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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2017

23.09.2015 | Original Article

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

verfasst von: A. S. Oshaba, E. S. Ali, S. M. Abd Elazim

Erschienen in: Neural Computing and Applications | Ausgabe 2/2017

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Abstract

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.
Vorheriger Artikel PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load
Nächster Artikel A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale, sparsely connected neural network

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Metadaten
Titel
Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm
verfasst von
A. S. Oshaba
E. S. Ali
S. M. Abd Elazim
Publikationsdatum
23.09.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2068-8

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