摘要
针对目前风电机组健康状态无法准确评估的问题,提出一种基于多参数融合和组合赋权的风电机组健康状态评估方法.根据故障频次与时长构建风电机组健康状态评估指标体系,通过将灰色关联分析法的参数层指标客观权重与层次分析法的参数层主观权重对应结合,再与上层指标权重综合,归一化得到组合指标权重,应用高斯函数确定指标对各状态等级的隶属度,采用参数-部件-系统逐层对风电机组开展健康状态评估,选取新疆某风电机组SCADA数据进行验证.结果表明:该方法可在故障发生前得出状态劣化的趋势,对机组早期的故障发出报警,从而达到整机状态预警的目的.
In order to solve the problem that it is difficult to accurately evaluate the health status of wind turbine,a wind turbine health assessment method based on multi-parameter fusion and combination weighting is proposed.The health evaluation index system of wind turbine is constructed according to the fault frequency and time.After combining the objective weight of the bottom index of the grey relational analysis method with the subjective weight of the sub-layer of the analytic hierarchy process,and then synthesizing the weight of the upper index,the weight of the combined index is obtained by normalization,and the membership degree of the index to each state level is determined by using the Gaussian function.The parameter-component-system is used to evaluate the health status of wind turbine layer by layer,and the SCADA data of a wind turbine in Xinjiang are selected for verification.The results show that this method can get the trend of state deterioration before the fault occurs,and give an alarm to the early fault of the unit,so as to achieve the purpose of state early warning of the whole machine,and has guiding significance for operation and maintenance.
作者
郭煜涛
谢丽蓉
包洪印
孙代青
GUO Yutao;XIE Lirong;BAO Hongyin;SUN Daiqing(Engineering Research Center for Renewable Energy Power Generation and Grid Technology of Ministry of Education,Xinjiang University,Urumqi Xinjiang 830017,China;CSIC of HaiWei(Xinjiang)New Energy Co.,Ltd.,Urumqi Xinjiang 830002,China)
出处
《新疆大学学报（自然科学版）（中英文）》
CAS
2022年第1期119-128,共10页
Journal of Xinjiang University(Natural Science Edition in Chinese and English)
基金
国家自然科学基金(51667021)
新疆维吾尔自治区区域协同创新专项(2018E02072)
新疆维吾尔自治区高校科研计划自然科学重点项目(XJDU2020I004).
关键词
组合赋权
健康状态评估
灰色关联分析
高斯隶属度函数
风电机组
combination weighting
health status assessment
grey correlation analysis
Gauss membership functions
wind turbine
