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.