Domain-Adaptive Transformer Partial Discharge Recognition Method Combining AlexNet-KAN with DANN

Abstract

The changes in operating conditions of a power transformer can cause a shift in the distribution of partial discharge data, leading to the gradual generation of unlabeled new data, which results in the degradation of the original partial discharge detection model and a decline in its classification performance. To address the aforementioned challenge, a domain-adaptive transformer partial discharge recognition method combining AlexNet-KAN with DANN is proposed. First, the Kolmogorov–Arnold Network (KAN) is introduced to improve the AlexNet model, resulting in the AlexNet-KAN model, which improves the accuracy of transformer partial discharge recognition. Second, the domain adversarial mechanism from domain adaptation theory is applied to the domain of transformer partial discharge recognition, leading to the development of a domain-adaptive transformer partial discharge recognition model that combines AlexNet-KAN with Domain Adversarial Neural Networks (DANNs). Experimental outcomes show that the proposed model effectively adapts transformer partial discharge data from the source domain to the target domain, addressing the issue of distribution shift in transformer partial discharge data with either no labels or very few labels in the new data.

Keywords

• transformer partial discharge
• deep learning
• Kolmogorov-Arnold networks
• domain adaptation
• domain adversarial neural networks