IJMTES International Journal of Modern Trends in Engineering and Science ISSN:

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1 Design of Fuzzy Based Maximum Power Point Tracking For Photovoltaic Applications Anjana Asok (Electronics & Communication, Mohandas College of Engineering, Trivandrum, India, Abstract The need for a renewable energy source that will not harm the environment has become very crucial nowadays as energy demand around the world is increasing. Using photovoltaic (PV) cells is one way to meet this need, converting sunlight directly into electricity without moving parts and no harmful pollution, by means of Photo-Voltaic effect. Photovoltaic effect is a process in which two dissimilar materials in close contact produce an electrical voltage when struck by light or other radiant energy. Today, solar-generated electricity serves people living in the most isolated spots on earth as well as in the center of metropolitan cities. First used in the space program, photovoltaic (PV) systems are now used in utility grid systems, telecommunications, landbased aids to navigation and more. This technology is still expensive when compared to other sources of power so it is important to optimize the efficiency of PV panels. This can be a challenge because as irradiation and temperature changes the voltage and current in the circuit changes. Maximum power point tracking (MPPT) is a technique that is used to get the maximum possible power from photovoltaic devices regardless of weather conditions. Different algorithms are existing for MPPT. In this paper, a Fuzzy based Maximum power point tracking algorithm is simulated. Fuzzy Based MPPT algorithm possess advantages such as improved time response, increased tracking speed and sudden response to unforeseen changes of atmospheric condition. Keywords Photo-voltaic (PV) Module; Fuzzy Logic; Maximum power point Tracking (MPPT) Algorithm; Boost Converter. 1. INTRODUCTION While fossil fuels exhaustion and greenhouse effects are widely concerned around the world, it is important to find alternative energy. Green energy offering the promise of clean and abundant energy gathered from self-rejuvenation sources such as solar energy, geothermal energy and wind source are typically developed. Solar cells are unique in that they directly convert the solar radiation into electricity. Photovoltaic (PV) power management concepts are essential to extract as much power as possible from the solar energy. Solar energy does not cause greenhouse effect, or cause pollutions, and is expected to enhance the feasibility of lowering cost and increasing conversion efficiency [8]. This paper focuses on the developmentsof high performance single-stage PV system with maximum power point tracking(mppt). Fig 1 shows the characteristic curves of PV cell. The maximum power point is changed nonlinearly. BP Solars SX series provides reliable photovoltaic power operating DC loads directly or, in an inverter-equipped system, AC loads. With 72 cells in series, it charges 24V batteries (or multiples of 24V) efficiently in virtually any climate. It can provide Power output for 25 years. Solar power uses the photovoltaic (PV) effect to transform solar energy into electrical energy, the PV panel is a nonlinear power source. The output power of a PV panel array depends on the PV voltage and unforeseen weather conditions. In order to optimize the ratio between output power and installation cost, dc/dc converters are used to draw maximum power from the PV panel array [13], [11]. Many algorithms have been proposed to adjust the duty cycle of the converter for maximum power point tracking (MPPT), such as perturb-and-observe method [15], Incremental conductance method [14], curve fitting method [9], fuzzy logic methods [10] [6], neural networks [5],etc. Most of MPPT methods lack strict convergence analysis, and thus, only pertinent MPPT is achieved. Among the intelligent based methods fuzzy logic controller has its own merits such that the MPPT algorithm can be easily formed. The shape of the membership function of fuzzy logic controllers can be adjusted such that the gap between the operation point and maximum power point can be adjusted. Figure 1. Characteristic Curve of PV cell Therefore in this paper, a control technique using fuzzy logic control associated with a MPPT controller are used to improve the efficiency of the photovoltaic system. Figure 2. Typical diagram of MPPT control in a PV System Volume: 02 Issue:

