Wind turbine output power prediction by a segmented multivariate polynomial-XGBoost model

ABSTRACT

Accurate prediction of wind turbine output power is important to ensure safe, stable, and efficient wind power generation. In this study, a hybrid prediction model based on a combination of segmented multivariate polynomial and extreme gradient boosting (XGBoost) under different operating conditions is proposed. First, the wind farm supervisory control and data acquisition (SCADA) data are processed using isolated forest for outliers, in order to improve model prediction accuracy. Then, random forest is used for feature selection, where wind speed, rotor speed, pitch angle, wind direction and temperature are found to be the most relevant features related to the output power. Next, based on the analysis of the wind turbine characteristic curves, its operating state is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT). Different polynomials are established for each phase, the CP phase is of order 5, the CS phase is of order 7, and the MPPT phase is of order 4. Finally, the XGBoost model is trained for power prediction with polynomial new features for each phase. The segmented multivariate polynomial-XGBoost model has a mean absolute percentage error (MAPE) of 0.04, a root mean square error (RMSE) of 49, and a coefficient of determination R² of 0.99. Compared to the single XGBoost model and the unsegmented polynomial-XGBoost model, the MAPE decreases by 42% and 33% respectively, while RMSE decreases by 50% and 12% respectively, resulting in high prediction accuracy.

KEYWORDS:

• Wind turbine output power prediction
• XGBoost
• hybrid method
• multivariate polynomial
• feature expansion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization, Jingwei Wang and Wangqiang Niu; methodology, Jingwei Wang; software, Jingwei Wang; validation, Jingwei Wang; resources, Wangqiang Niu and Yanhua Yang; data curation, Jingwei Wang and Wangqiang Niu; writing-original draft preparation, Jingwei Wang; writing-review and editing, Wangqiang Niu and Yanhua Yang; visualization, Jingwei Wang; supervision, Wangqiang Niu.

Data availability statement

The dataset is not publicly available due to its commercial character.

Additional information

Notes on contributors

Jingwei Wang

Jingwei Wang is currently pursuing an M.S. degree with Shanghai Maritime University, Shanghai, China. Her research direction is wind turbine output power prediction. (E-mail: [email protected]).

Wangqiang Niu

Wangqiang Niu has been a Lecturer with Shanghai Maritime University, Shanghai, since 2008, he has been an Associate Professor, since 2017. His research interests include prognostics and health management (PHM) of wind turbines, wireless power transfer, and control of marine equipment. (E-mail: [email protected]).

Yanhua Yang

Yanhua Yang is an engineer of EverOne (Shanghai) Company Limited, Shanghai, China. His interests include data analysis and optimization. (E-mail: [email protected])