Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

Zhikun Luo, Zhifeng Sun, Fengli Ma, Yihan Qin and Shihao Ma
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Zhikun Luo: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Zhifeng Sun: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Fengli Ma: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yihan Qin: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Shihao Ma: Wuzhong Baita Wind Power Corporation Limited, Wuzhong 751100, China

Energies, 2020, vol. 13, issue 16, 1-15

Abstract: As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical data exported from the Supervisory Control and Data Acquisition (SCADA) system for output power prediction. Then, we carry out power optimization through pitch angle adjustment based on the obtained prediction model. Our research results indicate that power output could be enhanced by adjusting pitch angle appropriately.

Keywords: power optimization; wind turbines; pitch angle adjustment; stacking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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