  Power transformer plays a critical role in grid infrastructure, and its
diagnosis is paramount for maintaining stable operation. However, the current
methods for transformer diagnosis focus on discrete dissolved gas analysis,
neglecting deep feature extraction of multichannel consecutive data. The
unutilized sequential data contains the significant temporal information
reflecting the transformer condition. In light of this, the structure of
multichannel consecutive data cross-extraction (MCDC) is proposed in this
article in order to comprehensively exploit the intrinsic characteristic and
evaluate the states of transformer. Moreover, for the better accommodation in
scenario of transformer diagnosis, one dimensional convolution neural network
attention (1DCNN-attention) mechanism is introduced and offers a more efficient
solution given the simplified spatial complexity. Finally, the effectiveness of
MCDC and the superior generalization ability, compared with other algorithms,
are validated in experiments conducted on a dataset collected from real
operation cases of power transformer. Additionally, the better stability of
1DCNN-attention has also been certified.
