İlhan TARIMER, Murat ARI, Fatih KORKMAZ, Hayati MAMUR, İsmail TOPALOGLU

Reduction of torque ripple in induction motor by artificial neural multinetworks

Direct torque control is used in the high performance control of induction motors. The most frequently faced problem of it is high torque ripples. In this study, a new approach based on artificial neural multinetworks is presented to overcome the problem. Two different artificial neural networks were suggested instead of vector selection and sector determination processes in the conventional direct torque control method. The conventional and the proposed control methods were evaluated on an induction motor through an experimental set. It was observed that the speed and torque responses of the proposed method were better than those of the conventional method. The experimental results also show that the proposed method would be a good alternative to the conventional method in induction motors.

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[1] Bleizgys V, Baskys A, Lipinskis T. Induction motor voltage amplitude control technique based on the motor efficiency observation. Elektron Elektrotech 2011; 3: 89-92.
[2] Lin G, Xu Z. Direct torque control of induction motor using neural network. In: Information Science and Engineering (ICISE); 2628 December 2009; pp. 4827-4830.
[3] Takahashi I, Noguchi T. A new quick-response and high efficiency control strategy of an induction motor. IEEE T Ind Appl 1986; 5: 820-827.
[4] Depenbrock M. Direct self control of inverter-fed induction machines. IEEE T Power Electr 1988; 4: 420-429.
[5] Vas P. Sensorless Vector and Direct Torque Control. New York, NY, USA: Oxford University Press, 1998.
[6] Okumus HI, Aktas M. Adaptive hysteresis band control for constant switching frequency in DTC induction machine drives. Turk J Electr Eng & Comp Sci 2010; 18: 59-69.
[7] Zang C, Cao X. Direct torque control based on space vector modulation with adaptive neural integrator for stator flux estimation in induction motors. In: Fifth International Conference on Natural Computation (ICNC 2009); 1416 August 2009; Tianjian, China. pp. 355-359.
[8] Casadei D, Serra G, Tani A. The use of matrix converters in direct torque control of induction machines. IEEE T Ind Electron 2001; 48: 1057-1064.
[9] Casadei D, Serra G, Tani A. Implementation of a direct torque control algorithm for induction motors based on discrete space vector modulation. IEEE T Power Electr 2000; 15: 769-777.
[10] Benaicha S, Zidani F, Said RN, Said MSN. Direct torque with fuzzy logic torque ripple reduction based stator flux vector control. In: International Conference on Computer and Electrical Engineering (ICCEE 09); 2830 December 2009; Dubai, UAE. pp. 128-133.
[11] Sadati N, Kaboli S, Adeli H, Hajipour E, Ferdowsi M. Online optimal neuro-fuzzy flux controller for DTC based induction motor drives. In: Applied Power Electronics Conference and Exposition; 1519 February 2009; TwentyFourth Annual IEEE. pp. 210-215.
[12] Tan Z, Li Y, Zeng Y. A three-level speed sensorless DTC drive of induction motor based on a full-order flux observer. In: International Conference on Power Systems Technology: 1317 October 2002; Kunming, China: pp. 1054-1058.
[13] Ya G, Weiguo L. A new method research of fuzzy DTC based on full-order state observer for stator flux linkage. In: IEEE 2011 International Conference on Computer Science and Automation Engineering (CSAE); 1012 June 2011. pp. 104-108.
[14] Aktas M, Okumus HI. Stator resistance estimation using ANN in DTC IM drives. Turk J Electr Eng & Comp Sci 2010; 18: 197-210.
[15] Kumar R, Gupta RA, Bhangale SV, Gothwal H. Artificial neural network based direct torque control of induction motor drives. International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007); 2022 December 2007; IETUK. pp. 361-367.
[16] Balli S, Tarımer I. An application of artificial neural networks for prediction and comparison with statistical methods. Elektron Elektrotech 2013; 2: 101-105.
[17] Neema DD, Patel RN, Thoke AS. Rotor flux and torque estimator for vector controlled induction drive using ANN. International Joint Conference on Neural Networks (IJCNN 2009); 1419 June 2009; Atlanta,Georgia, USA. pp. 2215-2220.
[18] Korkmaz F, C¸ akır MF, Topalo˘glu I, G¨urb¨uz R. Artificial neural network based DTC driver for PMSM. International Journal of Instrumentation and Control Systems 2013; 3: 1-7.
[19] Abbou A, Nasser T, Mahmoudi H, Akherraz M, Essadki A. Induction motor controls and implementation using DSPACE. WSEAS Transactions on Systems and Control 2012; 1: 26-35.