Deep Q-network for the Condition Monitoring of Power Transformer with Acoustic Signals

The secure and reliable operation of power transformer is of significance to the construction of new power system and the national target of “dual carbon”. Acoustic signals are important for the condition monitoring of transformer. In view of the difficulties to obtain abundant data samples of acoustic signals for the recognition of different failures of transformer, this paper proposes an online condition monitoring method for power transformer by the deep Q-network. With the input of the gammatone frequency cepstral coefficients (GFCCs) and the interaction of Q-learning strategy with the environment, the deep Q-network (DQN) model is built to recognize the operation condition of transformer by properly selecting the hyper-parameters of the framework. The acoustic signals of a 500kV power transformer during the running process are measured and analyzed. It is seen that the designed DQN model converges fast with high learning performance and exploring ability and high recognition rate.