数据挖掘在电力负荷坏数据智能辨识与修正中的应用

Application of data mining method in power load bad data intelligent identification and correction

负荷历史数据由于各种原因含有一定的坏数据,在进行高精度的电力负荷预测或系统分析前必须对历史数据进行预处理。本文采用基于加权核函数的模糊C均值聚类的改进算法—WKFCM,以核诱导距离的简单两项和替代欧氏距离作为聚类目标公式的不相似性测度函数,减小了计算复杂度。对数据进行聚类之后,采用收敛速度快、模式分类能力强的超圆神经元网络数据辨识模型,并对识别出的坏数据进行修正,实例证明本文提出的数据处理模型具有较好的效果。

There is a number of bad data in the load database produced, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle covering neural network based identification model for load data is proposed, and the bad data is modified. It is proved that the proposed data processing model has good effect.