Application of PNN Optimized by MEA in GIS Partial Discharge Recognition
- 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/).
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 -