DocumentCode :
1797346
Title :
Modeling of wind turbine power curve based on Gaussian process
Author :
Jin Zhou ; Peng Guo ; Xue-Ru Wang
Author_Institution :
Sch. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
Volume :
1
fYear :
2014
fDate :
13-16 July 2014
Firstpage :
71
Lastpage :
76
Abstract :
For wind farms, the relationship between wind speed and output can be described by power curve of wind turbines, and it is an important embodiment of power performance of wind turbines. Based on the mathematical model of the power curve of wind turbine, monitoring performance of the wind turbine can be designed. Power curve model of wind turbines can be established by using Gaussian process. Within the Bayesian context, the paper aims to train the Gaussian process by using the maximum likelihood optimized approach to find the optimal hyperparameters. The model was validated by the data. Finally, based on the wind turbine power curve mathematical model, the states of the wind turbine can be monitored by using the technology of control charts.
Keywords :
Bayes methods; Gaussian processes; control charts; maximum likelihood estimation; optimisation; wind power plants; wind turbines; Bayesian context; Gaussian process; control charts technology; mathematical model; maximum likelihood optimized approach; wind farms; wind speed; wind turbine monitoring performance; wind turbine power curve mathematical modeling; Abstracts; Context modeling; Covariance matrices; Gaussian processes; Monitoring; Turbines; Condition monitoring; Gaussian process; Modeling; Power curve;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2014 International Conference on
Conference_Location :
Lanzhou
ISSN :
2160-133X
Print_ISBN :
978-1-4799-4216-9
Type :
conf
DOI :
10.1109/ICMLC.2014.7009094
Filename :
7009094
Link To Document :
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