2 The proposed fuzzy logic process comprises of different rule bases which extracts maximum power from a PV module under varying solar irradiation and temperature. Typical diagram of the connection of MPPT control in a PV system is shown in Fig2.The rest of the paper is organized as follows. Section 2 starts with the Photo-Voltaic modeling. In Section 3, DC-DC boost converter is designed. In Section 4 proposed fuzzy MPPT controller is designed. Results are shown in Sections 5. Finally, conclusions are drawn in Section MODELLING OF A PV ARRAY PV cells are made of semiconductor materials, such as silicon. For solar cells, a thin semiconductor wafer is specially treated to form an electric field, positive on one side and negative on the other side. When light energy strikes the solar cell, electrons are knocked loose from the atoms in the semiconductor material. If conductors are attached to the positive and negative sides, forming an electrical circuit, the electrons can be captured in the form of an electric current that is, electricity.the typical equivalent circuit of PV cell is shown in Fig 3. An ideal solar cell is modelled by a current source in parallel with a diode. However no solar cell is ideal and thereby shunt and series resistances are added to the model. I S =I rs [ T C ] 3 exp ( qe g(t C T Ref ) ) (3) T Ref T C T Ref KA I rs is the cells reverse saturation current at a reference temperature and standard solar radiation and E g is the banggap energy of the semiconductor used in the cell. A PV array is a group of several PV cells which are electrically connected in series and parallel circuits to generate the required current and voltage. Consider there are N P parallel and N S series cells in a module. The terminal equation for the current and voltage of the array becomes as follows, VV qq( + IIRR SS ) NN I=N p I ph - N p I s [exp( SS NN PP NN PP VV NN SS +IIRR SS RR ssh (4) )-1] - KKKKTT CC The shunt resistance R sh is inversely related with shunt leakage current to the ground. In general, the PV efficiency is insensitive to variation in R sh and the shunt resistance can be assumed to approach infinity. On the other hand, asmall variation in R s will significantly affect the PV output power. I = N p I ph - N p I s [exp ( qq(vv+iirr ssss ) NN SS KKKKTT CC )- 1] (5) R sm = N S R S N P (6) The parameters of PV module used in this work are tabulated in table shown below. Parameter (STC) Value Parameter (STC) Value I mpp 4.35A V mpp 34.5V Figure 3. Equivalent circuit of PV cell The voltage-current characteristic equation of a solar cell is given as I = I ph I s [exp ( q(v+ir S ) ) -1] - V+IR S (1) KT C A R sh I ph is a light-generated current or photocurrent, I s is the cell saturation of dark current, q (= 1.6*10 19 C) is an electron charge, K (= 1.38*10 23 J/K) is a Boltzmann s constant, T c is the cells working temperature, A is an ideal factor, R sh is a Shunt resistance, and R s is a series resistance of solar cell. The photocurrent mainly depends on the solar insolation and cells working temperature, which is described as I ph = [I sc + K I (T C -T Ref ) ] G (2) I sc is the cell s short-circuit current at a 25 o C and 1kW/m 2, K I is the cell s short-circuit current temperature coefficient, T Ref is the cells reference temperature, and, G is the solar insolation in kw/m 2, On the other hand, the cells saturation current varies with the cell temperature, which is described as I sc 4.75A V oc 43.5V P max 150W R s 0.008Ω A 1.3 NOCT 47 O C N s 72 N p 1 Figure 4. Specifications of PV Module 3. DC-DC BOOST CONVERTER A power electronic system comprising a boost type dcdc converter is used to feed the power generated by the PV module to the load. A DC-DC step up converter is introduced to maintain the load voltage constant.the block schematic of the proposed scheme is shown in Fig 5. The input output voltage relationship of a dc-dc boost converter is given by V o /V i = 1/(1-D) (7), D = duty cycle. Since the duty ratio D is between 0 and 1 the output voltage must be higher than the input voltage in magnitude. As the DC voltage varies with irradiation, a closed loop fuzzy controller is incorporated to automatically vary the duty-cycle of the DC-DC converterto obtain constant DC voltage at the output. Volume: 02 Issue:

3 Inference engine mainly consist of Fuzzy rule base and fuzzy implication sub blocks. The fuzzified inputs are now fed to the inference engine and the rule base is then applied. The output fuzzy set are then identified using fuzzy implication method. e (t) = PP = PP(tt) PP(tt 1) (8) VV VV(tt) VV(tt 1) Δ e(t) =e(t) e(t-1) (9) Figure 5. Boost Converter 4. FUZZY LOGIC MPPT CONTROLLER The fuzzy control algorithm is capable of improving the tracking performance as compared with the classical methods for both linear and nonlinear loads. Also, fuzzylogic is appropriate for nonlinear control because it does not use complex mathematical equation. They have advantages to be robust and relatively simple to design since they do not require the knowledge of the exact model. The two FLC input variables are the error e and change of error e. The behaviour of a FLC depends on the shape of membership functions of the rule base. Figure 7. Membership function plots for e A) FUZZIFICATION The membership function values are assigned to the linguistic variables using five fuzzy subset called Negative Big (NB), Negative Small (NS), Zero (ZE), Positive Small (PS), Positive Big (PB). Fuzzy associative memory for the proposed system is given in Fig 6. Variable e and e are selected as the input variables, where e is the error between the reference voltage (V) and actual voltage (V o ) of the system, e is the change in error in the sampling interval. The output variable is the reference signal for PWM generator U. Δe e NB NS ZE PS PB NB NB NB NB NS ZE NS NB NB NS ZE PS ZE NB NS ZE PS PB PS NS ZE PS PB PB PB ZE PS PB PB PB Figure 8. Membership function plots for Δe A) DEFUZZIFICATION Once fuzzification is over, output fuzzy range is located. Since at this stage a non-fuzzy value of control is available a defuzzification stage is needed. Centroid Defuzzification method is used for defuzzification in the proposed scheme. Figure 6. Control Rules The membership function of the variables error, change in error and change in reference signal for PWM generator are shown in Fig 8,9,10 respectively. B) INFERENCE ENGINE Figure 9. Membership function plots foru Volume: 02 Issue:

