Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Oshaba, A.; Ali, E.; Abd Elazim, S.
• Is Part Of: Neural computing & applications. Volume 28:Issue 2(2017)
• Page Start: 365
• Page End: 374
• Publication Date: 2017-02
• Subjects: Ant colony optimization -- Genetic algorithm -- High-speed SRM -- Speed control -- PI controller -- Photovoltaic system; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2068-8
• Languages: English
• ISSNs: 0941-0643

Abstract

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.