Optimized speed controller for induction motor drive using quantum lightning search algorithm
Electric drives; Errors; Induction motors; Learning algorithms; Lightning; Mean square error; Optimization; Particle swarm optimization (PSO); Proportional control systems; Speed; Speed control; Backtracking search algorithms; MATLAB/Simulink environment; PI Controller; Proportional integral derivat...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-231892023-05-29T14:38:18Z Optimized speed controller for induction motor drive using quantum lightning search algorithm Ali J.A. Hannan M.A. Mohamed A. 56540826800 7103014445 57195440511 Electric drives; Errors; Induction motors; Learning algorithms; Lightning; Mean square error; Optimization; Particle swarm optimization (PSO); Proportional control systems; Speed; Speed control; Backtracking search algorithms; MATLAB/Simulink environment; PI Controller; Proportional integral derivative controllers; Search Algorithms; Three phase induction motor; Trial-and-error procedures; V/f control; Controllers This paper presents an improve proportional-integral-derivative (PID) controller design technique for controlling a three-phase induction motor (TIM) speed drive using quantum lightning search algorithm (QLSA). This proposed controller avoids the exhaustive conventional trial- and-error procedure for obtaining PID parameters. Objective function using in the proposed controller is mean absolute error (MAE) to enhance the TIM speed performance under sudden change of the speed and load conditions. The QLSA is used to improve two controller system PID and PI controllers in the TIM drive. Moreover, the QLSA algorithm comperes with three optimization algorithms, namely, lightning search algorithm (LSA), the backtracking search algorithm (BSA), the particle swarm optimization (PSO). Designed and validated the simulation model by using a MATLAB/Simulink environment. Results show that the QLSA-based PID and PI speed controller is achieved better results than the other optimization controllers through reduce of damping capability, enhance the transient response, minimize the MAE, root mean square error (RMSE) and standard division (SD) of the speed response. � 2016 IEEE. Final 2023-05-29T06:38:18Z 2023-05-29T06:38:18Z 2017 Conference Paper 10.1109/PECON.2016.7951615 2-s2.0-85024361913 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024361913&doi=10.1109%2fPECON.2016.7951615&partnerID=40&md5=5fadbce3b1d5286cfcaa49fe3b4f6927 https://irepository.uniten.edu.my/handle/123456789/23189 7951615 511 516 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Electric drives; Errors; Induction motors; Learning algorithms; Lightning; Mean square error; Optimization; Particle swarm optimization (PSO); Proportional control systems; Speed; Speed control; Backtracking search algorithms; MATLAB/Simulink environment; PI Controller; Proportional integral derivative controllers; Search Algorithms; Three phase induction motor; Trial-and-error procedures; V/f control; Controllers |
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56540826800 |
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56540826800 Ali J.A. Hannan M.A. Mohamed A. |
| format |
Conference Paper |
| author |
Ali J.A. Hannan M.A. Mohamed A. |
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Ali J.A. Hannan M.A. Mohamed A. Optimized speed controller for induction motor drive using quantum lightning search algorithm |
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Ali J.A. |
| title |
Optimized speed controller for induction motor drive using quantum lightning search algorithm |
| title_short |
Optimized speed controller for induction motor drive using quantum lightning search algorithm |
| title_full |
Optimized speed controller for induction motor drive using quantum lightning search algorithm |
| title_fullStr |
Optimized speed controller for induction motor drive using quantum lightning search algorithm |
| title_full_unstemmed |
Optimized speed controller for induction motor drive using quantum lightning search algorithm |
| title_sort |
optimized speed controller for induction motor drive using quantum lightning search algorithm |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806427565847478272 |
| score |
13.214268 |