Event-driven architecture-oriented spatio-temporal feature extraction method and system for multi-type fault power failure events

By constructing an event-driven architecture, XGBoost, temporal convolutional networks, and graph convolutional neural networks are used to extract the spatiotemporal features of power outage events in the distribution network. This solves the problem of neglecting the spatiotemporal coupling relationship in existing technologies and enables real-time and accurate fault diagnosis and pattern discovery.

CN120995281AActive Publication Date: 2025-11-21INFORMATION & COMM CO OF STATE GRID SHAANXI ELECTRIC POWER CO LTD
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Patent Information

Application Number
CN202511510818.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-22
Publication Date
2025-11-21
Estimated Expiration
2045-10-22

AI Technical Summary

Technical Problem

Existing technologies for analyzing power outage events in distribution networks neglect the dynamic coupling relationship between time and space characteristics, resulting in poor adaptability and difficulty in achieving real-time response and rapid diagnosis.

Method used

We employ an XGBoost model based on probability distribution to clean multi-source data, and combine temporal convolutional networks and graph convolutional neural networks to extract spatiotemporal features. We then construct an event-driven architecture to achieve real-time and accurate analysis of power outage events.

Benefits of technology

It enables real-time response and accurate diagnosis of power outage events caused by power distribution network faults, improves the timeliness and proactivity of fault handling, enhances the accuracy and depth of fault analysis, and possesses self-evolution and continuous learning capabilities.

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Abstract

The invention discloses an event-driven architecture-oriented spatial-temporal feature extraction method and system for multi-type fault power failure events, and belongs to the field of power system fault diagnosis. According to the method, historical fault data, equipment operation data and external environment data are collected through event monitoring triggering and multi-source data fusion; an XGBoost model based on probability distribution is adopted to carry out precise cleaning and complementing on the data, and a comprehensive full-scene fault sample library is constructed; and further, a time sequence convolutional network and a graph convolutional neural network are utilized to deeply mine and fuse spatio-temporal characteristics such as periodicity, high-incidence time periods, geographical distribution rules and spatial relevance of fault events from a sample library. According to the method, spatio-temporal evolution law analysis and key influence factor identification of fault events can be efficiently and intelligently realized, the accuracy and timeliness of power grid fault research and judgment are remarkably improved, a scientific basis is provided for fault positioning and emergency disposal, and construction of a novel power system is assisted.
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