A transformer area topological structure verification method based on sparse learning

Abstract

The invention discloses a transformer area topological structure verification method based on sparse learning, and relates to the field of electric power operation and maintenance. At present, in a method for carrying out topological error correction on users in a transformer area by utilizing mass power data, the more the power consumption data of the users at different moments is, the more accurate the estimation result is, but more time is needed for calculation of mass data. According to the technical scheme, on the basis of the power consumption data, collected by the power consumption information system, of the transformer area users, a parameterized model of the power consumption of the transformer area is constructed, a sparse self-adaptive parameter estimation method is provided,model parameters representing the topology of the transformer area users are identified, and the users with wrong statistics of the topological structure of the transformer area are further identifiedthrough threshold detection. The technical scheme has higher precision ratio, recall ratio and higher convergence rate, can carry out calculation on line according to the power consumption data of the users, can capture the change condition of the network topology in real time, and saves a large amount of manual on-site troubleshooting cost.

Description

technical field [0001] The invention relates to the field of electric power operation and maintenance, in particular to a method for verifying the topological structure of a station area based on sparse learning. Background technique [0002] At present, in the operation and maintenance of the low-voltage station area, the topology identification is mainly through the staff to find the line on site: find the transformer according to the live line in the meter box, and determine the corresponding name and number of the station area according to the nameplate of the transformer; if there are many underground cables Or if the wiring of the overhead line is messy and there are many shelters, it is necessary for the on-site personnel to record the structure of the station area by means of closing the gate for observation and manual drawing. Municipal power consumption has a lot of impact, and unified digital storage cannot be realized, so there is no unified interface for further...

AI Technical Summary

Problems solved by technology

[0002] At present, in the operation and maintenance of the low-voltage station area, the topology identification is mainly through the staff to find the line on site: find the transformer according to the live line in the meter box, and determine the corresponding name and number of the station area according to the nameplate of the transformer; if there are many underground cables Or if the wiring of the overhead line is messy and there are many shelters, it is necessary for the on-site personnel to record the structure of the station area by means of closing the gate for observation and manual drawing. Municipal power consumption has caused more impacts, and unified digital storage cannot be realized, so there is no unified interface for the system to further use

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