Dynamic Intelligent Cleaning for Dirty Electric Load Data Based on Data Mining
Xiaoxing Zhang · Dianli xitong zidonghua · 2005
There is a number of dirty data in the load database produced by SCADA system, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. This paper proposes a dynamic and intelligent model based on data mining theory. Firstly the Kohonen self-organization neural network is meliorated referring to fuzzy soft clustering arithmetic, the meliorated Kohonen network can realize the collateral calculation of Fuzzy c-means soft clustering arithmetic and the approach we proposed can find dynamically the new clustering center, that is the character curve of data, according to the updating of swatch data. Then the RBF neural network is introduced to identify dirty data and compose the intelligent cleaning model. The rapidness and dynamic performance of model make it suits real-time calculation. Test results using actual data of Jiangbei power supply bureau in Chongqing demonstrate the validity and feasibility of the model.