基于Vine-Copula贝叶斯网络模型的风机高温降容状态评估方法  

State Assessment Method of Capacity Reduction at High Temperature for Wind Turbine Based on Vine-Copula Bayesian Network Model

作　　者：杨锡运[1] 米尔扎提·买合木提 刘思渠 王其乐 YANG Xiyun;MAIHEMUTI Mierzhati;LIU Siqu;WANG Qile(School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China;Zhong-Neng Power-Tech Development Co.,Ltd,Xicheng District,Beijing 100034,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京市昌平区102206 [2]中能电力科技开发有限公司,北京市西城区100034

出　　处：《中国电机工程学报》2020年第11期3583-3591,共9页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51677067);中央高校基本科研业务费专项资金资助项目(2018MS27)。

摘　　要：风电机组在齿轮箱油温过高时会导致机组限功率运行,影响机组发电效率。传统应对风机高温降容状态多采用阈值判断,反应迟缓,加剧风机齿轮箱劣化趋势。利用贝叶斯网络对风机高温降容状态进行评估,为提取并准确合理地利用机组数据采集与监视控制系统(supervisory control and data acquisition system,SCADA)各个相关状态参数之间的耦合特性,通过vine-Copula模型对机组各个状态参数进行相关性分析,建立更符合机组实际运行状态的贝叶斯概率图形网络,实现对机组高温降容状态的评估。通过交叉熵算法对模型输出结果进行评价,发现与朴素贝叶斯模型相比,vine-Copula贝叶斯网络评估结果更为精确可靠,所建模型更符合机组实际运行工况,能够为现场的运维人员制定准确合理的运行和维护方案提供参考。When the oil temperature of the gearbox is too high,the wind turbine will lead to the limited power operation of the unit,which will affect the generating efficiency of the unit.Threshold judgment is often used to judge the wind turbine's high temperature capacity reduction state,which results in slow response and aggravates the deterioration trend of wind turbine gearbox.In this paper,Bayesian network was used to evaluate the wind turbine's high temperature capacity reduction state.In order to extract and make accurate and reasonable use of the coupling characteristics between the various state parameters of the supervisory control and data acquisition system(SCADA),the state parameters of the unit were introduced by vine-Copula model.Through correlation analysis,Bayesian probabilistic graph network which is more in line with the actual operation state of the unit was established to realize the evaluation of the unit's high temperature capacity reduction state.The cross-entropy algorithm was used to evaluate the output of the model.It is found that compared with the naive Bayesian model,the vine-Copula Bayesian network is more accurate and reliable,and the model is more in line with the actual operating conditions of the unit.It can provide a reference for the operation and maintenance personnel in the field to formulate accurate and reasonable operation and maintenance schemes.

关 键 词：风机高温降容 状态评估 相关性分析 贝叶斯网络 vine-Copula模型 

分 类 号：TM614[电气工程—电力系统及自动化]