基于数据挖掘的电力系统异常数据辨识与调整  

Power System Detection and Adjustment of Bad Data Based on Data Mining

作　　者：杨国强[1] 

机构地区：[1]淮南电力公司,安徽淮南232007

出　　处：《应用能源技术》2011年第1期25-28,共4页Applied Energy Technology

摘　　要：为了保证电力系统的可靠运行,需要对系统中的异常数据进行检测辨识与调整。在数据挖掘领域,模糊C均值聚类法(FCM)在处理小量低维的数据挖掘时是有效的,但是面向电力系统的数据库的数据挖掘是要处理大量、高维的数据,这样FCM算法在时间性能上难以令人满意。文中基于采样技术对FCM算法进行改进,利用遗传算法对聚类结果进行优化,利用一种新的基于遗传优化的采样模糊C均值聚类算法FFGO(Fuzzy FCM with Genetic Optim ization),实现对异常数据的实时动态处理。To ensure reliable operation of power system, the system would need to detect the bad data identification and adjustment. In data mining field, FCM algorithm is an efficient method in the process of small scale low dimensional database, but the time performance of FCM algorithm can not be satisfied for the large scale high dimensional database. In this paper, a new sampling FCM algorithm with genetic optimization （FFGO） is presented based on the sampling technique and genetic algorithm. The sampling technique and genetic algorithm are used in FFGO algorithm to improve the quality of clustering and realized the bad data real-time dynamic processing.

关 键 词：异常数据 数据挖掘 采样 模糊C均值聚类 遗传算法 

分 类 号：TM712[电气工程—电力系统及自动化]