Method for identifying transformer area user-transformer relation based on electric quantity correlation of user branches

Abstract

The invention belongs to the field of electric power topology big data analysis, and particularly relates to a method for identifying a transformer area user-transformer relation based on electric quantity correlation of user branches. The invention includes the following steps: S1, obtaining transformer area user branch data, power consumption data and adjacent transformer area data; S2, establishing an electric quantity correlation analysis model of the user branches; S3, using the electric quantity correlation analysis model to diagnose the affiliation relationship between the user branches and the transformer area; and S4, outputting a transformer area user-transformer relation result. On the basis of user classification, user branch data and adjacent transformer area data, positive correlation characteristics are provided by utilizing a Pearson's correlation coefficient, and the affiliation relationship between the user branch and the plurality of transformer areas is diagnosed by utilizing the quantitative trend change of positive and negative correlation coefficient values of the user branch and the two or more transformer areas, so that the transformer user relationship identification is realized. Point selection strategies participating in correlation calculation are optimized for users with different electricity utilization characteristics, positive and negative correlation characteristics are enhanced, the recognizable user range is further expanded, and the accuracy of table user relation recognition is improved.

Description

technical field [0001] The invention belongs to the field of power topology big data analysis, and in particular relates to a method for identifying the relationship between households in a station area based on the electric quantity correlation of user branches. Background technique [0002] With the full coverage of the electricity consumption information collection system, the most important operating data can be collected daily, as the most important operating data are the assessment table on the transformer side of the station area and the electricity sales data of the user's electric energy meter. According to the law of energy conservation, there is the following relationship between the supply and sale of electricity in the station area, that is: the power supply of the station area assessment meter = the sum of the electricity sales of each user's sub-meter + the line loss of the station area. [0003] When the user meter does not belong to the station area, how muc...

AI Technical Summary

Problems solved by technology

[0008] Since the substation areas that need to be identified for household substations are generally public substation areas, when using the correlation analysis results of electricity consumption and electricity for household substation identification, since the proportion of electricity consumption of a single user in the substation area is relatively low, its negative correlation characteristics are not Obviously, it cannot be accurately judged that the user does not belong to a certain station area. At the same time, the existing correlation technology can only judge that the user does not belong to the current station area. It is necessary to search all the station areas for correlation analysis, and determine N from the N station areas. - Only one station area is negatively correlated with the user to accurately locate the target station area, which consumes too much computing resources, and the accuracy is extremely low due to the large number of station areas

Images