This invention discloses a method and
system for high-frequency
transformer fault identification, belonging to the field of high-frequency
transformer fault detection technology. The method comprises: acquiring a characteristic gas vector sequence of the high-frequency
transformer and converting the characteristic gas vector sequence into an actual fault recurrence graph; generating an expanded dataset based on the actual fault recurrence graph, a pre-acquired
random noise vector, and a preset initial depth convolutional
generative adversarial network model; training a preset initial MobileNetV3 model based on the expanded dataset and the characteristic gas vector sequence and constructing a cross-entropy
loss function, then updating the model with the goal of minimizing the cross-entropy
loss function until a preset convergence condition is met, thus obtaining the MobileNetV3 model; inputting the fault recurrence graph to be tested into the MobileNetV3 model to obtain the fault detection result of the high-frequency transformer to be detected. Therefore, by implementing this invention, the low accuracy of high-frequency transformer fault identification and the insufficient ability to identify complex faults can be avoided, achieving high-precision and high-reliability identification of various types of high-frequency transformer faults.