Distribution network loss reduction method based on marketing and distribution big data fusion and terminal

Abstract

The invention provides a distribution network loss reduction method based on marketing and distribution big data fusion and a terminal, and the method comprises the steps of: acquiring distribution network operation data, and building a primary key relation between the data; constructing topology logic structure data of the distribution network; fusing the distribution network operation data and the topological logic structure data; respectively calculating user fine-grained loss and distribution network line fine-grained loss; optimizing the reactive power of the power grid; and after the topological logic data of the distribution network structure is established, according to the multi-branch tree topology expression model of the distribution network line, realizing the visualization of the data of the distribution network structure according to the line hierarchy and the topological relationship between branch lines, wires, towers and distribution transformer users. According to the method, on the basis of fusion of distribution network tower topology logic data and distribution transformer operation data, a current and reactive superposition algorithm is innovated, and distribution network line loss fine-grained management and control and distribution network reactive power reduction optimization scheme recommendation are realized; and a dynamic monitoring and early warning function of the operation condition of the distribution network is realized by using a structure visualization technology.

Description

technical field [0001] The invention relates to the technical field of electric power systems, in particular to a distribution network loss reduction method and a terminal based on the integration of distribution big data. Background technique [0002] At present, electric power workers have done a lot of research on how to reduce line loss, including some innovations in management or technology, but there is no loss reduction plan that is widely popularized and applied. [0003] In the existing technology, the value mining depth of the basic distribution network data is not enough. Due to the vacancy of the topology modeling technology of the distribution network line section, there is no effective index between several data systems, and the accurate data fusion cannot be realized, resulting in the calculation of the distribution network segment loss and the loss of nodes. Reactive power optimization cannot be carried out, and fine-grained calculation of distribution networ...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the value mining depth of the basic distribution network data is not enough. Due to the vacancy of the topology modeling technology of the distribution network line section, there is no effective index between several data systems, and the accurate data fusion cannot be realized, resulting in the calculation of the distribution network segment loss and the loss of nodes. Reactive power optimization cannot be performed, and fine-grained calculation of distribution network structure data cannot be realized

[0004] The existing distribution network reactive power compensation calculation is biased towards centralized compensation. Because the data is incomplete and lacks the function of segmented superposition calculation, although the speed is fast but the granularity is coarse, the cause of line loss cannot be accurately located to specific nodes or line segments, and the loss reduction work consumes a lot of manpower. material and poor

[0005] There is a lack of fine-grained monitoring tools for the operation status of the distribution network line section, which fails to provide effective and timely feedback on the distribution network line section and customer reactive power compensation undercompensation capacity, and cannot provide users with specific loss reduction and power saving decision-making services

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