Addressing the issue of imbalanced data acquisition in circuit breakers,this study adopts the Mahalanobis distance-based modified synthetic minority over-sampling technique (MSMOTE) for data augmentation to achieve efficient fault diagnosis for circuit breakers.Additionally,the firefly algorithm (FA) is utilized to optimize the number of nodes in the hidden layers and learning rate of convolutional neural network-long short-term memory (CNN-LSTM).The data expanded by the MSMOTE algorithm is input into the FA-CNN-LSTM model for training and classification.Experimental results indicate that the proposed method can efficiently diagnose circuit breaker faults even in scenarios with limited fault samples.With the optimization by the FA algorithm,the classification accuracy reaches 99%.Therefore,the circuit breaker fault diagnosis method proposed in this study exhibits excellent performance,offering a novel and effective approach for the analysis of power grid equipment status.