Research Of Data Analysis And Mining Technology Based On Power Quality Information From Multiple Sources

With the deeping research of electric power quality and electric power quality m onitoring network continues to expand,the value of power monitoring information is already more than in the field of electric power quality. At the same time,electric pow er monitoring system each has advantages of data feature.Therefore,the integration of various monitoring system data can be interconnected,so we can consult multi-sourc e data analysis and mining.And the analytical tools and methods become more richer.Power quality problems mainly for issues both steady-state and transient.In this paper,we start from different sources of data and settled in the power system fault diagnosis.We conducted in-depth analysis from the application offline and the online fault diagnosis and combine the data mining techniques and informati-on fusion technology.So can achieve the demands to solve practical problems.In the offline analysis applications,To protect the data for example,use the Associat ion Rules method to discover the relationships between the failure conditions and the protect incorrect operation,and dig out the incorrectprotection operation mode.This mining results can help improve research and design protection systems for power co mpanies and the protection of manufacturers. In real-time fault diagnosis aspects,beca use there is a lot of information when a fault occurs,the effective way to diagnose it is very important.Due to the accuracy and integrity of fault wave data,this paper pres ents a model based on fuzzy Petri net.By this model can determine the fault results.Jo in switch information on the basis of diagnosis,for more information on the different sources of data characteristics.Distributed Information Fusion Model,achieving the Multi-Source information fusion in the decision-making level,and numerical example s verified the accuracy of the model.