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2013 (Vol. 5, Issue: 18)
Article Information:

Speed Control of Induction Motor Fed from Wind Turbine via Particle Swarm Optimization Based PI Controller

A.S. Oshaba and E.S. Ali
Corresponding Author:  E.S. Ali 

Key words:  DC Generator, Genetic Algorithm, Induction Motor, Particle Swarm Optimization, Pulse Width Modulation , Speed Control, Wind Turbine
Vol. 5 , (18): 4594-4606
Submitted Accepted Published
January 16, 2013 February 18, 2013 May 05, 2013
Abstract:

Three-phase Induction Motor (IM) is widely used in the industry because of its rugged construction and absence of brushes. However, speed control of IM is required depending on the desired speed and application. This study proposes a design of a Proportional Integral (PI) controller using Particle Swarm Optimization (PSO) algorithm to control the speed of an IM supplied from wind turbine. The wind turbine acts as a prime mover to a connected DC generator. Pulse Width Modulation (PWM) is used to obtain three phase AC voltage from the output of DC generator. The proposed design problem of speed controller is formulated as an optimization problem. PSO is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed technique has been evaluated with respect to the variation of load torque and speed wind turbine. Also the performance of the proposed controller has been evaluated with the performance of the PI controller tuned by Genetic Algorithm (GA) in order to demonstrate the superior efficiency of the proposed PSO in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on PSO in compare to optimized PI controller based on GA over a wide range of load torque and speed wind turbine.
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  Cite this Reference:
A.S. Oshaba and E.S. Ali, 2013. Speed Control of Induction Motor Fed from Wind Turbine via Particle Swarm Optimization Based PI Controller.  Research Journal of Applied Sciences, Engineering and Technology, 5(18): 4594-4606.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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