Research Of The Low-Voltage Courts Line Loss Intelligent Diagnosis System Based On Data Mining Techniques

Line loss is an important technical and economic indicator of power supply enterprises.As an important issue related to the economic benefits of power grid enterprises and energy conservation and emission reduction,line loss management is particularly important in the management of low-voltage courts.At present,the line loss management system can only give the details of abnormal line loss courts according to the judgment rules.At the same time,the line loss judgment rules still follow the traditional one-size-fits-all model,which cannot meet the needs of lean management.With the gradual development of data mining technology,data analysis based on data mining is gradually applied to various scenarios.How to use data mining technology to perform intelligent diagnosis of line loss in low-voltage courts is the focus of this paper.For this reason,this paper studies the implementation method.This paper first studies the research status of line loss management in low-voltage courts.In view of the high degree of manual dependence,lack of intelligent auxiliary means,and differences in the current situation of line loss in different regions in the current line loss management process in courts,the management level of courts managers varies.In order to realize the intelligent analysis and diagnosis of abnormal line loss in the courts,it is proposed to fully excavate the power consumption information data and apply it to the line loss management in the courts,so as to realize the intelligent analysis and diagnosis of the abnormal cause of the line loss in the courts.The courts conducts abnormal cause analysis and electricity theft identification analysis.Secondly,carry out research on the analysis of the reasonable range of line loss rate and assessment threshold in low-voltage courts.In view of the fact that the current line loss assessment method is too simple and rough,the threshold value of the courts statistical line loss rate assessment is too broad,and different types of courts are not classified.and other problems,an optimized K-means clustering algorithm was proposed and applied to the line loss classification of low-voltage courts.Based on the neural network model,the theoretical line loss data processing and calculation of the courts were carried out,and K-means clustering was performed.The classification of the courts divided by the algorithm sets a reasonable line loss assessment threshold for the courts and a reasonable interval for the line loss.Finally,in view of the fact that the abnormal line loss rate in most courts is often caused by a combination of factors,and the current abnormal determination and cause analysis of line losses in the courts mainly rely on manual analysis and cannot consider the cause of abnormality throughout the year,a low-voltage courts is proposed.District line loss abnormal cause diagnosis and electricity stealing identification algorithm.This method studies the abnormal fluctuation of line loss in actual operation on site and analyzes the cause of abnormal line loss.Based on this,a diagnosis rule for abnormal line loss in low-voltage courts is formed,and the identification algorithm for electricity stealing is researched based on abnormal courts.Research based on this article,the research results of this paper are intensively designed to form an intelligent diagnosis system for the low-voltage courts,which can accurately locate the cause of the abnormality and completely solve the hidden danger of the abnormality in the courts,thereby assisting the purpose of improving the level of line loss management in the courts.The research results of this paper have a positive role in promoting the lean management of the courts,and have practical significance in saving energy,reducing losses,improving quality and increasing efficiency.