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

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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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  -