, Jiarong Hong; Data-driven yaw misalignment correction for utility-scale wind turbines. J. Renewable Sustainable Energy 1 November 2021; 13 (6): 063302. https://doi.org/10.1063/5.0056671
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