Modeling and Simulation of IM Drive Performance Using PI, ANN and FLC

Nikhitha, D.; Sekhar, J. N. Chandra

doi:10.1109/icitcs.2013.6717858

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…However, its control presents difficulties because of its high non-linearity [1,2]. Many intelligent approaches are used to speed control of IM such as ANN (Artificial Neural Network) [3,4]. The ANN approach has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm

Ali

2015

Energy

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Section: Introductionmentioning

confidence: 99%

Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm

Ali

2015

Energy

View full text Add to dashboard Cite

“…However, its control presents difficulties because of its high nonlinearity [1,2]. Many intelligent approaches are used to speed control of IM such as artificial neural network (ANN) [3,4]. The ANN approach has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

ICA-based speed control of induction motor fed by wind turbine

Ali

2015

Neural Comput & Applic

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This paper proposes the speed control of induction motor fed by wind turbine using Imperialist Competitive Algorithm (ICA). The wind turbine plays as a prime mover to a connected DC generator. Pulse-width modulation is used to get three-phase AC voltage from the output of DC generator. The proposed design problem of speed controller is established as an optimization problem. ICA is adopted to search for optimal controller parameters by minimizing the time domain objective function. The behavior of the proposed ICA has been estimated with the behavior of the conventional Ziegler-Nichols and genetic algorithm in order to prove the superiority of the proposed ICA in tuning PI controller. Also, the behavior of the proposed controller has been tested over a wide range of operating conditions. Simulation results confirm on the better behavior of the optimized PI controller based on ICA compared with other algorithms. AbbreviationsIM Induction motor DC Direct current AC Alternating current FLC Fuzzy logic controller ANN Artificial neural network SA Simulated annealing GA Genetic algorithm ABC Artificial bee colony ACO Ant colony algorithm PSO Particle swarm optimization BF Bacteria foraging ICA Imperialist Competitive Algorithm ZN Ziegler-Nichols PWM Pulse-width modulation PI Proportional plus integral IAE The integral of absolute value of the error ITAE The integral of the time-multiplied absolute value of the error ISE The integral of square error ITSE The integral of time-multiplied square errorList of symbols R s ; L ls Stator resistance and leakage inductance R 0 r ; L 0 lr Rotor resistance and leakage inductance L m Magnetizing inductance L S ; L 0 r Total stator and rotor inductances V qs ; i qs q axis stator voltage and current V 0 qr ; I 0 qr q axis rotor voltage and current V ds ; i ds d axis stator voltage and current V 0 dr ; I 0 dr d axis rotor voltage and current u qs ; u ds Stator q and d axis fluxes u 0 qr ; u 0 dr Rotor q and d axis fluxes x m Angular velocity of the rotor h m Rotor angular position P Number of pole pairs x r Electrical angular velocity (x m Á P) h r Electrical rotor angular position (h m Á P) T e Electromagnetic torque T L Shaft mechanical torque J c B Combined rotor and load viscous friction coefficient RThe wind turbine rotor radius V xThe wind speed x tThe mechanical angular rotor speed of the wind turbine P t Wind power (hp) ƿAir density (kg/m 3 )The area of turbine blades (m 2 )The armature generator current and terminal voltage i f , V fThe field generator current and voltage R a ; L aThe armature resistance and inductance R f ; L fThe field resistance and inductance R L ; L L The load resistance and inductance R t R a + R L L t L a + L L

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Simulation Analysis and Experimental Evaluation of Improved Field-Oriented Controlled Induction Motors Incorporating Intelligent Controllers

et al. 2022

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This work discusses the simulation and experimental demonstration of a genetic algorithm hybrid fuzzy-fuzzy controller (GA-HFFC) system to achieve speed control of a variable-speed induction motor (IM) drive based on a space vector pulse width modulation (SVPWM) technique by means of an eZdspF28335 digital signal processing (DSP) experiment board. Two features of field-oriented control (FOC) were used to design the GA-HFFC, namely, the current and frequency. To overcome the limitations of the FOC technique, the principles of the GA-HFFC were introduced through the acceleration-deceleration stages to regulate the speed of the rotor with the help of a fuzzy frequency controller, while a fuzzy stator current amplitude controller was involved during the steady-state stage. The results revealed that the proposed control approach could deliver a practical control solution in the presence of diverse operating conditions.

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Modeling and Simulation of IM Drive Performance Using PI, ANN and FLC

Cited by 5 publications

References 7 publications

Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm

Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm

ICA-based speed control of induction motor fed by wind turbine

Simulation Analysis and Experimental Evaluation of Improved Field-Oriented Controlled Induction Motors Incorporating Intelligent Controllers

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