Fault Diagnosis for the Power Transformer Based on Multi-feature Fusion algorithm
Authors
Chenfei Liu, Haoyang Cui, Gaofang Li
Corresponding Author
Chenfei Liu
Available Online September 2017.
- DOI
- 10.2991/icmmcce-17.2017.118How to use a DOI?
- Keywords
- fault diagnosis; SVM; multi-feature; D-S evidence theory; transformer
- Abstract
To address the low accuracy and low stability of a single algorithm for transformer fault diagnosis, this dissertation is based on multi feature fusion diagnosis algorithm by combing support vector machine (SVM) and D-S evidence theory, The way to construct the basic probability assignment(BPA) of evidence has been improved by calculating the correct recognition rate and misdiagnosis probability of the SVM classification results. Simulation results show that this method can obtain more reliable belief function of the evidence, and further improve the accuracy of multi-feature fusion fault diagnosis.
- 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 - Chenfei Liu AU - Haoyang Cui AU - Gaofang Li PY - 2017/09 DA - 2017/09 TI - Fault Diagnosis for the Power Transformer Based on Multi-feature Fusion algorithm BT - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017) PB - Atlantis Press SP - 647 EP - 651 SN - 2352-5401 UR - https://doi.org/10.2991/icmmcce-17.2017.118 DO - 10.2991/icmmcce-17.2017.118 ID - Liu2017/09 ER -