FLC-Based DTC Scheme to Improve the Dynamic Performance of an IM Drive

 

ABSTRACT:

This paper presents a fuzzy logic hysteresis comparator-based direct torque control (DTC) scheme of an induction motor (IM) under varying dynamic conditions. The fuzzy logic controller (FLC) is used to adjust the bandwidth of the torque hysteresis controller in order to reduce the torque and flux ripples and, hence, to improve motor dynamic response. The effects of torque hysteresis bandwidth on the amplitude of torque ripples of an IM are also discussed in this paper. Based on the slopes of motor-estimated torque and stator current, an FLC is designed to select the optimum bandwidth of the torque hysteresis controller. This paper also proposes a simpler algorithm than the conventional trigonometric function-based algorithm to evaluate the sector number (required for DTC scheme) of the stator flux-linkage space vector. The proposed algorithm reduces the computational burden on the microprocessor. In order to test the performance of the proposed FLC-based DTC scheme for IM drive, a complete simulation model is developed using MATLAB/ Simulink. The proposed FLC-based DTC scheme is also implemented in real time using DSP board DS1104 for a prototype 1/3 hp motor. The performance of the proposed drive is tested in both simulation and experiment.

KEYWORDS:

1          Direct torque control (DTC)

2          Field-oriented control(FOC)

3          Fuzzy logic controller (FLC)

4          Induction motor (IM)

5          Torque and flux hysteresis controllers

6          Torque ripples

SOFTWARE: MATLAB/SIMULINK

CONVENTIONAL BLOCK DIAGRAM:

 Fig. 1. Conventional DTC scheme for IM drive.

SIMULATION RESULTS:

Fig. 2. Steady-state speed responses of the IM drive for a step change in load from 0.3 to 0.8 N · m at 120 rad/s. (a) Conventional DTC. (b) FLC-based DTC.

Fig. 3. Developed torque responses of the IM drive for a step change in load from 0.3 to 0.8 N · m at speed of 120 rad/s. (a) Conventional DTC. (b) FLC-based DTC scheme.

Fig. 4. Developed torque of the IM drive at 40% of rated load. The step change in speed from 100 to 150 rad/s is applied at 0.15 s. (a) Conventional DTC. (b) FLC-based DTC scheme.

Fig. 5. Steady-state stator flux-linkage responses of the IM drive, at 40% rated load and speed of 120 rad/s. (a) Conventional DTC. (b) Proposed FLCbased DTC scheme.

Fig. 6. Steady-state stator current response of the IM drive at 40% rated load and speed of 120 rad/s. (a) Conventional DTC. (b) FLC-based DTC scheme.

CONCLUSION:

A novel FLC-based DTC scheme for IM drive has been presented in this paper. The proposed FLC-based IM drive has been successfully implemented in real time using DSP board DS1104 for a laboratory 1/3 hp IM. The FLC is used to adapt the bandwidth of the torque hysteresis controller in order to reduce the torque ripple of the motor. A performance comparison of the proposed FLC-based DTC scheme with a conventional DTC scheme has also been provided both in simulation and experiment. Comparative results show that the torque ripple of the proposed drive has considerably been reduced. The dynamic speed response of the proposed FLC-based DTC scheme has also been found better as compared to the conventional DTC scheme.

REFERENCES:

[1] I. Takahashi and T. Nouguchi, “A new quick response and high efficiency control strategy for an induction motor,” IEEE Trans. Ind. Appl., vol. IA- 22, no. 5, pp. 820–827, Sep. 1986.

[2] L. Tang, L. Zhong, M. F. Rahman, and Y. Hu, “A novel direct torque control for interior permanent-magnet synchronous machine drive with low ripple in torque and flux-a speed-sensorless approach,” IEEE Trans. Ind. Appl., vol. 39, no. 6, pp. 1748–1756, Sep./Oct. 2003.

[3] S. Kouro, R. Bernal, H. Miranda, C. A. Silva, and J. Rodriguez, “Highperformance torque and flux control for multilevel inverter fed induction motors,” IEEE Trans. Power Electron., vol. 22, no. 6, pp. 2116–2123, Nov. 2007.

[4] D. Casadei and T. Angelo, “Implementation of a direct torque control algorithm for induction motors based on discrete space vector modulation,” IEEE Trans. Power Electron., vol. 15, no. 4, pp. 769–777, July 2000.

[5] C.-T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall, 1996.

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