风机异常及缺失数据的填补方法研究  

Research on filling method of abnormal and missing data of wind turbines

作　　者：遇茜 钱政[1] 聂志鹏 Yu Qian;Qian Zheng;Nie Zhipeng(School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China;School of Transportation Science and Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100191 [2]北京航空航天大学交通科学与工程学院,北京100191

出　　处：《电测与仪表》2020年第23期1-8,共8页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61573046);教育部长江学者和创新团队发展计划项目(IRT1203)。

摘　　要：风机异常数据和缺失数据的识别和填补对于风机运行状态的评估和未来风速的预测具有重要意义。文章考虑到SCADA数据中某些风机可能存在异常数据和大量缺失数据的情况,对错误数据进行识别剔除,对缺失数据进行分类,对于个别不连续点缺失的情况进行均值填补;对于连续缺失并有旁侧风机数据参考的情况下,基于同时间段临近风机数据,先建立风向填补模型,绘制连续完整的风向数据,再分风向区间,分别使用SVM方法建立风速填补模型;对于无旁侧风机参考状态下的缺失数据,使用NAR神经网络进行逐点填补。文中采用某风场实测数据进行数据验证,并与其他几种传统神经网络填补方法进行比较,测试结果表明所提出的方法性能优于其他模型。The identification and filling of wind turbine abnormal data and missing data is of great significance for the assessment of the operating status of the wind turbine and the prediction of future wind speed.This paper considers that some wind turbines in SCADA system may have abnormal data and a large amount of missing data.Firstly,the wrong data is identified and excluded,and then,classified the missing data.In the case of missing individual discontinuities,filling of the mean of adjacent data is carried out;In the case of continuous missing and side wind turbine data reference,based on the adjacent wind turbine data in the same time period,the wind direction filling model is firstly established,the continuous and complete wind direction data is drawn,and then,SVM method is adopted to establish the wind speed filling model in each wind direction interval respectively.For the missing data without the side wind turbine reference,the NAR neural network is used for point-by-point wind speed filling.In this paper,the measured data of a certain wind field is used for data verification,and compared with other traditional neural network filling methods.The test results show that the proposed method outperforms other models.

关 键 词：异常数据 缺失数据 数据填补 SVM NAR 

分 类 号：TM614[电气工程—电力系统及自动化]