Training method of transient voltage stability evaluation model considering uncertain source load

By selecting a small number of training samples for preliminary training and adding training samples according to the optimal solution changes of the prevention and control optimization model, the stability evaluation model is gradually improved, and the problem of large number of training samples and long time in the existing technology is solved, and efficient transient voltage stability prevention and control is achieved.

CN119719770AActive Publication Date: 2025-03-28GUANGDONG UNIV OF TECH
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Patent Information

Application Number
CN202411699237.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2025-03-28
Estimated Expiration
2044-11-26

AI Technical Summary

Technical Problem

The prior art requires a large number of training samples when training convolutional neural network models, which takes a long time and occupies a lot of computing resources, making it difficult to effectively deal with the transient voltage stability prevention and control of source load uncertainty in power systems.

Method used

By selecting a small number of training samples with sparse distributions, the initial stable evaluation model is obtained, and then, based on the changes in the optimal solution obtained by the prevention and control optimization model, training samples are added in the sparse area of ​​the training sample and the area near the optimal solution to gradually improve the stable evaluation model.

Benefits of technology

On the premise of ensuring the accuracy of the model, the number of training samples is effectively reduced, the time-consuming model training is reduced, and the efficiency of prevention, control and optimization is improved.

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Abstract

The invention relates to the technical field of power system operation control, and discloses a training method of a transient voltage stability prevention and control evaluation model considering source load uncertainty, and the method comprises the steps: carrying out the preliminary training of a stability evaluation model through selecting a small number of training samples which are sparsely distributed, and obtaining an initial stability evaluation model; then, the initial stability evaluation model is put into prevention and control optimization solution; according to the change of an optimal solution obtained by the prevention and control optimization model, respectively adding training samples in a training sample sparse region and a region near the optimal solution to avoid missing the optimal solution and improve the precision of the optimal solution, and retraining the evaluation model by using the updated training samples; through several rounds of training, the stability evaluation model is gradually improved, and compared with an existing stability evaluation model training method, the method has the advantages that the number of training samples is effectively reduced on the premise of ensuring the precision, the time consumption of model training is reduced, and the prevention and control optimization efficiency is also improved.
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Citation Information

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