Power system source-load disturbance scene generation method in extreme weather scene

By constructing a generative adversarial network model and combining spatiotemporal correlation learning and multi-head attention mechanism, source-load disturbance scenarios under extreme weather conditions with high realism and diversity are generated, which solves the problem of insufficient scenario generation in existing technologies and improves the risk assessment and disaster prevention and mitigation capabilities of power systems.

CN122045803APending Publication Date: 2026-05-15STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
Filing Date
2025-12-19
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Existing technologies lack methods to accurately capture spatiotemporal correlations and effectively generate highly realistic source-load disturbance scenarios under extreme weather conditions, resulting in insufficient risk assessment of power systems under extreme weather conditions.

Method used

By constructing a generative adversarial network model and combining spatiotemporal correlation learning and multi-head attention mechanisms, the generator can automatically extract and learn the complex spatiotemporal variation patterns and dependencies of extreme weather events, generating highly realistic and diverse source-load disturbance scenarios.

Benefits of technology

It improves the accuracy and realism of scene generation, enhances the ability to generate extremely scarce samples, provides a more comprehensive and reliable risk assessment basis, and improves the disaster prevention and mitigation capabilities of the power system under extreme weather conditions.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122045803A_ABST
    Figure CN122045803A_ABST
Patent Text Reader

Abstract

The invention provides a power system source-load disturbance scene generation method in an extreme weather scene, belongs to the technical field of power systems, and solves the problem that a method capable of accurately capturing space-time correlation and effectively generating a high-authenticity source-load disturbance scene in the extreme weather scene lacks in the prior art. The method comprises the following steps: acquiring historical meteorological data of different regions in a target region and preprocessing the historical meteorological data to obtain standardized meteorological data; according to the standardized meteorological data, constructing a real training sample set in different extreme weather scenes; training a generative adversarial network model of the corresponding extreme weather scene by using the real training sample set in each extreme weather scene; for each extreme weather scene, loading the correspondingly trained generative adversarial network model, and generating a plurality of corresponding extreme weather meteorological data sequences; and constructing a source-load disturbance scene set in a corresponding extreme weather scene by using the generated extreme weather meteorological data sequence.
Need to check novelty before this filing date? Find Prior Art