An Efficient Constant Current Controller for PV SolarPower Generator Integrated with the Grid

ABSTRACT:  

This paper being the detailed design and modeling of grid integrated with the Photovoltaic Solar Power Generator. As the Photovoltaic System uses the solar energy as one of the renewable energies for the electrical energy production has an huge potential. The PV system is developing very fast as compared to its counterparts of the renewable energies. The DC voltage generated by the PV system is boosted by the DC-DC Boost converter.

The utility grid is incorporated with the PV Solar Power Generator through the 3-ı PWM DC-AC inverter, whose control is provided by a constant current controller. This controller uses a 3-ı phase locked loop (PLL) for tracking the phase angle of the utility grid and reacts fast enough to the changes in load or grid connection states, as a result, it seems to be efficient in supplying to load the constant voltage without phase jump. The complete mathematical model for the grid connected PV system is developed and simulated. The results verify that the proposed system is efficient to supply the local loads.

KEYWORDS:
  1. PV Solar Power Generator
  2. DC-DC Boost Converter
  3. PWM inverter
  4. PLL
  5. Constant Current Controller (CCC)

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1 Switching Model of Solar Inverter

EXPECTED SIMULATION RESULTS:

Fig.2 P-V Curve of the Solar Array

Fig. 3 V-I Curve of the Solar Array

Fig. 4 DC voltage delivered by the Boost converter

Fig. 5Inverter output voltage before filtering

Fig. 6 Inverter output voltage after filtering

Fig. 7 Load current for supplying the 2 MW load.

Fig.8 Load current for supplying the load of about 30 MW, 2 MVAr

CONCLUSION:

For improving the energy efficiency and power quality problem with the increment of the world energy demand, the power generation using the renewable energy source is the only solution. There are several countries located in the tropical and temperature regions, where the direct solar density may reach up to 1000W/m2. Hence PV system is considered as a primary resource. In this paper, the detailed modeling of grid connected PV generation system is developed. The DC-DC boost converter is used to optimize the PV array output with the closed loop control for keeping the DC bus voltage to be constant.

The 2 level 3-phase inverter is converting the DC into the sinusoidal AC voltage. The control of the solar inverter is support through the constant current controller. This controller tracks the phase and frequency of the utility grid voltage using the Phase- Locked-Loop (PLL) system and generates the switching pulses for the solar inverter. Using this controller the output voltage of the solar inverter and the grid voltage are in phase. Thus the PV system can be merge to the grid. The simulation results the being in this paper to validate the grid connected PV system model and the applied control scheme.

REFERENCES:

[1] A. M. Hava, T. A. Lipo and W. L. Erdman. “Utility interface issues for line connected PWM voltage source converters: a comparative study”, Proceeding of APEC’95, Dallas (USA), pp. 125-132, March 1995.

[2] L. J. BORLE, M. S. DYMOND and C. V. NAYAR, “Development and testing of a 20 kW grid interactive photovoltaic power conditioning system in Western Australia”, IEEE Transaction, Vol. 33, No. 2, pp. 502-508, 1997.

[3] M. Calais, J. Myrzik, T. Spooner, V. Agelidis, “Inverters for single- phase grid connected photovoltaic systems – an overview”, IEEE 33rd Annual Power Electronics Specialists Conference, Volume 4, 23-27 June 2002

[4] S. K. Chung, “Phase-Locked Loop for Grid connected Three-phase Power Conversion Systems”, IEE Proceeding on Electronic Power Application, Vol. 147, No. 3, pp. 213-219, 2000.

[5] S. Rahman, “Going green: the growth of renewable energy”, IEEE Power and Energy Magazine, 16-18 Nov./Dec. 2003.

