@article{4403,
  author = {Xinbai Xue},
  title = {Identification of Partial Discharge Types Based on Multifractal Detrended Fluctuation Analysis},
  journal = {Signals and Telecommunication Journal},
  year = {2025},
  volume = {14},
  number = {1},
  doi = {https://doi.org/10.6025/stj/2025/14/1/9-16},
  url = {https://www.dline.info/stj/fulltext/v14n1/stjv14n1_2.pdf},
  abstract = {This paper proposes a method for identifying partial discharge types based on multi fractaldetrended fluc-
tuation analysis. This method transforms partial discharge signals through multi fractal transformation,

extracts the fractal features of the signals, and combines with detrended fluctuation analysis to accurately
identify partial discharge types. Advanced algorithms and techniques are used in this research to classify and
analyze different types of partial discharges, achieving significant results. Experimental results demonstrate
that this method exhibits high accuracy and reliability in identifying partial discharge types, providing a new
effective means for partial discharge monitoring and fault diagnosis.},
}