Power distribution network topology identification method and system based on artificial intelligence

By constructing an RF-CNN-Attention hybrid model, which combines data preprocessing and attention mechanisms, the problem of high efficiency and high accuracy in distribution network topology identification under complex power grid environments is solved, and high-precision topology identification is achieved under noise and disaster scenarios.

CN122153623APending Publication Date: 2026-06-05HUAZHONG UNIV OF SCI & TECH +1
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Patent Information

Application Number
CN202610152388.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-03
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve efficient and accurate distribution network topology identification in the face of large-scale distributed power source integration and complex power grid environments, especially lacking multi-mode identification capabilities under noise interference and disaster scenarios.

Method used

An AI-based RF-CNN-Attention hybrid model is adopted. The KNN algorithm is used to fill in missing values, K-means clustering analysis and min-max normalization are used to process the distribution network measurement data, and random forest is used to select features. The CNN-Attention model is used to extract feature maps and assign different weights through the attention mechanism, and finally output the topology classification results.

Benefits of technology

It enhances the feature mining capability and processing efficiency of distribution network topology identification, improves identification accuracy and efficiency in noisy and complex environments, and can complete the topology classification of voltage data within 10 milliseconds.

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Abstract

The invention discloses a power distribution network topology identification method and system based on artificial intelligence, and belongs to the technical field of power system automation. KNN missing value filling, photovoltaic output scene clustering and normalization preprocessing are performed on power distribution network measurement data, a random forest is utilized to screen voltage features so as to reduce input dimensions, an RF-CNN-Attention hybrid model is constructed, CNN extracts local features, an attention mechanism calculates attention weights by generating query, key and value matrixes, different weights are given to a feature map, and the power distribution network measurement data are subjected to KNN missing value filling, photovoltaic output scene clustering and normalization preprocessing. And combining residual connection to enhance feature expression, and finally realizing topological label classification through a full connection layer and the like. According to the invention, through multi-model fusion and refined feature processing, the feature mining capability and processing efficiency of power distribution network topology identification are improved.
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