Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

The Fault Diagnosis of Smart Substation Equipment Based on Fuzzy Petri Nets

Authors
Shaowen Wan
Corresponding Author
Shaowen Wan
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.76How to use a DOI?
Keywords
fuzzy Petri nets, fault diagnosis, hierarchical model, smart substation equipment.
Abstract

This paper introduces fault diagnosis method and its mathematical descriptionthe of fuzzy Petri nets, including the basic concept and rule representation. On the basis of the original fuzzy Petri net reasoning decision, the transition threshold judgment is proposed. This paper presents a fault diagnosis method for smart substation equipment based on fuzzy Petri nets, establishes a hierarchical fault diagnosis model, and simplifies the decision algorithm, which is validated by actual data.

Copyright
© 2018, 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 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecae-18.2018.76How to use a DOI?
Copyright
© 2018, 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  - Shaowen Wan
PY  - 2018/03
DA  - 2018/03
TI  - The Fault Diagnosis of Smart Substation Equipment Based on Fuzzy Petri Nets
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.76
DO  - https://doi.org/10.2991/mecae-18.2018.76
ID  - Wan2018/03
ER  -