Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)

Application of PNN Optimized by MEA in GIS Partial Discharge Recognition

Authors
Ya Li, Haoyang Cui, Gaofang Li, Yongpeng Xu
Corresponding Author
Ya Li
Available Online September 2017.
DOI
10.2991/icmmcce-17.2017.119How to use a DOI?
Keywords
Mind Evolutionary Algorithm; Gas Insulated Switchgear; Probabilistic Neural Network; Discharge recognition
Abstract

Aiming at the problem that the probabilistic neural network (PNN) is difficult to determine the smoothing factors in the process of partial discharge recognition in GIS. A model of GIS partial discharge recognition based on Mind evolutionary algorithm (MEA) is proposed to optimize the PNN. The MEA has the strong ability of searching, obtaining the global approximate optimal solution, finding the optimal smoothing factor of PNN, and improving the accuracy of partial discharge classification. In order to verify the validity and practicability of this model, the simulations are carried out using three typical discharge defect samples. Compared with back propagation (BP) neural network and PNN, the results show that the partial discharge recognition accuracy and stability of PNN optimized by MEA are better and with certain research value.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
Series
Advances in Engineering Research
Publication Date
September 2017
ISBN
10.2991/icmmcce-17.2017.119
ISSN
2352-5401
DOI
10.2991/icmmcce-17.2017.119How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Ya Li
AU  - Haoyang Cui
AU  - Gaofang Li
AU  - Yongpeng Xu
PY  - 2017/09
DA  - 2017/09
TI  - Application of PNN Optimized by MEA in GIS Partial Discharge Recognition
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 652
EP  - 657
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.119
DO  - 10.2991/icmmcce-17.2017.119
ID  - Li2017/09
ER  -