A Single-Phase Buck-Boost Matrix Converter with Only Six Switches and Without Commutation Problem

ABSTRACT:

 In this paper, a single-phase buck-boost matrix converter is proposed which can both buck and boost the input voltage with step-changed frequency. It consists of only six unidirectional current flowing bidirectional voltage blocking switches, two input and output filter capacitors, and one inductor. It has following advantages over the existing single-phase matrix converters: 1) it can both buck and boost input voltage solving the limited voltage transfer ratio (only boost or buck) problem; 2) it also has enhanced reliability as it is immune from shoot-through problem of voltage source when all switches are turned-on simultaneously, and therefore, it has no need of PWM dead times and RC snubbers or dedicated soft-commutation strategies to solve the commutation problem; 3) it can also use high speed power MOSFETs as their body diodes never conduct, which eliminate their poor reverse recovery problem. The operation principle of the proposed converter is given, and switching strategies are developed to obtain various multiples and submultiples of input frequency. To verify its performance, a laboratory prototype is fabricated and experiments are performed to produce step-down and step-up voltage with three different frequencies of 120, 60 and 30 Hz.

KEYWORDS:

1.      Buck-boost operation

2.      Commutation problem

3.      Single-phase matrix converter

4.      Step-changed frequency

5.      Z-source

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1. Circuit topology of the proposed single-phase buck-boost MC.

 EXPERIMENTAL RESULTS:

Fig. 2. Experimental results of the proposed ac-ac converter under non-inverting buck-boost mode operations for  and   . (a) Boost operation when,  . (b) Buck operation when ,  . (c) Components stresses. (d) Zoom-in waveforms of (c).

Fig. 3. Experimental results of the proposed ac-ac converter under inverting buck-boost mode operations for   and  . (a) Boost operation when . (b) Buck operation when . (c) Components stresses. (d) Zoom-in waveforms

of (c).

Fig. 4. Experimental results of the proposed ac-ac converter under buck-boost mode operations for  and step-down frequency operation when . (a) Boost operation when . (b) Buck operation when . (c) Switch voltage and inductor current stresses (d) Zoom-in waveforms of (c).

Fig. 5. Experimental results of the proposed ac-ac converter under buck-boost mode operations for  and   step-up frequency operation when . (a) Boost operation when. (b) Buck operation when . (c) Switch voltage and inductor current stresses (d) Zoom-in waveforms of (c).

Fig. 6. Experimental waveforms of input voltage , output voltage , input current 

, and output current during 60 Hz inverting mode operation with inductive load    when 

 Fig. 7. Experimental waveforms of input voltage , output voltage , input current , and output current during 30 Hz mode operation with inductive load when 

Fig. 8. Efficiency of the proposed single-phase buck-boost matrix converter.

CONCLUSION:

In this paper, a single-phase buck-boost MC is proposed which consists of one inductor, two filter capacitors, and six unidirectional current conducting bidirectional voltage blocking switches. It can step-changed the output frequency with both voltage buck and boost operation, therefore, solves the limited gain (only buck or boost) ability of the existing single-phase MCs. The proposed single-phase MC is more reliable than the existing MCs as it can turn on all switches simultaneously without current overshoot problem caused by short-circuit of voltage source. Therefore, it does not have commutation problem and eliminates the need for PWM dead times and lossy RC snubbers or dedicated soft-commutation strategies, which is a significant advantage.

A detailed analysis of the proposed topology and switching strategies are given for buck-boost operation with step-down, same and step-up frequency. A scaled down laboratory prototype of the proposed MC with output voltage of 70 Vrms was fabricated based on TMS320F28335 DPS-kit to generate the control signals, and experimental results under buck and boost modes were given for output frequencies of 30 Hz (step-down frequency), 60 Hz (same frequency) and 120 Hz (step-up frequency). The proposed MC can be used in applications which require voltage regulation along with frequency variation such as to control the speed of a fan or a pump, to drive induction motor, for induction heating, and to implement a high boost AC-DC MC based on Cockcroft-Walton voltage multiplier, etc.

