Wildfire-induced transmission line trip prediction model based on prior knowledge and Siamese network supervision
DOI：10.19783/j.cnki.pspc.250046
Key Words:wildfire  trip  sample generation  imbalanced data  data-driven
Author NameAffiliation
XIE Congzhen School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
ZHOU Xiaojing School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
YU Song School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
MO Ziyang School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
HUANG Mengcheng School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
LAN Ziyi School of Electrical Power, South China University of Technology, Guangzhou 510641, China 
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Abstract:Accurately and effectively predicting wildfire-induced transmission line trip events is crucial for the safe operation of power grids. However, historical tripping data suffer from small sample imbalance, which makes machine learning models prone to misclassifying trip events as normal, thereby reducing prediction accuracy. To mitigate the risk of model collapse caused by informational noise in traditional sample imbalance handling methods, this paper proposes a wildfire-induced transmission line trip prediction model based on prior knowledge and Siamese network supervision. First, based on the original trip dataset, a multivariate probability statistical method is used to determine the number of virtual samples to be generated, thereby alleviating the small sample imbalance issue. Second, a generative oversampling method constrained by prior knowledge is applied to generate virtual positive samples and correct the distribution of positive samples in the dataset. Then, a Siamese network model filters the virtual samples, ensuring that generated positive samples match the characteristics of real data. Finally, a support vector machine (SVM) is employed as a binary classifier to predict line trips under wildfire conditions. Through high-quality and low-demand data generation, the proposed model improves the recall rate by up to 31.94% compared to conventional methods, effectively enhancing the prediction performance of wildfire-induced trip events in practical engineering environments.
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