Dear Professor Jan Sykulski,
We are pleased to submit an original research article entitled “Application of deep convolutional
neural network on partial discharge pattern recognition” for consideration as an IET-SMT paper. The
contribution of this research is the application of deep convolutional neural network (DCNN) on 2D
partial discharge data set, which helps us to reveal nonlinear relationship between features and PD
sources. The results of this work present the potential of the convolutional neural network to be used
as a supplementary knowledge-base for PD diagnosis systems.
some of articles published in this journal and address same issue are as follow:
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L. Satish and B. I. Gururaj, “Partial discharge pattern classification using multilayer neural
networks,” IEE Proc. A-Science, Meas. Technol., vol. 140, no. 4, pp. 323–330, 1993.
V. Basharan, W. I. M. Siluvairaj, and M. R. Velayutham, “Recognition of multiple partial
discharge patterns by multi-class support vector machine using fractal image processing
technique,” IET Sci. Meas. Technol., vol. 12, no. 8, pp. 1031–1038, 2018.
Y. Zhu, Y. Jia, and L. Wang, “Partial discharge pattern recognition method based on variable
predictive model-based class discriminate and partial least squares regression,” IET Sci.
Meas. Technol., vol. 10, no. 7, pp. 737–744, 2016.
T. Okamoto and T. Tanaka, “Partial discharge pattern recognition for three kinds of model
electrodes with a neural network,” IEE Proceedings-Science, Meas. Technol., vol. 142, no. 1,
pp. 75–84, 1995.
This manuscript has not been published and is not under consideration for publication elsewhere. We
would be very thankful if you give our manuscript a chance to be reviewed.
Again many thanks for your consideration
Vahid Parvin Darabad, assistant professor, Department of Electrical Engineering, Faculty of
Engineering, Golestan University, Gorgan, Iran.
Farshad Najafi, Msc graduated, Department of Electrical Engineering, Faculty of Engineering,
Golestan University, Gorgan, Iran.
Foad Najafi, PhD student, Department of electrical engineering, University of hawaii, Honlulu, HI,
USA