Abstract：The multi-source monitoring data of switchgear contains rich equipment operating status

information，and analyzing it can achieve switchgear fault diagnosis. A fault diagnosis method for switchgear based

on SMOTE-SSA-CNN was proposed. Firstly，based on monitoring data such as switchgear voltage，current，and

temperature and humidity，the synthetic minority over-sampling technique（SMOTE）algorithm was used to expand the original dataset，solving the problem of severe imbalance between positive and negative samples in the original dataset. Then，the sparrow search algorithm（SSA） was introduced to optimize the hyperparameters of

convolutional neural networks（CNN），such as the size and number of convolutional kernels，the number of fully

connected layer neurons，and the learning rate，in order to improve the accuracy of the model's fault diagnosis

results. Finally，the performance of the established SMOTE-SSA-CNN model was evaluated through example

analysis，verifying the effectiveness of the proposed method for switchgear fault diagnosis. Compared with

traditional fault diagnosis methods，the proposed method has better convergence and higher accuracy.

      Key words：switchgear；multi source monitoring data；synthetic minority over-sampling technique（SMOTE）

algorithm；sparrow search algorithm（SSA）；convolutional neural network（CNN）