Induction Motor Drive For PV Water PumpingWith Reduced Sensors

ABSTRACT:

 This study presents the reduced sensors based standalone solar photovoltaic (PV) energised water pumping. The system is configured to reduce both cost and complexity with simultaneous assurance of optimum power utilisation of PV array. The proposed system consists of an induction motor-operated water pump, controlled by modified direct torque control. The PV array is connected to the DC link through a DC–DC boost converter to provide maximum power point tracking (MPPT) control and DC-link voltage is maintained by a three-phase voltage-source inverter. The estimation of motor speed eliminates the use of tacho generator/encoder and makes the system cheaper and robust. Moreover, an attempt is made to reduce the number of current sensors and voltage sensors in the system. The proposed system constitutes only one current sensor and only one voltage sensor used for MPPT as well as for the phase voltage estimation and for the phase currents’ reconstruction. Parameters adaptation makes the system stable and insensitive toward parameters variation. Both simulation and experimental results on the developed prototype in the laboratory validate the suitability of proposed system.

 SOFTWARE: MATLAB/SIMULINK

 CIRCUIT DIAGRAM:

Fig. 1 circuit diagram (a) Proposed system,

EXPECTED SIMULATION RESULTS:

Fig. 2 Performance indices (a) PV array during starting to steady state at 1000 W/m2, (b) IMD indices at 1000 W/m2

 Fig. 3 Performance indices during insolation change 1000–500 W/m2

(a) PV array, (b) IMD indices 500–1000 W/m2, (c) PV array (d) IMD indices

Fig. 4 Adaptation mechanism

(a) Rs adaptation at rated speed and insolation, (b) τr Adaptation at rated speed and rated insolation

Fig. 5 Performance indices of the drive

(a) Starting at 1000 W/m2, (b) Starting at 500 W/m2, (c) Steady state at 1000 W/m2,

(d) Steady state at 500 W/m2

Fig. 6 Dynamic performance of the drive under variable insolation

(a) 1000–500 W/m2, (b) 500–1000 W/m2, (c) Intermediate speed signals at 1000–500

W/m2, (d) Intermediate speed signals at 500–1000 W/m2

Fig. 7 Intermediate signals in terms of

(a) Te* and Te at 1000–500 W/m2, (b) 500–1000 W/m2, (c) Reference stationary

components of flux and estimated flux at 1000–500 W/m2, (d) 500–1000 W/m2

Fig. 8 Reconstructed and measured current waveforms of phases a and b

at (a) Starting performance at 1000 W/m2, (b) 1000 W/m2, (c) 500 W/m2, (d) Boost

converter parameters at 1000 W/m2

CONCLUSION:

The modelling and simulation of the proposed system has been carried out in MATLAB/Simulink and its suitability is validated experimentally on a developed prototype in the laboratory. The system comprises of one voltage sensor and one current sensor, which are sufficient for the proper operation of the proposed system. The motor-drive system performs satisfactorily during starting at various insolations, steady-state, dynamic conditions represented by changing insolation. The speed estimation has been carried out by flux components in stationary frame of reference. The flux and torque are controlled separately. Therefore, successful observation of the proposed system with satisfactory performance has been achieved without the mechanical sensors. This topology improves the stability of the system. The stability of the system at rated condition toward stator resistance variation is shown by Nyquist stability curve and the stability toward the rotor-time constant perturbation is shown by Popov’s criteria. The DTC of an induction motor with fixed frequency switching technique reduces the torque ripple. The line voltages are estimated from this DC-link voltage. Moreover, the reconstruction of three-phase stator currents has been successfully carried out from DC-link current. Simulation results are well validated by test results. Owing to the virtues of simple structure, control, cost-effectiveness, fairly good efficiency and compactness, it is inferred that the suitability of the system can be judged by deploying it in the field.

