Application of Genetic Algorithms in Control of Induction Motor Drives

Thinh Cong Tran

Disertační práce

Anotace:

In this study, mathematical models, as well as induction motor control methods were presented and this thesis specifically emphasized the vector control model for induction motors. A genetic algorithm is also mentioned in detail. The second part contains some applications of genetic algorithms controlling induction motors such as: PID Speed Controller Optimization Using Online Genetic Algorithm for Induction Motor Drive, Rotor Time Constant Estimation of Induction Motor Using online PI-Adaptive and GA-Adaptive Model, Estimation of IM Parameters Using ANN and GA, PI-based Speed Controller for Vector Control Model of the Induction Motor Drive Using GA Tuned Fuzzy algorithm, Parameters Estimation for Sensorless Control of Induction Motor Drive Using Modified GA and CSA Algorithm. Finally, the experimental results consist of the parts: Speed control system with FOC modelling for many different levels using encoder, online GA-PID controller based on genetic algorithm in FOC controller modelling of induction motor, parameters estimation of induction motor using genetic algorithm and ANN with experimental data. The thesis shows that the application of genetic algorithms in induction motor control has many positive effects.

Abstract:

In this study, mathematical models, as well as induction motor control methods were presented and this thesis specifically emphasized the vector control model for induction motors. A genetic algorithm is also mentioned in detail. The second part contains some applications of genetic algorithms controlling induction motors such as: PID Speed Controller Optimization Using Online Genetic Algorithm for Induction Motor Drive, Rotor Time Constant Estimation of Induction Motor Using online PI-Adaptive and GA-Adaptive Model, Estimation of IM Parameters Using ANN and GA, PI-based Speed Controller for Vector Control Model of the Induction Motor Drive Using GA Tuned Fuzzy algorithm, Parameters Estimation for Sensorless Control of Induction Motor Drive Using Modified GA and CSA Algorithm. Finally, the experimental results consist of the parts: Speed control system with FOC modelling for many different levels using encoder, online GA-PID controller based on genetic algorithm in FOC controller modelling of induction motor, parameters estimation of induction motor using genetic algorithm and ANN with experimental data. The thesis shows that the application of genetic algorithms in induction motor control has many positive effects.

Klíčová slova

Induction motor drive, vector control, sensorless control, speed control, adaptive control, genetic algorithm, PID controller, parameters estimation, rotor time constant estimation, fuzzy logic, artificial neural network, Cuckoo search algorithm.

Keywords

Induction motor drive, vector control, sensorless control, speed control, adaptive control, genetic algorithm, PID controller, parameters estimation, rotor time constant estimation, fuzzy logic, artificial neural network, Cuckoo search algorithm.

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 5. 11. 2018

Obhajoba závěrečné práce

• Obhajoba proběhla 4. 12. 2018
• Vedoucí: Pavel Brandštetter
• Oponent: Jiří Lettl, Viktor Valouch, Petr Palacký

VŠB - Technická univerzita Ostrava

Fakulta elektrotechniky a informatiky

Doktorský studijní program / obor:
Elektrotechnika / Elektrické stroje, přístroje a pohony