Maximum power extraction from wind energy system based on fuzzy logic Control

ABSTRACT:

This paper proposes a variable speed control scheme for grid-connected wind energy conversion system (WECS) using permanent magnet synchronous generator (PMSG). The control algorithm tracks the maximum power for wind speeds below rated speed of wind turbines and ensures the power will not go over the rated power for wind speeds over the rated value. The control algorithm employs fuzzy logic controller (FLC) to effectively do this target. The wind turbine is connected to the grid via back-to-back PWM-VSC. Two effective computer simulation packages (PSIM and Simulink) are used to carry out the simulation effectively. The control system has two controllers for generator side and grid side converters. The main function of the generator side controller is to track the maximum power through controlling the rotational speed of the wind turbine using FLC. In the grid side converter, active and reactive power control has been achieved by controlling q-axis and d-axis current components, respectively. The d-axis current is set at zero for unity power factor and the q-axis current is controlled to deliver the power flowing from the dc-link to the electric utility grid.

KEYWORDS:

1.      Wind energy systems

2.      Permanent magnet synchronous generator

3.      Fuzzy logic controller

4.      Simulation software packages (PSIM and Simulink)

5.      Maximum power point tracking

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Schematic diagram of the overall system.

 EXPECTED SIMULATION RESULTS:

Fig. 2. Different simulation waveforms: (a) wind speed variation (7–13) m/s, (b)

actual and reference rotational speed (rad/s), (c) CP, (d) dc-link voltage (v), (e) active

power (W), and (f) reactive power (Var).

CONCLUSION:

A co-simulation (PSIM/Simulink) program has been proposed for WECS in this paper where PSIM contains the power circuits of the WECS and Matlab/Simulink contains the control circuit of the WECS. The idea behind integrating these two software packages is that, the Matlab/Simulink is a powerful tool for modeling the control  system, FLC and mathematical manipulation whereas PSIM is a powerful tool for modeling power electronics circuits and switches. Co-simulation (PSIM/Simulink) makes the simulation process so much easy, efficient, faster in response and powerful. The integration between PSIM and Simulink is the first time to be used in modeling WECS which help researchers in modifying the modeling of WECS in the future. The WT is connected to the grid via back to- back PWM converters which have been modeled in PSIM. The generator side and the grid side controllers have been modeled in Simulink. The generator side controller has been used to track the maximum power generated from WT through controlling the rotational speed of the turbine using FLC. The PMSG has been controlled in indirect-vector field oriented control technique and its speed reference has been obtained from FLC. In the grid side converter, active and reactive power control has been achieved by controlling q-axis and d-axis grid current components respectively. The d-axis grid current is controlled to be zero for unity power factor and the qaxis grid current is controlled to deliver the power flowing from the dc-link to the grid. The simulation results prove the superiority of FLC and the whole control system.

REFERENCES:

[1] V. Oghafy, H. Nikkhajoei, Maximum power extraction for a wind-turbine generator with no wind speed sensor, in: Proceedings on IEEE, Conversion and Delivery of Electrical Energy in the 21st Century, 2008, pp. 1–6.

[2] T. Ackerman, L. Söder, An overview of wind energy status 2002, Renewable and Sustainable Energy Reviews 6 (2002) 67–128.

[3] M.R. Dubois, Optimized permanent magnet generator topologies for direct drive wind turbines, Ph.D. dissertation, Delft Univ. Technol., Delft, The Netherlands, 2004.

[4] A. Grauers, Design of direct-driven permanent-magnet generators for wind turbines, Ph.D. dissertation, Chalmers Univ. Technol., Goteborg, Sweden, 1996.

[5] T. Thiringer, J. Linders, Control by variable rotor speed of a fixed pitch wind turbine operating in a wide speed range, IEEE Transactions on Energy Conversion EC-8 (1993) 520–526.