REFERENCES:

[1] Masters, G.M.: ‘Renewable and efficient electric power systems’ (IEEE Press,Wiley and Sons, Inc., Hoboken, New Jersey, 2013), pp. 445–452

[2] Foster, R., Ghassemi, M., Cota, M.: ‘Solar energy: renewable energy and the environment’ (CRC Press, Taylor and Francis Group, Inc., Boca Raton, Florida, 2010)

[3] Parvathy, S., Vivek, A.: ‘A photovoltaic water pumping system with high efficiency and high lifetime’. Int. Conf. Advancements in Power and Energy (TAP Energy), Kollam, India, 24–26 June 2015, pp. 489–493

[4] Shafiullah, G.M., Amanullah, M.T., Shawkat Ali, A.B.M., et al.: ‘Smart grids: opportunities, developments and trends’ (Springer, London, UK, 2013)

[5] Sontake, V.C., Kalamkar, V.R.: ‘Solar photovoltaic water pumping system – a comprehensive review’, Renew. Sustain. Energy Rev., 2016, 59, pp. 1038– 1067

Solar Powered Based Water Pumping System Using Perturb and Observation MPPT Technique

ABSTRACT:

This paper concentrates on solar photovoltaic(PV) water pumping system using perturb and observation maximum power point tracking(MPPT) technique. This whole system is divided into two stages. In the first stage, an arrangement of PV modules is made which is a combination of number PV cells in series or parallel to extract the solar energy and convert into electricity. To maximize the power output of PV module, perturb and observation (P&O) MPPT technique has been used. In its second stage, direct torque and flux control(DTFC) with space vector modulation(SVM) is used to control switching pulses of the voltage source inverter(VSI). The speed of induction motor drive is controlled by DTFC technique. The whole system is developed in MATLAB and outputs are observed.

 KEYWORDS:

  1. Solar PV array
  2. MPPT
  3. P&O Algorithm
  4. DC-DC Boost converter
  5. DTFC-SVM
  6. Induction motor

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

 Fig-1: Solar Water Pumping System

EXPECTED SIMULATION RESULTS:

Fig. 2. DC link voltage (output voltage of the boost converter)

Fig. 3. output waveform of IMD under no load

Fig. 4. Waveforms under loading condition

CONCLUSION:

In this paper control methods which regulates the flow rate of water supply of solar powered based water pumping systen using IMD is illustrated. From the simulation results it can be concluded that this system has good performance. As per view of irrigation system , the SPV array has been operated under standard enviromental conditions. The system is operated on maximum power by using P&O MPPT algorithm. Water flow rate and stator current of motor is controlled by the speed PI controller.

 REFERENCES:

 [1] U. Sharma, S. Kumar, and B. Singh, “Solar array fed water pumping system using induction motor drive,” 1st IEEE Int. Conf. Power Electron. Intell. Control Energy Syst. ICPEICES 2016, 2017.

[2] M. A. G. De Brito, L. P. Sampaio, L. G. Jr, G. A. Melo, and C. A. Canesin, “Comparative Analysis of MPPT Techniques for PV Applications,” pp. 99–104, 2011.

[3] D. P. Hohm, “Comparative Study of Maximum Power Point Tracking Algorithms Using an Experimental, Programmable, Maximum Power Point Tracking Test Bed,” 2000.

[4] S. Member, “A Comparative study of different MPPT techniques using different dc-dc converters in a standalone PV system,” pp. 1690–1695, 2016.

[5] Z. Ben Mahmoud, M. Ramouda, and A. Khedher, “A Comparative Study of Four Widely-Adopted MPPT Techniques for PV Power Systems,” no. 1, pp. 16–18, 2016.

Simulation and Analysis of Stand-alone Photovoltaic System with Boost Converter using MATLAB/Simulink

ABSTRACT:  

Use of renewable energy and in particular solar energy has brought significant attention over the past decades.  Many research works are carried out to analyze and validate the performance of P V modules. Implementation of experimental set up for P V based power system with DC-DC converter to validate the performance of the system is not always possible due to practical constraints. Software based simulation model helps to analyze the performance of P V and a common circuit based model which could be used for validating any commercial P V module will be more helpful.

