Power distribution network state perception and fault diagnosis method based on embedded edge calculation

By deploying embedded edge computing nodes on distribution network terminal equipment for local data processing and lightweight AI/ML model inference, combined with the edge-cloud collaboration mechanism, the problems of high latency, large bandwidth consumption and high privacy risks in the traditional cloud processing mode are solved, and efficient, real-time fault diagnosis and accuracy of distribution network are achieved.

CN122456751APending Publication Date: 2026-07-24ZHUHAI WANLIDA ELECTRICAL AUTOMATION
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Patent Information

Application Number
CN202610597720.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-30
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Traditional methods for fault diagnosis in distribution networks that rely on central station cloud processing suffer from high data transmission latency, large bandwidth consumption, concentrated computing resources, and high privacy risks, making it difficult to meet the real-time fault response requirements of distribution networks.

Method used

Deploying embedded edge computing nodes on distribution network terminal devices enables local data preprocessing and lightweight AI/ML model inference. Combined with edge-cloud collaboration mechanisms, it achieves selective data uploading and model optimization, reducing latency, saving bandwidth, and improving computing power.

Benefits of technology

It achieves millisecond-level response for power distribution network fault diagnosis, reduces data transmission latency and bandwidth consumption, enhances privacy protection, reduces cloud computing pressure, and improves the accuracy of fault diagnosis and system scalability.

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Abstract

The invention provides a power distribution network state perception and fault diagnosis method based on embedded edge computing, which comprises the following steps: deploying an embedded edge computing node on a power distribution network terminal device, acquiring multivariate operation data of a power distribution network in real time by the edge computing node through a high-precision sensor, and locally preprocessing the acquired multivariate operation data; a lightweight neural network model is deployed on an edge computing node, and real-time sensing and AI / ML fault diagnosis of the operation state of the power distribution network are realized through real-time reasoning; the edge computing node interacts with the central station through an edge cloud collaboration mechanism, and the edge cloud collaboration mechanism comprises a data selective uploading strategy, federal learning type model optimization and central station deep diagnosis supplementation so as to optimize a diagnosis result and model parameters. According to the invention, high-precision real-time sensing of the running state of the power distribution network and rapid and accurate diagnosis of faults can be realized.
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