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High performance of brain emotional intelligent controller for DTC-SVM based sensorless induction motor drive

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Abstract

This paper introduces the application of the induction motor (IM) drive brain emotional intelligent controller (BEIC). Intelligent regulation, modelled on the human brain, is capable of generating impulses and is used as a controller. A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm. This paper proposes that speed estimation using BEIC for direct torque control (DTC) of IM drive. The experimental work is conducted on a hardware-in-loop mechanism using a real-time digital simulator (Op-RTDS-OP5600). The simulation and test results are discussed. The proposed method is compared to the DTC-SVM-based IM drive speed control with the existing controllers.

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References

  1. Takahashi I, Noguchi T (1986) A new quick-response and high- efficiency control strategy of an IM. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.1986.4504799

    Article  Google Scholar 

  2. Buja GS, Kazmierkowski MP (2004) DTC of PWM inverter-fed AC motors. IEEE Trans Ind Appl. https://doi.org/10.1109/TIE.2004.831717

    Article  Google Scholar 

  3. Stando D, Kazmierkowski MP (2013) Novel speed sensorless DTC-SVM scheme for induction motor drives. 2013 International Conference-Workshop Compatibility And Power Electronics, Ljubljana, pp 225–230. https://doi.org/10.1109/CPE.2013.6601159

  4. Belkacem S, Naceri F, Betta A, Laggoune L (2005) Speed Sensorless DTC of IM Based on an Improved Adaptive Flux Observer. IEEE Conference. https://doi.org/10.1109/ICIT.2005.1600816

    Article  Google Scholar 

  5. Khan MR, Iqbal A, Ahmad M (2008) MRAS-Based Sensorless Control of A Vector Controlled Five-Phase IM Drive. Electric Power Systems Research. https://doi.org/10.1016/j.epsr.2007.11.006

    Article  Google Scholar 

  6. Adamidis G, Koustsogiannis Z, Vagdatis P (2011) Investigation of the performance of a variable-speed drive using DTC-SVM. Electr Power Compon Syst. https://doi.org/10.1080/15325008.2011.567214

    Article  Google Scholar 

  7. Alagarsamy R, Sahaaya Arul Mary SA (2020) Intelligent rule-based approach for effective information retrieval and dynamic storage in local repositories. J Supercomput 76:3984–3998. https://doi.org/10.1007/s11227-017-2170-z

    Article  Google Scholar 

  8. Paul Joshua K, Mohanalin J, Jaya Christa ST (2020) Adaptive neuro-fuzzy inference system based under-frequency load shedding for Tamil Nadu. J Supercomput 76:4184–4198. https://doi.org/10.1007/s11227-018-2309-6

    Article  Google Scholar 

  9. Lucena C, Palma L, Cardoso A, Gil P (2012) optimal gains tuning of pi-fuzzy controllers. Conf Control Autom. https://doi.org/10.1109/MED.2012.6265740

    Article  Google Scholar 

  10. Hafeez M, Nasiruddin M, Rahim NA, Wooi Ping H (2014) Self-tuned NFC and adaptive torque hysteresis-based DTC for IM drive. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.2013.2272031

    Article  Google Scholar 

  11. Khan MA, Kim Y, Choo J (2018) Intelligent fault detection using raw vibration signals via dilated convolutional neural networks. J Supercomput. https://doi.org/10.1007/s11227-018-2711-0

    Article  Google Scholar 

  12. Moren J, Balkenius C (2000) A computational model of emotional learning in the amygdala. 6th International conference simulation adaptation behavior, Cambridge, MA, pp. 411–436

  13. Moren J (2002) Emotion and learning: a computational model of the Amygdala. Ph.D. dissertation, Lund Univ., Lund, Sweden

  14. Beheshti Z, Hashim SZM (2010) A review of emotional learning and it's utilization in control engineering. Int J Adv Soft Comput Appl

  15. Daryabeigi E, Markadeh GRA, Lucas C (2010) Emotional controller (BELBIC) for electric drives — A review, IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, pp 2901–2907. https://doi.org/10.1109/IECON.2010.5674934

  16. Lucas C, Shahmirzadi D, Sheikholeslami N (2004) Introducing brain emotional learning based intelligent control. Int J Intell Automat Soft Comput. https://doi.org/10.1080/10798587.2004.10642862

