Transformer area user identification and discrimination method based on supervised learning

Abstract

The invention relates to the field of data analysis, in particular to a transformer area user identification and discrimination method based on a supervised learning method. The method comprises the steps of determining a corresponding tag of user data according to a transformer area and a phase to which a user belongs, establishing a training set, a verification set and a test set, and determining in a cross verification mode; Identifying the voltage data of the to-be-identified user by adopting the trained training model; And establishing a quantitative evaluation index of the reliability ofthe transformer area user identification result, and calculating the reliability of the primary identification result of the transformer area user. According to the invention, conversion from unsupervised learning to supervised learning is realized, the hardware and labor cost is reduced, and the identification result is more reliable; Meanwhile, a quantitative evaluation index of a transformer area user household change relation identification result is established, accurate identification of the dispute user is achieved, the transformer area to which the user belongs and the phase are accurately and effectively identified, the cross-transformer-area user ownership problem is thoroughly solved, and a foundation is laid for comprehensively guiding work in the fields of operation, maintenance, first-aid repair, technical improvement, planning and the like of the low-voltage transformer area.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a method for identifying and discriminating station area users based on an optimization-based supervised learning method. Background technique [0002] Accurate basic station area files are an important basis for a series of advanced applications such as line loss rate analysis, distribution network fault location, emergency repair work order issuance, and three-phase unbalance analysis. However, due to the late start of my country's power system and the imperfect initial development plan, the distribution of distribution transformers in my country at this stage is scattered and the distribution lines are intricate. At the same time, due to the loss of information records, untimely updates, and incomplete information of the power grid company during the years of operation, the archives of the station area are often inaccurate, that is, a small number of end users have a real connecti...

AI Technical Summary

Problems solved by technology

When the data quality is low, the accuracy of identification is low and the identification result is unreliable

[0007] 3. Although some of the existing technologies can identify the relationship between users more accurately, there are still two major problems: first, there is no quantitative evaluation index for the identification results, so it is not known which users' identification results are reliable and which ones are reliable. The identification results of the users are controversial; secondly, for the "disputed users" whose identification results are not reliable enough, no further strategy is adopted to distinguish the station area and phase they belong to

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