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Power transformer plays a critical role in grid infrastructure, and itsdiagnosis is paramount for maintaining stable operation. However, the currentmethods for transformer diagnosis focus on discrete dissolved gas analysis,neglecting deep feature extraction of multichannel consecutive data. Theunutilized sequential data contains the significant temporal informationreflecting the transformer condition. In light of this, the structure ofmultichannel consecutive data cross-extraction (MCDC) is proposed in thisarticle in order to comprehensively exploit the intrinsic characteristic andevaluate the states of transformer. Moreover, for the better accommodation inscenario of transformer diagnosis, one dimensional convolution neural networkattention (1DCNN-attention) mechanism is introduced and offers a more efficientsolution given the simplified spatial complexity. Finally, the effectiveness ofMCDC and the superior generalization ability, compared with other algorithms,are validated in experiments conducted on a dataset collected from realoperation cases of power transformer. Additionally, the better stability of1DCNN-attention has also been certified.

Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer