基于参数关联性的电站参数异常点清洗方法  

Parameter Correlation Based Parameter Abnormal Point Cleaning Method for Power Station

作　　者：徐搏超 XU Bochao(East China Electric Power Test&Research Institute,China Datang Corporation Science and Technology Research Institute Co.,Ltd.,Hefei 230031,China)

机构地区：[1]中国大唐集团科学技术研究院有限公司华东电力试验研究院,安徽省合肥市230031

出　　处：《电力系统自动化》2020年第20期142-147,共6页Automation of Electric Power Systems

摘　　要：针对电站参数虚假数据和异常状态点的区分问题,提出了一种将关联规则、基于密度模式的空间数据聚类(DBSCAN)算法和改进高斯核相关向量机(RVM)相结合的清洗方法。首先,引入关联规则分析参数间的关联性,找出强关联参数组合;然后,利用DBSCAN算法初步检测异常点,给出了结合关联参数的清洗流程,区分了虚假数据和系统异常状态点;最后,使用RVM清洗虚假数据,并通过改进高斯核空间样本点形式降低时间成本。案例结果表明,基于参数关联性的清洗方法能有效提高清洗的准确性和时效性。Aiming at distinguishing false data and abnormal state points in power plant parameters,a cleaning method based on association rule,density-based spatial clustering of applications with noise(DBSCAN)algorithm and improved Gauss kernel relevance vector machine(RVM)is proposed.Firstly,association rules are introduced to analyze the association among parameters and find out the combination of parameters with strong association.Secondly,the DBSCAN algorithm is used to detect the abnormal point preliminarily,and the cleaning procedure combined with the associated parameters is proposed to distinguish the false data and the system abnormal state points.Finally,RVM is used to clean the false data,and the time cost is reduced by improving the Gaussian kernel space sample point form.Test results show that the cleaning method based on parameter correlation can effectively improve the accuracy and timeliness of cleaning.

关 键 词：数据清洗 参数关联性 高斯核空间 异常检测 

分 类 号：TM62[电气工程—电力系统及自动化] TP311.13[自动化与计算机技术—计算机软件与理论]