Fuzzy logic control for a wind/battery renewable energy production system

ABSTRACT:

In this study, a designed proportional-integral (PI) controller and a fuzzy logic controller (FLC) that fix  the voltage amplitude to a constant value of 380 V and 50 Hz for loads supplied from a wind/battery hybrid energy system are explained and compared. The quality of the power produced by the wind turbine is affected by the continuous and unpredictable variations of the wind speed. Therefore, voltage-stabilizing controllers must be integrated into the system in order to keep the voltage magnitude and frequency constant at the load terminals, which requires constant voltage and frequency. A fuzzy logic-based controller to be used for the voltage control of the designed hybrid system is proposed and compared with a classical PI controller for performance validation. The entire designed system is modeled and simulated using MATLAB/Simulink GUI (graphical user interface) with all of its subcomponents.

KEYWORDS:

1.      Fuzzy logic controller

2.      Proportional-integral controller

3.      Renewable energy

4.      Wind turbine

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Figure 1. PV/battery renewable source.

EXPECTED SIMULATION RESULTS:

Figure 2. Power on the load: a) with a PI controller and b) with a FLC.

Figure 3. Results of the PI controller.

Figure 4. Results of the FLC.

Figure 5. PI controller results.

Figure 6. FLC results.

Figure 7. Vab−load variation for PI controller

Figure 8. Vab−load variation for the FLC.

Figure 9. Wind turbine V, I, active, and reactive power variations with PI system.

Figure 10. Wind turbine V, I, active, and reactive power variations with FLC system.

CONCLUSION:

The waveform of the loads was very similar to the sinus wave form using both the PI and fuzzy logic controllers. The system can fix the voltage on the loads at a constant value of 380 V regardless of effects from the variations of the wind speed. The system frequency value is steady at 50 Hz. According to the value of the wind speed, the used regulator works effectively by turning on and off the batteries. The maximum overshoot and settling time values of the FLC were much better than those of the PI controller. The PI controller maximum overshoot voltage that can be reached is 392 V. This value is 384 V with the FLC system. The PI controller’s setting time was 2 s; this value was 0.05 s for the FLC. When the THD values are compared, it is seen that both controllers had values in the standard ranges. However, the FLC’s value was better than the PI’s, as was its sinus wave form. When the produced energy is greater and the loads are low, the wind turbine must be arranged to recharge the batteries. This can be done by the management of the energy. When there is no wind, the loads are supplied only with batteries. When the batteries are empty, the loads will have no energy supply. To prevent this situation, a diesel generator can be added to the system or the system can be supplied with energy by the main network.

REFERENCES:

[1] J. Peinke, P. Schaumann, S. Barth, Wind Energy Proceedings of the Euromech Colloquium, Berlin-Heidelberg, Springer, 2007.           

[2] Global Wind and Energy Council, Market Forecast 2010-2014, available at: http://www.gwec.net/fileadmin/documents/Publications/Global Wind 2007 report/market%20forecast%202010- 2014.JPG.

[3] M.R. Patel, Wind and Solar Power Systems, Boca Raton, Florida, CRC Press, 2006.

[4] T. Ackerman, Wind Power in Power Systems, New York, John Wiley and Sons, 2005.

[5] P.A. Stott, M.A.Mueller, “Modelling fully variable speed hybrid wind diesel systems”, 41st International Universities Power Engineering Conference, Vol. 1, pp. 212-216, 2006.

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machin

ABSTRACT:

In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained.

KEYWORDS:

1.      Induction motor

2.      Lyapunov function

3.      Model reference

4.      Adaptive system (MRAS)

5.      Sensorless control

6.      Speed estimation

7.      Vector control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of the new MRAS observer.

EXPECTED SIMULATION RESULTS:

Fig. 2. Speed estimation error.

Fig. 3. Speed tracking error.

Fig. 4. Rotor flux.

Fig. 5. Load torque and Rs variations.

Fig. 6. Classical MRAS observer: Reference, actual, and estimated speed

for load torque and Rs variations.

Fig. 7. Classical MRAS observer: Zoom of Reference, actual, and estimated

speed for load torque and Rs variations.

Fig. 8. Speed of induction motor.

Fig. 9. Speed zoom.

Fig. 10. Speed estimation error.

Fig. 11. Speed tracking error.

