Domain-Adaptive Transformer Partial Discharge Recognition Method Combining AlexNet-KAN with DANN
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Summary
This summary is machine-generated.This study introduces a novel domain-adaptive method for power transformer partial discharge recognition. The approach enhances model performance by adapting to new data distributions, improving detection accuracy with unlabeled or minimally labeled data.
Area Of Science
- Electrical Engineering
- Artificial Intelligence
- Machine Learning
Background
- Power transformer operating conditions can alter partial discharge (PD) data distributions, leading to unlabeled new data.
- This data shift degrades existing PD detection models, reducing classification performance.
- Accurate PD recognition is crucial for transformer health monitoring and preventing failures.
Purpose Of The Study
- To develop a domain-adaptive method for recognizing transformer partial discharge data under changing operating conditions.
- To improve the accuracy and robustness of PD detection models when faced with unlabeled or sparsely labeled new data.
- To address the challenge of distribution shift in PD data.
Main Methods
- Proposed a novel AlexNet-KAN model, integrating the Kolmogorov-Arnold Network (KAN) to enhance the AlexNet architecture for improved PD recognition accuracy.
- Applied domain adversarial neural networks (DANNs) from domain adaptation theory to the PD recognition domain.
- Developed a domain-adaptive transformer PD recognition model combining AlexNet-KAN with DANNs.
Main Results
- The proposed AlexNet-KAN model demonstrated improved accuracy in transformer partial discharge recognition.
- The domain-adaptive model effectively adapted PD data from source to target domains.
- Successfully addressed the distribution shift issue in transformer PD data, even with limited or no new labels.
Conclusions
- The proposed domain-adaptive transformer partial discharge recognition method effectively handles distribution shifts in PD data.
- The integration of AlexNet-KAN and DANNs provides a robust solution for recognizing PD with unlabeled or few-labeled new data.
- This approach significantly enhances the reliability of PD detection models in dynamic power transformer environments.
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