Method and system for monitoring load state of power distribution area based on stereo portrait analysis

By constructing a three-dimensional image model and multimodal fusion coding, combined with graph neural networks and dynamic partitioning mechanisms, the problem of insufficient accuracy in anomaly identification in distribution transformer area load status monitoring is solved, achieving high-precision and high-sensitivity load status monitoring, and supporting intelligent early warning and decision support.

CN121440937APending Publication Date: 2026-01-30FOSHAN POWER SUPPLY BUREAU GUANGDONG POWER GRID
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Patent Information

Application Number
CN202511690460.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-18
Publication Date
2026-01-30

AI Technical Summary

Technical Problem

In existing technologies, the load status monitoring of distribution substations lacks three-dimensional correlation analysis capabilities, resulting in insufficient accuracy and timeliness in identifying load anomalies, especially in the causal identification between abnormal load fluctuations and the on-site physical environment.

Method used

A three-dimensional image model is constructed using multi-view image data. Coupled feature vectors are generated through multimodal fusion encoding. Combined with graph neural networks and dynamic partitioning mechanisms, load trends are monitored in real time, and anomaly identification and evaluation are performed to generate load anomaly response maps.

Benefits of technology

It improves the positioning accuracy and response speed of load fluctuation areas, enhances the trend perception capability of load state evolution process, realizes multi-source fusion identification of abnormal behavior, improves the accuracy and sensitivity of monitoring, and supports intelligent early warning and decision support.

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Abstract

The invention discloses a power distribution area load state monitoring method and system based on stereo portrait analysis, relates to the technical field of urban power grids, and effectively improves the positioning precision and response speed of a load fluctuation area by introducing a three-dimensional image reconstruction and dynamic partitioning mechanism; a graph neural network and space-time long-short-term memory network modeling method is combined, so that the trend perception capability of the load state evolution process is enhanced; further, multi-source fusion recognition of abnormal behaviors is realized through association matching of the image change track and the load feature sequence, and the monitoring accuracy and sensitivity are effectively improved; based on risk index assessment and a map visualization expression mode, an abnormal area positioning result is visual and transparent, and intelligent early warning and decision assistance are supported; the method has the advantages of high data fusion capability, high risk identification precision, high visual interactivity and the like, and the intelligent and practical level of load state monitoring of the power distribution area is remarkably improved.
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