REFERENCES:

[1] B. H. Kwon, B. D. Min, and J. H. Kim, “Novel topologies of AC choppers,” IEE Proc. Electr. Power Appl., vol. 143, no. 4, pp. 323–330, Jul. 1996.

[2] X. P. Fang, Z. M. Qian, and F. Z. Peng, “Single-phase Z-source PWM ac–ac converters,” IEEE Power Electron. Lett., vol. 3, no. 4, pp. 121–124, Dec. 2005.

[3] T. B. Lazzarin, R. L. Andersen, and I. Barbi, “A switched-capacitor three-phase ac-ac converter,” IEEE Trans. Ind. Electron., vol. 62, no. 2, pp. 735–745, Feb. 2015.

[4] D.-C. Lee, and Y.-S. Kim, “Control of single-phase-to-three-phase ac/dc/ac PWM converters for induction motor drives,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp. 797– 804, Apr. 2007.

[5] J. E. C. d. Santos, C. B. Jacobina, N. Rocha, and E. R. C. d. Silve, “Six-phase machine drive system with reversible parallel ac-dc-ac converters,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 2049– 2053, May. 2011.

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A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system

ABSTRACT:

The power from wind varies depending on the environmental factors. Many methods have been proposed to locate and track the maximum power point (MPPT) of the wind, such as the fuzzy logic (FL), artificial neural network (ANN) and neuro-fuzzy. In this paper, a variable-speed wind-generator maximum power- point-tracking (MPPT) based on adaptative neuro-fuzzy inference system (ANFIS) is presented. It is designed as a combination of the Sugeno fuzzy model and neural network. The ANFIS model is used to predict the optimal speed rotation using the variation of the wind speed as the input. The wind energy conversion system (WECS) employing a permanent magnet synchronous generator connected to a DC bus using a power converter is presented. A wind speed step model was used in the design phase. The performance

of the WECS with the proposed ANFIS controller is tested for fast wind speed variation. Simulation results showed the possibility of achieving maximum power tracking for the wind and output voltage regulation for the DC bus simultaneously with the ANFIS controller. The results also proved the good response and robustness of the control system proposed.

KEYWORDS:

1.      Wind energy

2.      Power generation

3.      Variable speed wind generator

4.      MPPT

5.      ANFIS

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Wind generation system configuration.

 EXPECTED SIMULATION RESULTS:

Fig. 2. Training error.

Fig. 3. Setup function of wind speed.

Fig. 4. Rotor speed response.

Fig..5 . Output power response.

Fig. 6. Efficiency.

Fig. 7. DC bus voltage response.

Fig. 8. Wind speed.

Fig. 9. Rotor speed response.

Fig. 10. Output power response.

Fig. 11. Efficiency.

Fig. 12. DC bus voltage response.

Fig. 13. Comparative output power response with ANFIS and FL.v

CONCLUSION:

In this paper, the WECS was modeled using d-q rotor reference frame. A variable speed wind generator maximum power point tracking based on an adaptative neuro-fuzzy-inference-system (ANFIS) was presented. The feasibility of this controller is demonstrated and the simulation results for both cases proved the robustness, fast response, and exact maximum power tracking capabilities of the ANFIS control strategy. The results show also that ANFIS model has a better response compared to fuzzy logic model.

REFERENCES:

Ansel, A., & Robyns, B. (2006). Modelling and simulation of an autonomous variable speed micro hydropower station. Mathematics and Computers in Simulation, 71(4), 320–332.

Avci, E., & Avci, D. (2007). The performance comparison of discrete wavelet neural network and discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33, 3.

Avci, E., Hanbay, D., & Varol, A. (2007). An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33(3), 582–589.

Aznarte, M. J. L., Sánchez, J. M. B., Lugilde, D. N., Fernández, C. D. L., Guardia, C. D. Dl., & Sánchez, F. A. (2007). Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications, 32(4), 1218–1225.

Avci, E., & Akpolat, Z. H. (2006). Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications, 31(3), 495–503.

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.