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Energy Optimisation of Sensorless Induction Motor Drives Using a Novel Robust-Adaptive Flux Simulator

Ashari, Djoni, Pislaru, Crinela and Ball, Andrew (2012) Energy Optimisation of Sensorless Induction Motor Drives Using a Novel Robust-Adaptive Flux Simulator. In: The 2nd International Electric Drives Production Conference, 15th - 18th October 2012, Nuremberg, Germany. (Unpublished)

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Modern drive systems should have improved reliability and one solution is the reduction or elimination of the number of speed sensors while maximizing the efficiency of motor and drive systems. The paper presents the development of a novel robust-adaptive-flux simulator which is used for the energy optimisation of sensorless induction motor drives. The closed loop system contains a predictive current controller and an observer which is robust against parameters variation. The estimated values of the rotor magnetic flux are used to determine the motor core losses by the robust-adaptive observer. Particle Swarm Optimisation (PSO) algorithm is used for the optimization of rotor speed so the motor losses are minimized and so the motor efficiency is increased. The simulated results show that the proposed sensorless control strategy ensures that the drive system has high dynamic performance for a wide range of rotor speeds and leads to a significant energy saving under different load operating conditions.

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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: induction motors; sensorless control; energy efficient; robust adaptive observer; field-oriented control; copper and iron losses.
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Schools: School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
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References: [1] J. Holtz, ―Sensorless Control of Induction Motor Drives, Proceedings of The IEEE, Vol. 90, No. 8, August 2002. [2] Razali, A. N. Abdalla, R. G.honi and C. Venkataseshaiah, Improving squirrel cage induction motor efficiency: Technical review, International Journal of Physical Sciences Vol. 7(8), pp. 1129 - 1140, 16 February, 2012. [3] J. Kennedy, R. Eberhart, ―Particle Swarm optimization, In: Proceeding of IEEE International Conference Neural Network, vol. 4 1995, p 1942- 7. [4] R. Doncker, D.W.J. Pulle, A. Veltman, Advanced Electrical Drives, Analysis, Modelling, Control, Springer-New York, 2011 pp 313. [5] J. Guzinski, H. Abu-Rub, ―Predictive current control implementation in the sensorless induction motor drive, Industrial Electronics (ISIE), 2011 IEEE International Symposium on , 2011, pp.691-696. [6] Texas Instrument, ―Field Orientated Control of 3-Phase AC-Motors, Texas Instruments Europe, February 1998. [7] Barr, Michael, ―Pulse Width Modulation, Embedded Systems Programming, September 2001, pp 103-104. [8] H. A. Mantooth, ―Electrothermal Simulation of an IGBT PWM Inverter, IEEE Transaction on Power Electronic, Vol 12 Issue 3, pp 474-484, May 1997 [9] A.M.A. Amin, M.I.El-Korfally, A.A. Sayed, O. T. M. Hegazy, ―Efficiency Optimization of Two-Asymmetrical Winding Induction Motor Based on Swarm Intelligence, IEE Transactions Energy Conversion, Vol 24 no 1 March 2009 [10] K. O. Jones, ―Comparison of Genetic Algorithm and and Particle Swarm Optimization, International Conference on Computer Systems and Technologies - CompSysTech’2005 [11] R.H.A. Hamid, A.M.A. Amin, R.S. Ahmed, A.A.A. EI-Gammal, ―New Technique for maximum efficiency of induction motor based on particle swarm optimisation (PSO), IEEE ISIE 2006, July 9-12, 2006,Montreal, Quebec, Canada.
Depositing User: Crinela Pislaru
Date Deposited: 29 Jul 2013 11:13
Last Modified: 28 Aug 2021 19:49


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