Status of the transformer is highly associated with safe and stable operation of the whole power system. Since the vibration signal is generated along with operation of the transformer and can indicate change of equipment operation status in real-time

fault diagnosis based on the vibration signal is a feasible method to monitor status of the transformer. In this paper

an end-to-end multi-branch-attention-multiscale CNN (MAMCNN) framework is proposed based on a one-dimensional convolutional neural network

in which multi-branch inputs

multiscale residual learning

and attention mechanism-guided multi-branch fusion techniques are integrated to identify states of the 220 kV transformer. To address the problem of small samples for transformer fault diagnosis

the proposed method first tests on a public dataset of rolling element bearing vibration. Results show that MAMCNN still has good differentiation for fault features under strong noise and fluctuating operating conditions without denoising

and has high accuracy and stability for state identification. MAMCNN is then applied to 220 kV transformer fault diagnosis based on vibration signals

and results exhibit high accuracy

rapid and stable convergence in identifying four transformer states.