 CONCLUSION:

In this paper, a new MRAS rotor speed observer was proposed to improve the performance of sensorless vector controller of induction machine. The control robustness is achieved by a sliding-mode controller and its stability is proved using a Lyapunov approach. Simulation and experimental results for different speed profiles had shown, on the one hand, that the proposed new MRAS observer was able to estimate accurately the actual speed at low and zero speed when the conventional MRAS observer is limited. On the other hand, the robustness of the proposed observer regarding load torque and stator resistance variations, especially at low and zero speed, is much better than the classical observer.

REFERENCES:

[1] J. W. Finch and D. Giaouris, “Controlled AC electrical drives,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 481–491, Feb. 2008.

[2] J.-I. Ha and S.-K. Sul, “Sensorless field-orientation control of an induction machine by high-frequency signal injection,” IEEE Trans. Ind. Appl., vol. 35, no. 1, pp. 45–51, Jan./Feb. 1999.

[3] C. Caruana, G.M. Asher, and M. Sumner, “Performance of high frequency signal injection techniques for zero-low-frequency vector control induction machines under sensorless conditions,” IEEE Trans. Ind. Electron., vol. 53, no. 1, pp. 225–238, Feb. 2006.

[4] F. Peng and T. Fukao, “Robust speed identification for speed-sensorless vector control of induction motors,” IEEE Trans. Ind. Appl., vol. 30, no. 5, pp. 1234–1240, Sep./Oct. 1994.

[5] C. Schauder, “Adaptive speed identification for vector control of induction motors without rotational transducers,” IEEE Trans. Ind. Appl., vol. 28, no. 5, pp. 1054–1061, Sep./Oct. 1992.

A Hysteresis Current Controller for Grid-Connected Inverter with Reduced Losses

ABSTRACT:

In this paper, a hysteresis current controller with reduced losses for three-phase grid-connected inverter is proposed. In the proposed hysteresis current controller, one of the inverter phase is clamped to the positive or negative inverter buses depending on the polarity of the phase current. Totally, each inverter phase is clamped for the duration of one third of the fundamental output period. As the inverter phase is inactive when the current is the highest, the switching losses are reduced. Simulation and experimental results are included to show the effectiveness of the proposed controller.

KEYWORDS:

1.      Current controller

2.      Hysteresis

3.      Grid-connected inverter

4.      Losses

5.      Clamped

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig.1. Power controller of grid-connected inverter

 EXPECTED SIMULATION RESULTS:

Fig.2. Output current and switching pattern of: (a) conventional hysteresis

current controller, (b) proposed hysteresis current controller

CONCLUSION:

A simple hysteresis current controller with reduced losses has been proposed in this paper. In the proposed current controller, one of the inverter phase is clamped to the positive or negative DC bus, depending on the polarity, when the magnitude of the current is the greatest. This lead to reduction of the average switching frequency as well as the switching losses. Simulation and experimental results have shown that the proposed hysteresis controller is able to reduce the switching losses without sacrificing the output current waveform.

REFERENCES:

[1] S. Jain and V. Agarwal, “A Single-Stage Grid Connected Inverter Topology for Solar PV Systems With Maximum Power Point Tracking,” IEEE Trans. Power Electron., vol. 22, no. 5, pp. 1928- 1940, 2007.

[2] M. Mohseni and S. M. Islam, “A new vector-based hysteresis current control scheme for three-phase PWM voltage-source inverters,” IEEE Trans. Power Electron., vol. 25, no. 9, pp. 2299– 2309, 2010.

[3] M. P. Kazmierkowski and M. A. Dzieniakowski, “Review of current regulation techniques for three-phase PWM inverters,” Proc. IECON’94 - 20th Annu. Conf. IEEE Ind. Electron., vol. 1, pp. 567– 575, 1994.

[4] Y. Zhang and H. Lin, “Simplified model predictive current control method of voltage-source inverter,” 8th Int. Conf. Power Electron. - ECCE Asia, pp. 1726–1733, 2011.

[5] C. C. Hua, C. W. Wu, and C. W. Chuang, “A digital predictive current control with improved sampled inductor current for cascaded inverters,” IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 1718–1726, 2009.