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In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability. [ABSTRACT FROM AUTHOR]

Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made the fault detection, classification, and location estimation process more challenging. Transmission line datasets are stream data which are continuously collected by various sensors and hence, require generalized and fast fault diagnosis approaches. Newly collected datasets including voltages and currents for faulty and non-faulty situations might not have adequate and accurate labels that are useful to train neural networks. In this paper, a novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. This method is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source convolutional neural network to predict a dissimilar target dataset. By transferring this knowledge, faults from various transmission lines, even without sufficient data samples with labels, can be diagnosed faster and more efficiently than the existing methods. To prove the feasibility and effectiveness of this methodology, seven different datasets that include various lengths of transmission lines are used. The robustness of the proposed methodology against the generator voltage fluctuations, variations in fault locations, fault inception angle, fault resistance, and phase difference between the two generators are well studied to prove the reliability of this technique for fault diagnosis of transmission lines. [ABSTRACT FROM AUTHOR]