Application of improved chicken swarm optimization for MPPT in photovoltaic system

Application of improved chicken swarm optimization for MPPT in photovoltaic system The chicken swarm optimization algorithm is a new biology optimization algorithm, but its high‐dimensional operation usually causes deviation and the iteration time of optimizing is a little long. An improved chicken swarm optimization algorithm is proposed. In the improved algorithm, initial positions are arranged according to chaotic sequence; therefore, the uniformity and ergodicity of population are enhanced. Adaptive inertia weight is introduced to update the rule of hens; thus, the speed of global search and the ability of local search are enhanced. The following coefficient of chicks is changed into random quantity, so the risk of falling into local extremum is avoided. These improvements enhance the search ability in the early stage and the track ability in the late stage of the algorithm. The improved algorithm is applied in the maximum power point tracking control of the photovoltaic system and is compared with other algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optimal Control Applications and Methods Wiley

Application of improved chicken swarm optimization for MPPT in photovoltaic system

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0143-2087
eISSN
1099-1514
D.O.I.
10.1002/oca.2394
Publisher site
See Article on Publisher Site

Abstract

The chicken swarm optimization algorithm is a new biology optimization algorithm, but its high‐dimensional operation usually causes deviation and the iteration time of optimizing is a little long. An improved chicken swarm optimization algorithm is proposed. In the improved algorithm, initial positions are arranged according to chaotic sequence; therefore, the uniformity and ergodicity of population are enhanced. Adaptive inertia weight is introduced to update the rule of hens; thus, the speed of global search and the ability of local search are enhanced. The following coefficient of chicks is changed into random quantity, so the risk of falling into local extremum is avoided. These improvements enhance the search ability in the early stage and the track ability in the late stage of the algorithm. The improved algorithm is applied in the maximum power point tracking control of the photovoltaic system and is compared with other algorithms.

Journal

Optimal Control Applications and MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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