Gravitational search optimization approach to improve fuzzy logic speed controller for induction motor drive

• Autorzy: Ali J. A. , Hannan M. A. , Mohamed A.

Identyfikatory

DOI

10.15199/48.2016.08.54

Warianty tytułu

PL

Optymalizacja typu grawitacyjne badanie jako metoda poprawy jakości sterownika fuzzy logic zastosowanego do silnika indukcyjnego

• Języki publikacji: EN

Abstrakty

EN

Fuzzy logic controller (FLC) is very useful for controlling speed and torque variables in the three-phase induction motor (TIM) operation. However, the conventional FLC has the exhaustive traditional trial and error procedure in obtaining membership functions (MFs). This paper presents an adaptive FLC design technique for TIM using a gravitational search algorithm (GSA) optimization technique. This technique provides the numerical values to limit the error and change in error of the MFs based on the evaluation results of the objective function formulated by the GSA. The root mean square error (RMSE) of the speed response is used as a fitness function. An optimal GSA- based FLC (GSAF) fitness function is also employed to tune and minimize the RMSE for improving the performance of the TIM in terms of changes speed and torque. Space vector pulse width modulation (SVPWM) technique is utilized to generate signals via voltage/frequency control strategy for variable frequency inverter. Results obtained from the GSAF are compared with those obtained through particle swarm optimization (PSO) to validate the developed controller. The robustness of the GSAF is better than that of the PSO controller in all tested cases in terms of damping capability and transient response under different load and speed.

PL

W artykule zaprezentowano adaptacyjny sterownik typu fuzzy logic przeznaczony do trójfazowego silnika indukcyjnego wykorzystujący algorytm optymalizacyjny badania grawitacyjnego. Jako funkcję fitness użyto błąd rms odpowiedzi prędkości. Do zasilania silnika wykorzystano metodę modulacji szerokości impulsu.

Słowa kluczowe

EN

fuzzy logic control; gravitational search algorithm; particle swarm optimization (PSO); induction motor

PL

sterowanie fuzzy logic; algorytm grawitacyjnego badania; silnik indukcyjny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2016
• Tom: R. 92, nr 8
• Strony: 200--205
• Opis fizyczny: Bibliogr. 15 poz., rys., wykr.

Twórcy

autor

Ali J. A.

• Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)

autor

Hannan M. A.

• Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)

autor

Mohamed A.

• Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)

Bibliografia

• [1] Ali J. A., Hannan M A, Mohamed A., Rule-Based Fuzzy and V/f control for Induction Motor Speed Responses using SVPWM Switching Technique, Electrical review, R. 91 NR 3/2015.
• [2] Mezouar A., Fellah M. K., Hadjeri S., Adaptive sliding mode observer for induction motor using two-time-scale approach, Electr. Power Syst. Res. 77 (2007) 604-418.
• [3] Ustun S. V., Demirtas M., Modeling and control of V/f controlled induction motor using genetic-ANFIS algorithm, Energy Conv. Manag. 50 (2009) 786-791.
• [4] Behera S., Das S. P., Doradla S. R., Quasi-resonant inverterfed direct torque controlled induction motor drive, Electr. Power Syst. Res. 77 (2007) 946-955.
• [5] Rajkumar M. V., Manoharan P. S., Ravi A., Simulation and an experimental investigation of SVPWM technique on a multilevel voltage source inverter for photovoltaic systems, Int. J. Electr. Power Energy Syst. 52 (2013) 116-131.
• [6] Pan I., Das S., Gupta A., Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay, ISA Trans. 50 (2011) 28-36.
• [7] Lin C., Radial basis function neural network-based adaptive critic control of induction motors, Appl. Soft. Comput. 11 (2011) 3066-3074.
• [8] Nounou H. N., Rehman H., Application of adaptive fuzzy control to ac machines, Appl. Soft. Comput. 7 (2007) 899-907.
• [9] Luo Y., Chen W., Sensorless stator field orientation controlled induction motor drive with a fuzzy speed controller, Comput. Math. Appl. 64 (2012) 1206-1216.
• [10] Luo Y. C., Ke Z. S., Kuo Y. P., Sensorless Rotor-Field Oriented Controlled Induction Motor Drive with Particle Swarm Optimization Algorithm Speed Controller Design Strategy, Math. Probl. Eng. (2014) 1-13..
• [11] Saribulut L., Teke A., Tumay M., Vector-based reference location estimating for space vector modulation, Electr. Power Syst. Res. 86 (2012) 51-60.
• [12] Ezoji H., Sheikholeslami A., Rezanezhad M., Livani H., A new control method for Dynamic Voltage Restorer with asymmetrical inverter legs based on fuzzy logic controller, Simul. Model. Pract. Theory 18 (2010) 806–819.
• [13] Rashedi E., Nezamabadi-pour H., Saryazdi S., GSA: A Gravitational Search Algorithm, Inf. Sci. 179 (2009) 2232- 2248.
• [14] Sarker J., Goswami S. K., Solution of multiple UPFC placement problems using Gravitational Search Algorithm, Int. J. Electr. Power Energy Syst. 55 (2014) 531–541.
• [15] Shuaib Y. M., Kalavathi M. S., Rajan C. C. A., Optimal capacitor placement in radial distribution system using Gravitational Search Algorithm, Int. J. Electr. Power Energy Syst. 64 (2015) 384-397.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.