Wind turbine power curve modeling and monitoring with Gaussian Process and SPRT

Guo, Peng and Infield, David (2018) Wind turbine power curve modeling and monitoring with Gaussian Process and SPRT. IEEE Transactions on Sustainable Energy. ISSN 1949-3029 (

[thumbnail of Guo-Infield-IEEE-TSE-2018-Wind-turbine-power-curve-modeling-and-monitoring]
Text. Filename: Guo_Infield_IEEE_TSE_2018_Wind_turbine_power_curve_modeling_and_monitoring.pdf
Accepted Author Manuscript

Download (810kB)| Preview


The wind turbine power curve is an important indicator of the performance of a wind turbine. Modeling and monitoring the power curve can detect wind turbine operating abnormalities and degradation in a timely manner. This paper firstly points out the drawbacks of the standard binned power curve modeling method of IEC-61400-12-1. Multiple factors that influence the wind energy capture and power output of a wind turbine are analyzed in detail and used as the power curve model inputs. A multivariable power curve model is constructed with a modified Cholesky decomposition Gaussian Process (GP) and validated using wind turbine SCADA data. A Sequential Probability Ratio Test (SPRT) with two groups of hypotheses is introduced to analyze and detect abnormal changes in GP power curve prediction residuals and thus detect abnormal operation. In order to locate failed components when an alarm is identified, longitudinal and transverse data comparisons are proposed to check the operation of specific components. The modeling and monitoring methods proposed in this paper successfully identify faults and locate the faulty component for two wind turbines with anemometer failure and pitch system failure respectively.