Simulation

of mathematical model for Photo voltaic (P V) module and DC-DC boost converter is presented in this paper. The model presented in this paper can be used as a generalized P V module to analyze the performance of any commercially available P V modules. I-V characteristics and P-V characteristics of P V module under different temperature and irradiation level can be obtained using the model. The design of DC-DC boost converter is also discussed in detail. Simulation of DC-DC converter is performed and the constant DC supply fed converter and P V fed converter generates the results.

 BLOCK DIAGRAM:

Fig. 1 Sim u link Model of proposed system

EXPECTED SIMULATION RESULTS:

Fig.2 P WM Pulse generation

Fig. 3(a) Input Voltage of DC-DC Boost Converter

Fig. 4(b) Output Voltage of Boost Converter constant DC input supply

Fig. 5 (c) Output current of Boost Converter constant DC input supply

Fig. 6 (a) Input voltage of P V fed converter

Fig. 7 (b) Output voltage and current waveform of P V fed converter

Fig. 8. Change in irradiation level of P V Module

Fig. 9. Output Voltage and Current wave forms of Boost Converter at

different irradiation level.

CONCLUSION:

A circuit based system model of P V modules helps to analyze the performance of commercial P V modules. The commonly used blocks in the form of masked subsystem block develops a general model of P V module. I-V and P-V characteristics outputs are generated for MS X 60 P V module under different irradiation and different temperature levels and the matlab/simulink simulates the module under various conditions as presented in the data sheet. The results obtained from the simulation shows excellent matching with the characteristics graphs provided in the data sheet of the selected models.

Thus,

the model can be used to analyze the performance of any commercial P V module. Matlab/Simulink simulates the DC-DC boost converter and the converter generates  the results with constant DC input supply and by interconnecting the P V module with it. The results shows close match between the output of converter with constant DC input and the P V fed converter. The P V fed DC-DC boost converter generates the output voltage and current for change of irradiation levels at constant temperature is also presented.

REFERENCES:

 [1] J. A. Go w, C.D.Manning, “ Development of photo voltaic array model for the use in power electronic simulation studies,” I E E Proceedings Electric power applications, Vol. 146, No.2, March,1999.

[2] J e e-H o o n Jung, and S. Ahmed, “Model Construction of Single Crystalline Photo voltaic Panels for Real-time Simulation,” IEEE Energy Conversion Congress & Expo, September 12-16, 2010, Atlanta, USA.

[3] T. F. E l shatter, M. T. E l ha g r y, E. M. Ab o u-E l z a  h a b, and A. A. T. Elk o u s y, “Fuzzy modeling of photo voltaic panel equivalent circuit,” in Proc. Conf. Record 28th IEEE Photo voltaic Spec. Conf., pp. 1656– 1659, 2000.

[4] M. Ba l z a n i and A. Re at ti, “Neural network based model of a P V array for the optimum performance of P V system,” in Proc. P h.D. Res. Micro electron. Electron., vol. 2, pp. 123–126, 2005.

CONTROL OF SOLID OXIDE FUEL CELL (SOFC) SYSTEMS IN STAND-ALONE AND GRID CONNECTED MODES

ABSTRACT

As energy consumption rises, one must find suitable alternative means of generation to supplement conventional existing generation facilities. In this regard, distributed generation (DG) will continue to play a critical role in the energy supply demand realm. The common technologies available as DG are micro-turbines, solar, photovoltaic systems, fuel cells stack and wind energy systems. In this project, dynamic model of solid oxide fuel cell (SOFC) is done. Fuel cells operate at low voltages and hence fuel cells need to be boosted and inverted in order to connect to the utility grid. A DC-DC converter and a DC-AC inverter were used for interfacing SOFC with the grid. These models are built in MATLAB/SIMULINK. The power characteristics of the fuel cell, DC-DC converter, DC-AC inverter are plotted for reference real power of 50kW for standalone applications. The power characteristics of the DC-AC inverter are plotted for 30kW, 50kW, 70kW of load and also for step change in load for grid connected applications.

