This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power ...
Active Diverse Learning Neural Network Ensemble Approach for Power Transformer Fault Diagnosis. Yu Xu, Dongbo Zhang, Yaonan Wang. Active Diverse Learning ...
This paper proposes a new framework, named BDD, which bridges Duval's method with a deep neural network (DNN) approach for power transformer fault diagnosis.
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A novel object data correction based diversity neural network ensemble method is proposed by analyzing the object data correction principle.
The PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key ...
In this research, a new Improved Snapshot Ensemble Convolutional Neural Network (ISECNN) method with diversity regularization is proposed for fault diagnosis.
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Active Diverse Learning Neural Network Ensemble Approach for Power Transformer Fault Diagnosis · Yu XuDongbo ZhangYaonan Wang. Computer Science, Engineering. J ...
Mar 23, 2021 · In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity.
This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to ...
This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of ...