The Research On The Application Of Data Mining Technology In Fine Power Production Management And Control System

With the rapid development of informatization in the power industry, power systemhas accumulated vast amounts of data. The lack of processing and analysis to the data,results in a “data explosion but knowledge poor “phenomenon. According “SG186Project Plan” and “Smart Grid Plan” which have been released by The State Grid, it isimperative to apply data mining techniques to power system data processing, in order toachieve timeliness, standardization and fine control.This paper introduces the data mining technology application in electric powersystem, and the power industry informatization status at home and abroad.Throughstudying the rough set and artificial neural network theory in data mining, andintroducing the main technology and methods to getting and processing data inintegration enterprise-class information platform.Then a data mining algorithm based-onrough set and artificial neural has been studyed, and the validity of the algorithm isverified. Applying the algorithm into processing the original data obtained by theenterprise-class information platform, Fine Power Production Management and ControlSystem is designed which relying on the production management information system.The system attributes reduction and BP neural network training to the raw data acquiredby platform to move redundant data.It uses Matlab tools to do simulation experiments, inwhice the data samples are processed. That varifies the validity of the algorithm’s in datapreprocessing. The Fine Power Production Management and Control System includesindexes system,indexes monitoring,supervision and rectification management, overhaulwork dynamic and special task management five functionai modules.The functionmudules are implemented in this paper.The system can timely and accurately acquire theproduction business functioning information of company systems.Then it can extractkey indicators for performance evaluation commence, promoting fine productionmanagement and scientific and objective management assessment.