    Article  Google Scholar 

  17. Rahman MA, Milasi RM, Lucas C, Arrabi BN, Radwan TS (2008) Implementation of emotional controller for interior PMSM drive. IEEE Trans Ind Appl. https://doi.org/10.1109/IAS.2006.256774

    Article  Google Scholar 

  18. Sheikholeslami N, Shahmirzadi D, Semsar-Kazerooni E, Lucas C, Yazdanpanah M (2006) Applying brain emotional learning algorithm for multivariable control of HVAC systems. J Intell Fuzzy Syst 17:35–46

    Google Scholar 

  19. Ale Aghaee S, Lucas C, Amiri Zadeh K (2012) Applying brain emotional learning based intelligent controller (belbic) to multiple–area power systems. Asian J Control 14(6):1580–1588. https://doi.org/10.1002/asjc.493

    Article  MathSciNet  MATH  Google Scholar 

  20. Mehrabian A, Lucas C, Roshanian J (2006) Aerospace launch vehicle control: an intelligent adaptive approach. Aerosp Sci Technol 10:149–155. https://doi.org/10.1016/j.ast.2005.11.002

    Article  MATH  Google Scholar 

  21. Lucas C, Milasi R, Araabi B (2006) Intelligent modeling and control of washing machine using locally linear neuro-fuzzy (LLNF) modeling and modified brain emotional learning based intelligent controller (BELBIC). Asian J Control 8:393–400. https://doi.org/10.1111/j.1934-6093.2006.tb00290.x

    Article  MathSciNet  Google Scholar 

  22. Rouhani H, Jalili M, Araabi BN et al (2007) Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger. Expert Syst Appl 32(3):911–918. https://doi.org/10.1016/j.ifacol.2017.08.1912

    Article  Google Scholar 

  23. Qutubuddin MD, Yadaiah N (2017) Modeling and implementation of BEIC for PMSM drive. Eng Appl Artif Intell Syst. https://doi.org/10.1016/j.engappai.2017.02.007

    Article  Google Scholar 

  24. Samsonovich AV (2013) Emotional biologically inspired cognitive architecture. Biol Inspir Cognit Archit. https://doi.org/10.1007/978-3-319-63940-6

    Article  Google Scholar 

  25. Ravji R, Mija SJ (2014) Design of brain emotional learning based intelligent controller (BELBIC) for uncertain systems. IEEE Int Conf (ICACCCT). https://doi.org/10.1109/ICACCCT.2014.7019265

    Article  Google Scholar 

  26. Abourida S, Belanger J (2009) Real-Time platform for the control prototyping and simulation of power electronics and motor drives, Proc. 3rd Int. Conf. Modeling, Simulation and Applied Optimization, pp.1–6

  27. Dufour C, Lapointe V, Bélanger J, Abourida S (2008) Hardware-in-the-loop closed-loop experiments with an FPGA-based permanent magnet synchronous motor drive system and a rapidly prototyped controller. IEEE International Symposium, Cambridge, UK, DOI: https://doi.org/10.1109/ISIE.2008.4677105

  28. Hosseinzadeh Soreshjani M, Arab Markadeh G, Daryabeigi E, Abjadi NR, Kargar A (2015) Application of brain emotional learning-based intelligent controller to power flowcontrol with thyristor-controlled series capacitance. IET Generation, Transmission & Distribution, pp 1964–1976. https://doi.org/10.1049/iet-gtd.2014.0986

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Acknowledgements

We are very grateful to thank Principal JNTUH, Hyderabad and Ministry of Science Technology, GOI, New Delhi for providing facility of sponsored DST Project under CSRI.

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Correspondence to Sridhar Savarapu.

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Appendix

Appendix

Parameters of induction motor

Rating

Parameters

Ps = 120 W,Vs = 36 V,

Rs = 0.896Ω, Lls = 1.94 mH

3ΦC, f = 120 Hz

Rr = 1.82Ω, Llr = 2.45 mH,

Is = 6A, P = 4

Lm = 69.3 mH

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Savarapu, S., Narri, Y. High performance of brain emotional intelligent controller for DTC-SVM based sensorless induction motor drive. J Supercomput 77, 8745–8766 (2021). https://doi.org/10.1007/s11227-020-03556-9

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