A method for carrying out transformer area user identification based on optimized supervised learning

Abstract

The invention relates to the field of data analysis, and discloses a method for carrying out transformer area user identification based on optimized supervised learning. The method comprises the following steps of determining a user with a known station user topological relation and a station area and a phase to which the user belongs, determining a corresponding tag of user data according to thestation area and the phase to which the user belongs, establishing a training set, a verification set and a test set, determining k parameters in a KNN model by adopting a cross verification mode, andcompleting model training; and identifying and classifying the voltage data to be identified by adopting the trained model and the determined k value, thereby realizing the identification of the users in the transformer area to be identified. According to the invention, conversion from unsupervised learning to supervised learning is realized; a training set, a verification set and a test set arereasonably set, and k parameters are determined by adopting a cross verification mode, so that the transformer area and the phase of a user are accurately and effectively identified, the problem of cross-transformer-area user ownership 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 a low-voltage transformer area.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a method for identifying station area users based on optimized supervised learning. 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 connection relationship with the station area....

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is: in view of the above-mentioned existing problems, considering that the current State Grid has determined the station areas and phases to which some users belong through the traditional station area user identification method, these users are used as training objects to adopt The method of supervised learning classifies the users to be identified, and the present invention provides a method for identifying users in a station area based on optimized supervised learning, which is used to improve the accuracy and efficiency of user identification in a station area, while reducing hardware and labor costs , laying a good foundation for comprehensively guiding the work in various fields such as operation, maintenance, emergency repair, technical transformation, and planning of low-voltage station areas

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