Fault Diagnosis for the Power Transformer Based on Multi-feature Fusion algorithm

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/).

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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  -