Comparative Analysis for Interval Modeling Algorithms of Wind Turbine Power Curve

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Chu, Jingchun^1 ; Yuan, Ling^1 ; Hu, Yang^2 ; Pan, Lei^1

Guodian United Power Technology Company Limited, Beijing

100039, China^1

School of Control and Computer Engineering, North China Electric Power University, Beijing

102206, China^2

关键词: Conditional probabilities;    Gaussian process regression;    Kernel Density Estimation;    Operational performance;    Relevance Vector Machine;    Stochastic uncertainties;    Variable speed-variable pitches;    Wind power predictions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052028/pdf
DOI  :  10.1088/1757-899X/569/5/052028

来源: IOP

【 摘 要 】

Considering the stochastic uncertainty of wind power generation, joint estimation of wind turbine power curve (WTPC) in point and interval forms is carefully studied in this paper. Firstly, inducing factors of uncertain data and outliers types in measured data are analyzed to show necessity of uncertainty modeling of WTPC. Then, theoretical principles of different methods for WTPC modeling such as Gaussian Bin method, kernel density estimation (KDE), conditional kernel density estimation (CKDE), conditional probability via Copula, Gaussian process regression (GPR) and relevance vector machine (RVM), are seriously straightened up. Their theoretical essences are discussed and compared in depth. Subsequently, evaluation indexes for point and interval estimations of WTPC are defined to quantify modeling performance. Finally, using measured data from actual 1.5MW variable-speed variable-pitch (VSVP) wind turbines, simulations are executed to show diversities of these methods and their features are discussed from different views. Meanwhile, their potential application scenarios and usage strategies are carefully analyzed, including wind power prediction and operational performance evaluation of wind turbine in an infinite-time-horizon form or a sliding-window form. It is helpful to enhance development of WTPC modeling techniques.