基于数据挖掘的电力系统异常数据辨识与调整  

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

作　　者：熊燕[1] 李艳红[1] 

机构地区：[1]淮南供电公司,安徽淮南232007

出　　处：《安徽电气工程职业技术学院学报》2010年第3期11-15,共5页Journal of Anhui Electrical Engineering Professional Technique College

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时要处理大量、高维的数据,这样FCM算法在时间性能上难以令人满意。本文基于采样技术对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 Optimization),实现对异常数据的实时动态处理。To ensure reliable operation of power system,the system needs to take the bad data identification and adjustment.In data mining field,FCM algorithm is an efficient method in the process of small scale and low dimensional database,but the time performance of FCM algorithm can not be satisfied for the large scale and high dimensional database such as the database of power system.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 realize 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：TM732[电气工程—电力系统及自动化]