Direct torque control of induction motor is one of the most widely used machines in industrial applications due to its high reliability, relatively low cost, and less maintenance requirement. The aim of this thesis is to design and comparative analysis of fuzzy logic proportional integral controller and genetic algorithm and fuzzy adaptive proportional integral controller for direct torque controlled induction motor. The parameters of the scale factor of fuzzy logic controller were genetically optimized using integral Absolute Square of error as fitness function. The proposed of genetic algorithm and fuzzy adaptive proportional integral controller that was the genetic algorithm working to optimize the fuzzy proportional integral controller. In this approach, according to speed error and its first time derivative, the proportional coefficient and integral coefficient can be adjusted by fuzzy logic controller, and then the scale factor of the fuzzy logic adapter parameters are optimized by genetic algorithm to improve the self-adaptation of speed. This increases the performance of induction motor drive. The performance of genetic algorithm and fuzzy adaptive proportional integral system controller was compared with fuzzy logic proportional integral controller using MATLAB simulation results with different operating conditions. It is observed from the simulation results that by using fuzzy logic proportional integral and genetic algorithm and fuzzy adaptive proportional integral controllers, from the reference speed of 50 rad/se, the percentage peak overshoots were 22.84% and 15.698% respectively. Thus, genetic algorithm and fuzzy adaptive proportional integral controller shows less overshoot that fuzzy logic proportional controller. Also the speed reaches its desired set value at 0.648 second in genetic algorithm and fuzzy logic proportional integral controller controlled induction motor drive.
DIRECT TORQUE CONTROL OF INDUCTION MOTOR BASED ON GENETIC ALGORITHM AND FUZZY ADAPTIVE PI CONTROLLER
2019-05-23
doi:10.20372/nadre/3842
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
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