Bidirectional Recurrent Neural Network based on Multi-Kernel Learning Support Vector Machine for Transformer Fault Diagnosis

Traditional neural network has many weaknesses, such as a lack of mining transformer timing relation, poor generalization of classification, and low classification accuracy of heterogeneous data. Aiming at questions raised, this paper proposes a bidirectional recurrent neural network model based on a multi-kernel learning support vector machine. Through a bidirectional recurrent neural network for feature extraction, the features of the before and after time fusion and obvious data are outputted. The multi-kernel learning support vector machine method was carried out on the characteristics of data classification. The study of multi-kernel support vector machines in the weighted average of the way nuclear fusion improves the accuracy of characteristic data classification. Numerical simulation analysis of the temporal channel length for sequential network diagnostic performance, the effects of multi-kernel learning on the generalization ability of support vector machine, the influence on heterogeneous data processing capabilities, and transformer fault data classification experiment verifies the correctness and effectiveness of the bidirectional recurrent neural network based on multi-kernel learning support vector machine model. The experiment result shows that the diagnosis performance of bidirectional recurrent networks based on a multi-kernel learning support vector machine is better, and the prediction accuracy of the model is improved by more than 1.78% compared with several commonly used neural networks.

@article{Zhao2023,
  title     = {Bidirectional Recurrent Neural Network based on Multi-Kernel Learning Support Vector Machine for Transformer Fault Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {1},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xun Zhao and Shuai Chen and Ke Gao and Lin Luo},
  doi       = {10.14569/IJACSA.2023.0140114},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140114}
}