Proceedings of the 2017 4th International Conference on Education, Management and Computing Technology (ICEMCT 2017)

Early-warning Model of Support Vector Machine Based on Hybrid Quantum-behaved Particle Swarm Optimization for Power System Operational Status

Authors
Jinchao Li, Fangwei Duan
Corresponding Author
Jinchao Li
Available Online May 2017.
DOI
10.2991/icemct-17.2017.341How to use a DOI?
Keywords
Electric power system, Operation state, Evaluation indexes, Support vector machine (SVM), Quantum particle swarm
Abstract

In order to overcome the problem that the support vector machine (SVM) is not timely in the early warning of the electric power system operation status and the prediction precision is not high, the improved quantum particle swarm optimization algorithm is combined with the SVM to establish a model of hybrid quantum particle swarm optimization SVM for the electric power system operation state early warning, and carry on the simulation for evaluation indexes. The simulation results show that the model is feasible and effective.

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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Volume Title
Proceedings of the 2017 4th International Conference on Education, Management and Computing Technology (ICEMCT 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
May 2017
ISBN
978-94-6252-323-4
ISSN
2352-5398
DOI
10.2991/icemct-17.2017.341How to use a DOI?
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  - Jinchao Li
AU  - Fangwei Duan
PY  - 2017/05
DA  - 2017/05
TI  - Early-warning Model of Support Vector Machine Based on Hybrid Quantum-behaved Particle Swarm Optimization for Power System Operational Status
BT  - Proceedings of the 2017 4th International Conference on Education, Management and Computing Technology (ICEMCT 2017)
PB  - Atlantis Press
SP  - 1601
EP  - 1605
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemct-17.2017.341
DO  - 10.2991/icemct-17.2017.341
ID  - Li2017/05
ER  -