Transient evaluation method based on SHAP

By constructing a transient assessment method based on SHAP, embedding a deep neural network with a Shapley module and using three-dimensional tensor input, the problem of insufficient model interpretability in power systems is solved, achieving fast and accurate transient stability assessment and feature attribution, thereby improving the safety and control efficiency of the power grid.

CN121903009APending Publication Date: 2026-04-21CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information

Application Number
CN202512020212.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-30
Publication Date
2026-04-21

AI Technical Summary

Technical Problem

Existing data-driven transient stability assessment models in power systems suffer from insufficient interpretability, making it difficult to balance accuracy and interpretability. Furthermore, their high computational cost and long inference time limit their online application.

Method used

A transient evaluation method based on SHAP is constructed. By embedding a deep neural network with a Shapley module, feature attribution and prediction are executed simultaneously. A deep SHAP network is designed with time-space-feature three-dimensional tensor input. The influence of features on stability is analyzed by combining Shapley values ​​to achieve transparent interpretation.

Benefits of technology

It enables rapid, accurate, and interpretable transient stability assessment of the power grid, reduces inference time, identifies key characteristics and dominant unstable generator groups, and provides guidance for safety control.

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Abstract

The invention discloses a transient evaluation method based on SHAP, and relates to the field of transient stability of a power system, and the method comprises the steps: S1, constructing a prediction-interpretation synchronization framework: embedding a Shapley module supporting parallel calculation into a deep neural network, and achieving the synchronous execution of transient stability evaluation and feature attribution in forward propagation; s2, designing a spatio-temporal feature fusion architecture: adopting a time-space-feature three-dimensional tensor as input, constructing a deep SHAP network by referring to a convolutional neural network thought, and enhancing transient process dynamic feature modeling capability; and S3, establishing a quantitative decision support mechanism: based on the quantitative influence of the Shapley value analysis feature on the stability, realizing transparent explanation of an evaluation result, and providing a decision basis for beforehand prevention and control strategy making and post emergency control deployment. According to the method, the balance between the transparency and the accuracy of transient stability evaluation is realized, and a more accurate, quicker and interpretable evaluation result is provided for power grid dispatching.
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