Hierarchical Bayesian Reliability Model for Wind Turbines with Small Fault Sample Sets

doi:10.11930/j.issn.1004-9649.201909005

Abstract

Abstract: It is beneficial to improve the health management level of the full-life-cycle of wind turbines through establishing the reliability model for wind turbine and estimating its parameter accurately. As the fault sample size of new constructed wind turbines is small, traditional reliability modeling and parameter estimation methods with large sample sets are no longer applicable. This paper applies Bayesian reliability theory to reliability modeling and parameter estimation of wind turbine with small fault sample sets. Based on the fault samples from other wind farms as the priori information of the model parameters. A hierarchical Bayesian reliability model is established. Then the proposed model is solved by the Gibbs algorithm and the posterior distribution of the model parameters is obtained. The normalized root mean square error and the mean width of 95% confidence interval of reliability function are chosen as measurement indices. The comparison of the modeling precision among the traditional reliability model, general Bayesian reliability model and hierarchical Bayesian reliability model are performed. Finally, the generator carbon brushes from various wind farms is taken as an sample to demonstrate the superiority of the hierarchical Bayesian reliability model with small fault sample sets.

Key words: wind turbine, reliability modeling, parameter estimation, condition of small sample size, hierarchical Bayesian reliability model

CLC Number:

TM614

WANG Dameng, MA Zhiyong, LIU Yibing, TENG Wei. Hierarchical Bayesian Reliability Model for Wind Turbines with Small Fault Sample Sets[J]. Electric Power, 2019, 52(12): 97-104.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201909005

https://www.electricpower.com.cn/EN/Y2019/V52/I12/97

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