KEYWORDS:

  1. Distributed Generation
  2. DC-DC Converter
  3. Solid Oxide Fuel Cell (SOFC)

SOFTWARE: MATLAB/SIMULINK

SIMULATION MODEL:

image001

Figure 1 Simulation model for GRID connected applications

SIMULATION RESULTS

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Figure 2. Power response for 50kW of load

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Figure 3. Current response for 50kW of load

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Figure 4. Power response for 50kW of load

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Figure 5. Current response for 30kW of load

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Figure 6. Power response for 70kW of load

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Figure 7. Current response for 70kW of load

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Figure 8. Response of power for step change in load

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Figure 9. Response of current for step change in load

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Figure 10. Response of power flow during faults in load

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Figure 11. Response of current flow during faults in load

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Figure 12. Response of Reactive Power Flow of 200 VAR

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Figure 13. Response of Reactive power Flow for step change

CONCLUSION

A dynamic model of the solid oxide fuel cell (SOFC) was developed in this project in MATLAB environment setup.

A DC-DC boost converter topology and its closed loop control feedback system have been built. A three phase inverter has been modeled and connected between the SOFC-DC-DC system on the one side and the utility grid on the other side. A control strategy for the inverter switching signals has been discussed and modeled successfully.

The fuel cell, the converter and the inverter characteristics were obtained for a reference real power of 50kW.The slow response of the fuel cell is due to the slow and gradual change in the fuel flow which is proportional to the stack current. The interconnection of the fuel cell with the converter boosts the stack voltages and also regulates it for varying load current conditions. The fuel cell stack voltage drops to zero for discontinuous current and the system shuts down. The fuel cell unit shuts off for real power above the maximum limit. Additional power at the converter is provided by the inductor, connected in series with the equivalent load which acts as an energy storage. The inductor can be replaced by any energy storage device such as a capacitor or a battery for providing additional power during load transients.

The inverter control scheme uses a constant power control strategy for grid connected applications and a constant voltage control strategy for standalone applications to control the voltage across inverter and current flowing through the load. The characteristics for the system have been obtained. The inverter voltage, current, power waveform have been plotted. The real power injection into the grid takes less than 0.1s to reach the commanded value of 50kW. The reactive power injection has been assumed to be zero and was evident from the simulation results. The maximum power limit on the fuel cell is 400kW. For any reference power beyond this limit, the fuel cell loses stability and drops to zero. This limit has been set by the parameters considered for the fuel cell data. Higher power can be commanded by either increasing the number of the cells, increasing the reversible standard potential or by decreasing the fuel cell resistance.

The system was then subjected to a step change in the reference real power from 40 to 80kW.The fuel cell, the converter and the inverter responses were obtained. The characteristics of the fuel cell (voltage, current and power) have a slower gradual change at the instant of step change. The DC link voltage was maintained at the reference value by the closed loop control system. Step change in the reference power from 40 to 80kW has been considered in order to observe the sharing of power from inverter to grid and from grid to the load of the fuel cell. The reactive power was zero until the step change and after the step change, oscillations were observed in the reactive power as well. Voltage, current, power characteristics of inverter, load and grid as been plotted for various conditions of load.