4 5. SIMULATION RESULTS To test the operation of the designed system, the irradiation was changed while the temperature maintained its value at 25 o C. The illumination varies between three different levels. The first level is 1000 W/m 2 which starts at 0.0 s and finishes at 0.4s. Another level, which is 800 W/m 2, starts at 0.4s and lasts for 0.3 s and finally, 600 W/m 2 irradiation level starts right after previous level and ends at 1.0 s. In these three levels chosen, we have sudden rise and fall in the irradiation values, hence, we can get a fair evaluation of the FL MPPT controller. The voltage current and power of boost converter is shown in Fig 10. Figure 12. Irradiation and Output power Figure 10. Voltage, Current and Power of Boost Converter The 3D input outputsurface is also obtained by using MATLAB FL Inference as shown in Fig 11. Figure 13. Comparison with existing P and O algorithm Figure 11. The input-output surface waveform of the FLC The obtained power values are definitely equal to the expected maximum power eventhough there is change in irradiance. The response of change in irradiance and power is shown in Fig 12. The obtained power value from fuzzy model is about 150W which meets the required criteria and obtained power value from already existing Perturbation and observation algorithm is about 140W. The comparison of both algorithms is shown in Fig CONCLUSION A simple power electronic controller for interfacing photovoltaic arrays with DC-DC converter has been simulated. By applying the pulse width modulation (PWM) control scheme with Fuzzy based MPPT algorithm to DC- DC converter, it can draw maximum power from photovoltaic array. The fuzzy logic control is an effective tool. Simulation results show that the proposed MPPT can track the MPP faster when compared to P and O algorithm. The proposed MPPT using fuzzy logic can track maximum power point even though irradiance changes. However we can improve the system performance by combining fuzzy and P and O algorithm, by switching between them as irradiation changes. REFERENCES [1] Sathish Kumar Kollimalla, Mahesh Kumar Mishra, Variable Perturbation Size Adaptive POM PPT Algorithm for Sudden ChangesinIrradiance,IEEETrans.SustainableEnergy., vol.5, no.3,jun [2] Sathish Kumar Kollimalla,Mahesh Kumar Mishra, A Novel Adaptive PO MPPT Algorithm Considering Sudden Changes in the Irradiance,IEEE Trans.Sustainable Energy., vol.4,no. 3,2013 Volume: 02 Issue:

5 [3] Kinal Kachhiya, Makarand Lokhande, Mukesh Patel, MAT- LAB/Simulink Model of Solar PV Module and MPPT Algorithm, Proceedings of the National Conference on Recent Trends in Engineering and Technology, [4] S. Lalouni, D. Rekioua, T. Rekioua, and E. Matagne, Fuzzy logic control of stand-alone photovoltaic system with battery storage, J. Power Sources, vol. 193, pp , [5] Michael E. Ropp, Member, IEEE, and Sigifredo Gonzalez, Development of a MATLAB/Simulink Model of a Single- Phase Grid-Connected Photovoltaic System, IEEE Transactions on energy conversion, 2009 [6] C. Zhang and D. Zhao, MPPT with asymmetric fuzzy control for photovoltaic system, in Proc. ICIEA, pp ,2009 [7] I. H. Altasa and A. M. Sharaf, A novel maximum power fuzzy logic controller for photovoltaic solar energy systems, Renewable Energy, vol. 33, pp , [8] T. Esram and P. L. Chapman, Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques, IEEE Trans. on Energy Conversion, vol. 22, no. 2, pp , [9] A. Garrigo, J. M. Blanes, J. A. Carrasco, and J. B. Ejea, Real time estimation of photovoltaic modules characteristics and its application to maximum power point operation, Renewable Energy, vol. 32, pp , [10] T. L. Kottas, Y. S. Boutalis, and A. D. Karlisa, New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks, IEEE Trans. Energy Convers., vol. 21, no. 3, pp , Sep [11] V. Salas, E. Olias, A. Barrado, and A. Lazaro, Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems, Solar Energy Mater. Solar Cells, vol. 90, pp , [12] F. Fernandez-Bernal, L. Rouco, P. Centeno, M. Gonzalez, and M. Alonao, Modelling of photovoltaic plants for power system dynamic studies, in Proc. 5th IEEE Int. Conf. Power Syst. Manage. Control, Apr. 2002, pp [13] Y. C. Kuo, T. J. Liang, and J. F. Chen, Novel MPPT controller for photovoltaic energy conversion system, IEEE Trans. Ind. Electron., vol. 48, no. 3, Jun [14] K. H. Hussein, I. Muta, T. Hoshino, and M. Osakada, Maximum photovoltaic power tracking: An algorithm for rapidly changing atmospheric condition, Proc. Inst. Electr. Eng., Gen. Transmiss. Distrib., vol. 142, no. 1, pp , [15] Z. Salameh and D. Taylor, Step-up maximum power point tracker for photovoltaic arrays, Solar Energy, vol. 44, no. 1, pp , Volume: 02 Issue: