小故障样本条件下的风电机组分层贝叶斯可靠性模型

doi:10.11930/j.issn.1004-9649.201909005

中国电力 ›› 2019, Vol. 52 ›› Issue (12): 97-104.DOI: 10.11930/j.issn.1004-9649.201909005

小故障样本条件下的风电机组分层贝叶斯可靠性模型

王达梦, 马志勇, 柳亦兵, 滕伟

华北电力大学电站设备状态监测与控制教育部重点实验室, 北京 102206

收稿日期:2019-09-02 修回日期:2019-10-12 发布日期:2019-12-05
通讯作者: 柳亦兵(1961-),男,通信作者,博士,教授,从事电力设备状态监测、动力学分析、故障诊断及寿命预测等研究,E-mail:lyb@ncepu.edu.cn
作者简介:王达梦(1992-),男,博士研究生,从事风电设备的可靠性分析、故障危害性分析、维护策略优化等研究,E-mail:wangdameng@ncepu.edu.cn
基金资助:
国家自然科学基金资助项目（半监督环境下风电机组群的智能化故障诊断与寿命预测，51775186）

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

WANG Dameng, MA Zhiyong, LIU Yibing, TENG Wei

Key Laboratory of Condition Monitoring and Control for Power Plant Euipment, Ministry of Education, North China Electric Power University, Beijing 102206, China

Received:2019-09-02 Revised:2019-10-12 Published:2019-12-05
Supported by:
This work is supported by the National Natural Science Foundation of China (Intelligent Fault Diagnosis and Life Prediction of Wind Turbine Flock in Semi-supervised Environment, No.51775186)

摘要/Abstract

摘要： 建立风电机组部件的可靠性模型，并准确估计模型参数，有利于提高风电机组全寿命周期的健康管理水平。新投运机组的故障样本较少，大样本条件下的传统可靠性建模及其参数估计方法不再适用。将贝叶斯可靠性理论应用于小故障样本条件下的风电机组部件可靠性建模及模型参数估计中。以其他风电场的故障数据为模型参数的先验信息，建立了分层贝叶斯可靠性模型，通过Gibbs算法求解模型并获得模型参数的后验分布，以标准均方根误差及可靠度函数的95%置信区间的平均宽度作为衡量指标，对比了传统可靠性模型、一般贝叶斯可靠性及分层贝叶斯可靠性模型的建模精度。最后以风电机组发电机碳刷为例，验证了小样本故障条件下分层贝叶斯可靠性模型的优越性。

关键词: 风电机组, 可靠性建模, 模型参数估计, 小样本条件, 分层贝叶斯可靠性

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wind turbines is small, traditional reliability modeling and parameter estimation methods with large sample sets are no longer applicable.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

中图分类号:

TM614

王达梦, 马志勇, 柳亦兵, 滕伟. 小故障样本条件下的风电机组分层贝叶斯可靠性模型[J]. 中国电力, 2019, 52(12): 97-104.

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

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小故障样本条件下的风电机组分层贝叶斯可靠性模型

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

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小故障样本条件下的风电机组分层贝叶斯可靠性模型

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

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