 REFERENCES

  1. Padulles, G. W. Ault, and J. R. McDonald, “An Approach to the Dynamic Modeling of Fuel Cell Characteristics for Distributed Generation Operation,” IEEE- PES Winter Meeting, vol. 1, Issue 1, pp. 134-138, January 2000.
  2. Pasricha, and S. R. Shaw, “A Dynamic PEM Fuel Cell Model,” IEEE Trans. Energy Conversion, vol. 21, Issue 2, pp. 484-490, June 2006.
  3. R. Pathapati, X. Xue, and J. Tang, “A New Dynamic Model for Predicting Transient Phenomena in a PEM Fuel Cell System,” Renewable Energy, vol. 30, Issue 1, pp. 1-22, January 2005.
  4. Wang, and M. H. Nehrir, “Dynamic Models and Model Validation for a PEM Fuel Cells Using Electrical Circuits,” IEEE Trans. Energy Conversion, vol. 20, Issue 2, pp. 442-451, June 2005.
  5. J. Hall, and R. G. Colclaser, “Transient Modeling and Simulation of a Tubular Solid Oxide Fuel Cell,” IEEE Trans. Energy Conversion, vol. 14, Issue 3, pp.749-753, September1999.

Active Power Factor Correction for Rectifier using Micro-controller

 

ABSTRACT:

Industrialization increases the use of inductive load and hence power system loses its efficiency. Rigid occurrence of mains rectification circuits and the day by day increase in electronics consumers inside the electronic devices enhances the cause of mains harmonic distortion. Power is very precious in the present technological revolution and thus it requires to improve the power factor with a suitable method.This paper presents the simulation and the experimental results for active power factor correction system. Closed loop circuit is simulated in MATLAB using PI controller. The system has been implemented in MATLAB/SIMULINK environment.

 

KEYWORDS:

 

  1. Micro-controller
  2. Power factor correction system
  3. DC-DC boost converter
  4. Total harmonic distortion (THD)
  5. PI controller

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

image001

Fig. 1. Circuit diagram of active power factor correction system

EXPECTED SIMULATION RESULTS:

 image002

Fig. 2. Input voltage of conventional converter in PSIM software

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Fig. 3. Output voltage of conventional converter in PSIM software

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Fig. 4. Output and input current waveform of conventional converter in PSIM software.

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Fig. 5. Input current waveform at 15kHz in PSIM software

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Fig. 6  Input voltage waveform at 15 kHz in PSIM software.

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Fig. 7 . Output voltage waveform at 15kHz in PSIM software.

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Fig. 8. Input current waveform in PSIM software.

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Fig. 9. Input voltage waveform in PSIM software

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Fig. 10. Output voltage waveform in PSIM software.

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Fig. 11. Firing pulse for MOSFET IRF640 captured in DSO.

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Fig. 12. Input current and voltage waveform captured in DSO.

CONCLUSION:

Analog firing circuit designing makes circuit complex and also it requires the maintenance. Employing microcontroller instead reduces all its disadvantages thus being economical. It is easier to design with precision output. It was very interesting and absorbing to design AC-DC converter in the power electronics laboratory using power MOSFET IRF640.The design is adequate for many purposes. These improvements have been tested in principle, but some detailed work remains to be done in this area. This research work can be extended for the speed control of the motor using PI controller or fuzzy logic controller, Maximum Power Point Tracking (MPPT) using this circuit can be studied later on.

 REFERENCES:

[1] B.K.Bose, “Modern power electronics and AC Drives”, PHI,2001 .

[2] P.C.Sen, “Power Electronics”, Tata McGraw Hill Publishers, 4th edition, 1987.

[3] N.Mohan, T.M.Undeland, W.P.Robbins, “Power Electronics: Converters application and Design”, New York: Wiley, 3rd edition, 2006.

[4] Mohammed E. El-Hawary, “Principles of Electric Machines with Power Electronic Applications”, Wiley India, 2nd edition, 2011.

[5] Gayakwad, “Operational Amplifier”, Prentice Hall of India, 2009.

