International Journal of Computational Intelligence Systems

Volume 7, Issue 4, August 2014, Pages 605 - 614

Fault Section Estimation for Power Systems Based on Adaptive Fuzzy Petri Nets

Authors
Z.Y. He, J.W. Yang, Q.F. Zeng, T.L. Zang
Corresponding Author
Z.Y. He
Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2014.960259How to use a DOI?
Keywords
Fault-section estimation, Power system, fault AFPN
Abstract
Due to the advantages of Fuzzy reasoning Petri-nets(FPN)on uncertain and incomplete information processing. It is a promising technique to solve the complex power system fault-section estimation problem. Therefore, we propose a novel estimation method based on Adaptive Fuzzy Petri Nets (AFPN), in this algorithm, the AFPN is used to build a dynamic fault diagnosis fuzzy reasoning model, where the weights in fuzzy reasoning are decided by the incomplete and uncertain alarm information of protective relays and circuit breakers. The validity and feasibility of this method is illustrated by simulation examples. Results show that the fault section can be diagnosed correctly through fuzzy reasoning models for ten cases, and the AFPN not only takes the descriptive advantages of fuzzy Petri net, but also has learning ability as neural network..
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 4
Pages
605 - 614
Publication Date
2017/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2014.960259How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Z.Y. He
AU  - J.W. Yang
AU  - Q.F. Zeng
AU  - T.L. Zang
PY  - 2017
DA  - 2017/01
TI  - Fault Section Estimation for Power Systems Based on Adaptive Fuzzy Petri Nets
JO  - International Journal of Computational Intelligence Systems
SP  - 605
EP  - 614
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.960259
DO  - https://doi.org/10.1080/18756891.2014.960259
ID  - He2017
ER  -