Fuzzy Efficiency Enhancement of Induction Motor Drive

 

ABSTRACT:

Efficiency improvement of motor drives is important not only from the viewpoints of energy loss and hence cost saving, but also from the perspective of environmental pollution. Several efficiency optimization methods for induction motor (IM) drives have been introduced nowadays by researchers. Distinctively, artificial intelligence (AI)-based techniques, in particular Fuzzy Logic (FL) one, have been emerged as a powerful complement to conventional methods. Design objectives that are mathematically hard to express can be incorporated into a Fuzzy Logic Controller (FLC) using simple linguistic terms. The merit of FLC relies on its ability to express the amount of ambiguity in human reasoning. When the mathematical model of a process does not exist or exists with uncertainties, FLC has proven to be one of the best alternatives to move with unknown process. Even when the process model is well-known, there may still be parameter variation issues and power electronic systems, which are known to be often approximately defined. The purpose of this paper is to demonstrate that a great efficiency improvement of motor drive can be achieved and hence a significant amount of energy can be saved by adjusting the flux level according to the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. An extensive simulation results highlight and confirm the efficiency improvement with the proposed algorithm.

KEYWORDS:

  1. Induction Motor Drive
  2. Indirect Field Oriented Control (IFOC)
  3. Efficiency Enhancement
  4. Losses Minimization
  5. Optimization
  6. Fuzzy Logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1. Block diagram of the optimization system

 EXPECTED SIMULATION RESULTS:

Fig.2. Motor Performances Comparison

Fig.3. Motor efficiency evolution with motor load

 CONCLUSION:

This paper aims to improve the induction motor drive efficiency that leads to a significant amount of energy saving. This efficiency enhancement is carried out by adjusting the flux level depending on the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. A series of the induction motor drive performances are obtained with a variable load under this proposed algorithm. The application of the proposed algorithm yields to a series of simulation performances of the induction motor drive with a variable load. They present the IM drive efficiency evolution with a certain load profile with the suggested losses minimization strategy based on fuzzy control and the conventional field oriented control. The comparison between these two control schemes reveals that the achieved results are of a great interest. Indeed, the fuzzy control contributes with a great deal to the efficiency improvement for all operating speeds particularly in light load region. This contribution conducts to a paramount energy saving and hence to environment protection.

REFERENCES:

[1] I. Boldea, A. Nasser, The Induction Machine Design Handbook, CRC Press Inc; 2nd Revised Edition, 2009.

[2] Jinchuan. Li and all, “A new Optimization Method on Vector Control of Induction Motors”, Electric Machines and Drives, 2005 IEEE International Conference, 15-18 May 2005, pp.1995-2001.

[3] H. Sepahv and, Sh. Ferhangi, “Enhancing Performance of a Fuzzy Efficiency Optimizer for Induction Motor Drives”, Power Electronics Specialists Conference, 2006. PESC ’06. 37th IEEE, 18-22 June.2006, pp.1-5.

[4] Branko Blanusa and all, “An Improved Search Based Algorithm for Efficiency Optimization in the Induction Motor Drives”, XLII Konferencija- za ETRAN, Hercy-Novi, 2003.

[5] D. S. Kirischen, D. W. Novoty and T. A. Lipo, “Optimal Efficiency Control of an Induction Motor Drive”, IEEE Transaction on Energy Conversion, Vol. EC-2, N° 1, March 1987, pp.70-76.

An Intelligent Speed Controller for Indirect Vector Controlled Induction Motor Drive

 

ABSTRACT:

This paper presents the speed control scheme of indirect vector controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PI controller or Fuzzy-PI controller is employed in closed loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent based Fuzzy PI controller. Based on settling time and dynamic response the performance of Fuzzy Logic Controller is compared with that of the PI Controller to sudden load changes. It’s provides better control of motor torque with high dynamic performance. The simulated design is tested using various tool boxes in MATLAB. Simulation results of both the controllers are presented for comparison.

KEYWORDS:

  1. Indirect Vector Control (IVC)
  2. Space Vector Pulse Width Modulation (SVPWM)
  3. PI Controller
  4. Fuzzy Logic Controller (FLC)

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1 Block diagram of a proposed scheme

EXPECTED SIMULATION RESULTS:

Fig.2 Starting response

Fig.3 Step response

Fig.4 Speed response for with and without load impact

Fig.5 Torque response for with and without load impact

CONCLUSION:

In this paper the concept of fuzzy logic has been presented and the SVM based indirect vector controlled induction motor drive is simulated using both PI and Fuzzy PI controller. The results of both controllers under the dynamics conditions are compared and analyzed. The simulation result support that the FLC settles quickly and has better performance than when PI controller.

REFERENCES:

[1] Bimal K.Bose, “Modern Power Electronics and AC Drives”, Pearson education.

[2] Leonhand.W, ‘Control of Electrical Drives’, Springer Verlag 1990.

[3] Yang Li Yinghong, Chen Yaai and Li Zhengxi “A Novel Fuzzy Logic Controller for Indirect Vector Control Induction Motor

[4] Drive” Proceeding of the 7th World Congress on Intelligent and Automation Jun 25 – 27, 2008, Chongqing,China, pp. 24-28

[5] R.A. Gupta, Rajesh Kumar, S.V.Bhangale “Indirect Vector Controlled Induction Motor Drive with Fuzzy Logic based Intelligent Controller”, ICTES,UK,December 2007,pp.368-373.

 

 

An Intelligent Speed Controller for Indirect Vector Controlled Induction Motor Drive

ABSTRACT:

This paper presents the speed control scheme of indirect vector controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PI controller or Fuzzy-PI controller is employed in closed loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent based Fuzzy PI controller. Based on settling time and dynamic response the performance of Fuzzy Logic Controller is compared with that of the PI Controller to sudden load changes. It’s provides better control of motor torque with high dynamic performance. The simulated design is tested using various tool boxes in MATLAB. Simulation results of both the controllers are presented for comparison.

KEYWORDS:

  1. Indirect Vector Control (IVC)
  2. Space Vector Pulse Width Modulation (SVPWM)
  3. PI Controller
  4. Fuzzy Logic Controller (FLC)

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1 Block diagram of a proposed scheme

EXPECTED SIMULATION RESULTS:

 

Fig.2 Starting response

 Fig.3 Step response

 

Fig.4 Speed response for with and without load impact

Fig.5 Torque response for with and without load impact

CONCLUSION:

In this paper the concept of fuzzy logic has been presented and the SVM based indirect vector controlled induction motor drive is simulated using both PI and Fuzzy PI controller. The results of both controllers under the dynamics conditions are compared and analyzed. The simulation result support that the FLC settles quickly and has better performance than when PI controller.

REFERENCES:

[1] Bimal K.Bose, “Modern Power Electronics and AC Drives”, Pearson education.

[2] Leonhand.W, ‘Control of Electrical Drives’, Springer Verlag 1990.

[3] Yang Li Yinghong, Chen Yaai and Li Zhengxi “A Novel Fuzzy Logic Controller for Indirect Vector Control Induction Motor

[4] Drive” Proceeding of the 7th World Congress on Intelligent and Automation Jun 25 – 27, 2008, Chongqing,China, pp. 24-28

[5] R.A. Gupta, Rajesh Kumar, S.V.Bhangale “Indirect Vector Controlled Induction Motor Drive with Fuzzy Logic based Intelligent Controller”, ICTES,UK,December 2007,pp.368-373.

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.