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A transformer area topological structure verification method based on sparse learning

A verification method and topology technology, applied in the field of station topological structure verification based on sparse learning, can solve the problems of inability to realize digital storage, no interface, low efficiency, etc., and achieve cost saving, high precision rate, high The effect of precision

Active Publication Date: 2019-06-21
STATE GRID ZHEJIANG ELECTRIC POWER CO MARKETING SERVICE CENT +2
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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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  • A transformer area topological structure verification method based on sparse learning
  • A transformer area topological structure verification method based on sparse learning
  • A transformer area topological structure verification method based on sparse learning

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Embodiment example

[0066] In order to verify the performance of the method for verifying the topology structure of the station area based on sparse learning proposed by the present invention, we use the data from January to May 2018 of a station area I in Jiaxing City for testing. There are a total of 61 users in this station area. The experimental results show that the parameter value of the user whose serial number is 36 in this station has abnormal convergence, W(36)=-0.94, which is less than the threshold value is a suspicious user. After manually checking the station area, it is confirmed that the user does not belong to station area I. In order to further illustrate the effectiveness of the sparse learning of the present invention in accelerating parameter convergence, image 3 The comparison of the convergence performance of sparse learning and non-sparse learning (ρ=0) is given. In the case of the same step size, the sparse learning algorithm converges faster, and the estimated value is...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/11G06F17/16
Inventor 严华江姚力倪琳娜周佑杨思洁徐玮韡郑宇峰
Owner STATE GRID ZHEJIANG ELECTRIC POWER CO MARKETING SERVICE CENT
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