摘要
针对老化导致的风电机性能退化问题提出了一种基于信息融合的风电机组整体老化评估方法。选取SCADA数据中的输出功率、机舱振动、主轴承温度、发电机后轴承温度、齿轮箱转速和机舱温度作为老化评估标准,通过神经网络求取各评估标准的权重,将评估标准的信息进行融合,建立整体老化评估模型,并验证其可靠性。研究表明,利用信息融合的方法建立风电机组的老化评估模型简单有效,可靠性高,为后续风电机组老化程度的研究提供了新的思路。
An overall aging evaluation method for wind turbines based on information fusion is proposed for the degradation of wind turbine performance caused by aging.The output power,cabin vibration,main bearing temperature,generator rear bearing temperature,gearbox speed and cabin temperature are selected in SCADA data as the aging evaluation criteria.The weights of each evaluation criterion are obtained through the neural network.The information of evaluation criteria is fused to establish an overall aging evaluation model and verify its reliability.The study shows information fusion is an effective and simple way to establish the aging evaluation model of wind turbines in a high reliability.At the same time it provides a new idea for the study of the aging degree of wind turbines.
作者
苏连成
邢美玲
SU Liancheng;XING Meiling(School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)
出处
《燕山大学学报》
CAS
北大核心
2020年第4期379-387,共9页
Journal of Yanshan University
基金
河北省自然科学基金资助项目(F2015203412)。
