Title: Parsing and verification method of basic power grid data based on multi data source fusion

Authors: Zhibin Zhou; Zhiguo Zhou; Xiongfeng Ye

Addresses: State Grid Zhejiang Electric Power Company Quzhou Power Supply Company, Quzhou, 324000, China ' State Grid Zhejiang Electric Power Company Quzhou Power Supply Company, Quzhou, 324000, China ' State Grid Zhejiang Electric Power Company Quzhou Power Supply Company, Quzhou, 324000, China

Abstract: In order to solve the problems of low parsing accuracy, low verification accuracy, and long data parsing and verification time in traditional power grid basic data parsing and verification methods, a parsing and verification method of basic power grid data based on multi data source fusion is proposed. Using the D-S evidence theory to fuse multiple data sources in the power grid, smoothing the fusion results and inputting them into an RBF neural network to obtain the parsing results of the power grid basic data. Combining the five verification principle attributes and Bayes' theorem, the power grid basic data verification is implemented. The experimental results show that the average data parsing accuracy of the proposed method is 96.69%, the average validation accuracy is 96.48%, and the time consumption varies between 0.23 s and 0.55 s, which is of great significance for improving data quality and management level.

Keywords: multi data source fusion; basic power grid data; parsing verification; D-S evidence theory; RBF neural network; Bayes' theorem.

DOI: 10.1504/IJBIDM.2025.149068

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.153 - 167

Received: 21 Oct 2024
Accepted: 16 Jan 2025
Published online: 13 Oct 2025 *

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