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Research on distribution network fault identification based on active transfer learning and autoencoder

Youzhuo Zheng et al.

International Journal of Environment and Pollution2026https://doi.org/10.1504/ijep.2026.151766article
ABDC C
Weight
0.50

What the paper says

The stable operation of distribution networks is crucial for economic stability and daily life, yet frequent faults arise due to long transmission lines and wide coverage.Conventional fault diagnosis based solely on voltage and current data is limited by load fluctuations and external interference.To address this, this paper introduces time-frequency entropy (TFE) features into an autoencoder model to characterise signal dynamics and energy distribution for improved fault identification.Furthermore, an active transfer learning strategy combined with a self-attention-based autoencoder is developed to enhance cross-domain adaptability between source and target networks.Experiments on the IEEE 33-node distribution network show that the proposed method achieves 99.49% accuracy while significantly reducing training time, demonstrating its effectiveness and practical value for cross-scenario fault diagnosis in distribution networks.

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https://doi.org/https://doi.org/10.1504/ijep.2026.151766

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@article{youzhuo2026,
  title        = {{Research on distribution network fault identification based on active transfer learning and autoencoder}},
  author       = {Youzhuo Zheng et al.},
  journal      = {International Journal of Environment and Pollution},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijep.2026.151766},
}

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Research on distribution network fault identification based on active transfer learning and autoencoder

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.