Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift

@article{Miranda2012DiagnosingFI,
  title={Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift},
  author={Vladimiro Miranda and Adalbery R. Castro and Sucupira Lima},
  journal={IEEE Transactions on Power Delivery},
  year={2012},
  volume={27},
  pages={1350-1357}
}
This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. Then, a parallel model is built where the… CONTINUE READING
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