 

 

 

Performance Analysis of P&O and Incremental Conductance MPPT Algorithms Under Rapidly Changing Weather Conditions

 

ABSTRACT:

In this paper, the comparative analysis of two maximum power point tracking (MPPT) algorithms namely Perturb and Observe (P&O) and Incremental conductance (InC) is presented for the Photo-Voltaic (PV) power generation system. The mathematical model of the PV array is developed and transformed into MATLAB/Simulink environment. This model is used throughout the paper to simulate the PV source characteristics identical to that of a 20 Wp PV panel. The MPPT algorithms generate proper duty ratio for interfacing dc-dc boost converter driving resistive load. The performances of these algorithms are evaluated at gradual and rapidly changing weather conditions where it is observed that InC method tracks the rapidly changing insolation level at a faster rate as compared to P&O. Depending upon the prevailing environmental conditions the MPPT algorithms finds a unique operating point to track the maximum available power. The algorithms find a fixed duty ratio by comparing the previous power, voltage and current thereby optimizing the power output of the panel. The main objective is to compare the tracking capability and stability of the algorithms under different environmental situations on par with other real world tests.

KEYWORDS:

  1. Maximum Power Point Tracking (MPPT)
  2. Photovoltaic (PV)
  3. DC-DC Boost Converter
  4. Perturb & Observe (P&O)
  5. Incremental Conduction (InC)

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

 image001

Fig. 1. PV Panel Interfaced with Boost Converter for MPP Tracking

 EXPECTED SIMULATION RESULTS:

 image002

 Fig. 2. Experimental Measured PV Characteristics

 image003

 Fig. 3. Experimental Results showing Source Voltage, Load Voltage and Duty Ratio

image004

Fig. 4. Performances of P&O and InC under slowly changing climatic conditions (a) Irradiations Levels (b), (c) & (d) Duty ratio (e) Panel Voltage (f) Panel Power (g) Oscillations in Duty by the algorithms

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Fig. 5. Performances of P&O and InC under rapidly changing climatic conditions (a) Insolations (b)& (c) Duty ratio (d)&(e) Panel Voltage (f) Panel Power

 CONCLUSION:

The presented studies in this paper were the comparative analysis of two MPPT algorithms, Perturb & Observe and Incremental Conductance and conducted through boost converter. The simulation results prove positively that the P&O and the Incremental Conductance MPPTs reach the intended maximum power point. In the slowly changing whether both algorithms perform without significantly changes. It has observed that the Incremental Conductance reaches at the MPP three times faster than P&O in all cases and shows better performance for rapid changes and a better stability when the MPP is achieved. It has observed that P&O shows oscillations around the MPP when it reaches in steady state position which results in some power loss. But in case of InC there are no additional oscillations at steady state condition. However the P&O MPPT are mostly used in practice due to their simplicity. The originality and the specificity of the presented results obtain during this research reside in the fact that external parameters as irradiation and fixed temperature were introduced, at first as linear functions (ramp input) and, at second as random (step input) ones describing more closely the actual applicative conditions. The effect of the changing weather on the voltage and power of the PV panel according to change in MPP has shown in the results section.

REFERENCES:

[1] Tariq, J. Asghar, “Development of an Analog Maximum Power Point Tracker for Photovoltaic Panel”, PEDS. International Conference on, 2005, vol. 1, no., pp. 251, 255.

[2] H. Al-Bahadili, H. Al-Saadi, R. Al-Sayed, M.A.-S. Hasan, “Simulation of maximum power point tracking for photovoltaic systems”, Applications of Information Technology to Renewable Energy Processes and Systems (IT-DREPS), 1st International Conference & Exhibition on the , 2013, vol., no., pp. 79,84.

[3] Lu Yuan, Cui Xingxing, “Study on maximum power point tracking for photovoltaic power generation system”, Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference on, 2010, vol. 9, pp. 180,183.

[4] G. Walker, “Evaluating MPPT converter topologies using a MATLAB PV model”, Journal of Electrical & Electronics Engineering, 2001, Australia, IEAust, vol. 21, No. 1, pp. 49-56.

[5] Beriber, D.; Talha, A, “MPPT techniques for PV systems,” Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on, vol., no., pp.1437, 1442, 13-17 May 2013.