A distribution network loss reduction method and terminal based on big data fusion of operation and distribution

Abstract

The invention provides a distribution network loss reduction method and a terminal based on the integration of big data of operation and distribution, which can obtain distribution network operation data, establish a primary key relationship between the data, construct distribution network topology logical structure data, and compare distribution network operation data with topology Integrate the logical structure data; calculate the fine-grained loss of users and the fine-grained loss of the distribution network separately; optimize the reactive power of the power grid; after the topology logic data of the distribution network structure is established, the model is expressed according to the multi-branch tree topology of the distribution network. , and realize the visualization of distribution network structure data according to the line hierarchy and branch lines, wires, towers, and distribution transformer users. The method is based on the integration of distribution network tower topology logic data and distribution transformer operation data, and innovative current and reactive power superposition algorithm to realize fine-grained control of distribution network line loss and recommendation of distribution network reactive power reduction optimization scheme. Dynamic monitoring and early warning function of distribution network operation status.

Description

technical field [0001] The invention relates to the technical field of electric power systems, and in particular, to a distribution network loss reduction method and a terminal based on the fusion of big data of operation and distribution. Background technique [0002] At present, electric power workers have carried out a lot of research on how to reduce line losses, including some innovations in management or technology, but there is no widely used loss reduction scheme. [0003] In the prior art, the value mining depth of the basic data of the distribution network is not enough. Due to the lack of topological structure modeling technology of the distribution network line segment, 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 node loss. 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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