基于数据挖掘技术的电力网络参数估计方法

错误的网络参数对状态估计的结果会产生较大的影响 ,一般都认为参数值是常量 ,但实际上网络参数随周围环境、天气状况及负荷水平等变化而变化。基于数据挖掘技术的电力网络参数估计方法是利用大量的样本数据估计网络的真实参数值的一种方法。首先 ,利用聚类分析技术对历史数据进行分类 ,分成不同类型的样本数据;其次 ,利用数据处理技术对各个样本数据中的孤立点、空缺值等进行处理;最后 ,利用线性回归技术估计满足一定条件的网络参数。经过计算验证 ,证明基于数据挖掘技术的状态估计方法具有很高可靠性。

Power Grid Parameter Estimation Based on Data Mining Techniques

Inaccurate grid parameters have large affect on grid condition estimation.Normally those parameters are considered as constants.However,they are varying constantly with ambient,weather conditions and load levels.This paper presents a power grid parameter estimation method based on data mining techniques,which uses large amount of sample data to estimate accurate grid parameters.First,historical data are categorized into different sample types using cluster analysis.Then,isolated points and vacant values in the samples are processed using data processing techniques.Finally,grid parameters that satisfy certain conditions are estimated using linear regression method.Calculations show that this method is very effective.