Tracking inherent saliencies of standard induction machines for zero speed sensorless control using different signal processing methods

For mechanical sensor-less control of inverter fed induction machines, a satisfactory performance at low speed down to zero fundamental frequency can so far only be achieved by using signal injection methods and exploiting non-fundamental wave effects. The voltage pulse excitation technique is applied in this paper. The main problem in signal injection based sensorless control a round zero fundamental frequency is the separation of the different signal components related to the saliencies. The focus of this paper is using fast Fourier transform (FFT) to determine the amplitude and angle of the signal components induced by saturation and trained artificial neural network (ANN) for on-line compensation of these components at all loads. In addition a reduction of the remaining amplitude modulation (AM) is proposed. The output after the disturbance elimination is thus the extracted slotting signal that is used as control signal for calculating the rotor position using phase locked loop (PLL). This position is then used for calculating the rotor flux position in combination with the current model (rotor-equation) for field oriented control (FOC). The performance of the proposed structure is verified on a standard induction machine with un-skewed rotor.

[1]  T. M. Wolbank,et al.  Impact of the point of operation on sensorless control of induction motors based on the INFORM method , 2000, 7th IEEE International Power Electronics Congress. Technical Proceedings. CIEP 2000 (Cat. No.00TH8529).

[2]  Joachim Holtz,et al.  Elimination of saturation effects in sensorless position controlled induction motors , 2002 .

[3]  P. Garcia,et al.  Saliency tracking-based, sensorless control of AC machines using structured neural networks , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[4]  T. Fagernes Nestli,et al.  Evaluation and comparison of predictor models for rotor flux calculation in induction motors , 1994, Proceedings of 1994 Power Electronics Specialist Conference - PESC'94.

[5]  Robert D. Lorenz,et al.  Measuring, modeling and decoupling of saturation-induced saliencies in carrier signal injection-based sensorless AC drives , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[6]  M. Schroedl,et al.  Sensorless control of AC machines at low speed and standstill based on the "INFORM" method , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Robert D. Lorenz,et al.  Using multiple saliencies for the estimation of flux, position, and velocity in AC machines , 1997 .

[9]  Joachim Holtz,et al.  Methods for Speed Sensorless Control of AC Drives , 1996 .

[10]  Th.M. Wolbank,et al.  Combination of signal injection and neural networks for sensorless control of inverter fed induction machines , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[11]  G.M. Asher,et al.  Flux position estimation in cage induction machines using synchronous HF injection and Kalman filtering , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).