Fuzzy Logic Based Smart Solar Power MPPT

groggy clay of logical constitution Based tonic solar mightiness MPPTDesign and Optimization of a befuddled Logic Based Smart Solar tycoon uttermost Power headway TrackerA. Kocaba and H.. OkumuKaradeniz Technical University, Trabzon/TurkeyAbstract- As the issue of efficiency is one of the most(prenominal) important fundamentals of solar former geneproportionn, maximum government agency channelise tracking (MPPT) carcasss present an important duty in solar creator management schemes in solar causation gene proportionalityn. This paper includes exact of the radiation diagram and amelioratement of addled logic based solar queen MPPT, and the governing bodyology to achieve the best governing body performance. MPPT system was evaluated at the DC-DC occasion converters exponentfulness of impedance conversion show of view and coope ration of woolly-headed logic theory. The parameters of fuzzy inference system atomic number 18 optimized to come up the faste st and accurate system responses. The performance of the proposed fuzzy logic based MPPT under mingled in operation(p) conditions compared and improvement of this performance is dealt with. The fuzzy logic MPPT suffice realized utilize a buck-boost military unit converter. Computer colors practiced and simulation results also represented. Keywords- maximal Power Point Tracking, addled Logic Control, Solar Power Generation. Solar condition is one of the most harmless and cleanest, plentiful supply of cipher in the world. It is unfailing and like to be our fundamental supply of power in future 1-2. Solar power has already utilise astray for industrial, commercial, residential and military applications. Performance of solar power generation by photovoltaic (PV) cells depends on the environmental conditions such as insolation, sunlight tilt, stretch along var.s, airmass and cell temperature 10. Power converter units should be associated with the PV cells and the consign t o control the power flow from the PV cells to the load. PV systems use MPPT systems to maximize power output, by the ability of continually arranging the duty ratio increment command of the shake offing thingumabob in the power converter unit. Algorithms used in MPPT systems maximize power extractions of PV cells by haughty the duty ratio of the power converter with the changing PV cell output variable quantity combinations like changes in power versus changes in voltage etc. MPPT algorithms such as perturb and observe, incremental conductance have been evaluated until now. 3. Unchanging step size for the control signal, increment of the duty ratio command, used in these methods. Too small steps sizes cause decompress tracking process and too large step sizes cause oscillations almost the maximum power point (MPP).To perform automatically adjusting step sizes, variable step sizing algorithms such as fuzzy logic has been genuine 4-7. Specification of fuzzy logic ascendencys is made according to their skill of simulating gentleman thinking. variant than conventional controllers, fuzzy controllers have the ability to experimental methods and their results to project variable step sizes of control signals without the need of understanding the systems mathematical stupefy 9. Effectiveness of the MPPT algorithm is directly related with the stimulant drug and the output variables that are selected for the system. In general for output variable, duty ratio of the power switch selected. As remark variables power (P) versus voltage (V) slope and changes of the slope, P-V slope and random variable of power, variation of power and variation of voltage, sum of conductance and increment of conductance would be selected 8. In this paper as stimulant drugs of the controller variation of power and variation of voltage, as an output duty ratio of the power switch selected.Sunpower SPR-305E-WHT-D (One series module and one parallel strings) PV plug-in used for simulations in this paper. The characteristics of the PV beautify at 25 C and at various peter levels are shown in find 1. The characteristics of the PV panel at 1000 W/m2 and at various temperature levels are shown in recruit 2. To make the analysis simple, we have worked on resistive load. witness 3 illustrates the racing circuitry of PV panel and resistive load, and authorized (I)-voltage (V) characteristics with an prick 1000 W/m2 and temperature of 25 C. The intersection of the I-V characteristic curve (blue) of the PV panel and load I-V curve (red) is the operating point of the system. From this figure it usher out be observed that the operating point changes with the change of the load value. The maximum power point (MPP) gouge be achieved through proper selection of the load. Maximum power may be extracted from the PV array by incorporating an intelligent apparatus altering the load resistance of the PV array. Power converters are usually used to achieve this purpo se. count on 1. The characteristics of the PV panel at 25 C and at various irradiation levels.Figure 2. The characteristics of the PV panel at 1000 W/m2 and at various temperature levels.Figure 3. PV system with resistive loadFigure 4 shows the block diagram of the investigated MPPT system. The system includes a PV panel, a buck-boost power converter and a fuzzy logic based MPPT controller. The function of controlling power flow from source to the load is carried out by the zeta showcase buck-boost converter as shown in Figure 5. The values of converter circuit elements are Lin = 11 H, L1 = 378 H, CIN = C1 = 1000 F and CPV = 680 F. The pulse width passage (PWM) switching frequency was set to 200 kHz. Internal resistances were ignored to obtain (1) on the converters input and output voltage equation in loaded state (1)If we assume that converter operates lossless with a resistive load RL, the value of the power obtained from this PV system would be (2)It is demonstrated additional ly, in Figure 5, that P-V curves at miscellaneous irradiation levels according (2) and miscellaneous duty ratio commands with the ohmic load 3 . The intersections show the operating points of PV system.In this study fuzzy theory is used to design the MPPT controller. Required fuzzy input variables are generated by fuzzy MPPT controllers by reading voltage and current signals obtained from the PV panel. The fuzzy input variables would then can be used to describe the increment of the duty ratio command for adjusting operating point of the PV panel in order to maximize the power extraction. In Figure 6 the flowchart of the calculation process illustrated. Designs of fuzzy controllers are varies according to the input variables selected. As mentioned before in this paper as input variables, variation of power and variation of voltage of the PV array selected.Figure 4. Solar MPPT system.Figure 5. Power converter and PV power characteristics.Figure 6. Flowchart of calculation process.A. logy MPPT Tracking AlgorithmIn this study fuzzy logic MPPT system used variations of PV cell power output (PPV) and variations of voltage (VPV) as the fuzzy input variables. By victimisation MATLAB Simulink proposed solar power maximum power point tracking system implemented and universe of discourse (UOC) of input and output social station functions determined. After determination of UOD social status functions, they are grouped with the names negative big, negative small, zero, positive small, positive big (NB, NS, ZE, PS, PB). Fuzzy rules database is shown in Figure 7. Iterations made by moving on the P-V slopes specify regions as shown in the figure 7.Figure 7. Fuzzy rules for algorithm using PPV and VPV as the inputs.First of all symmetric rank and file functions used in the simulations as shown in Figure 9 and then asymmetrical membership functions used as shown in Figure 10. The performance difference compared then. For fuzzification Mamdani method and for defuzzific ation centre of gravity method used in this study. Fuzzy embrasure system evaluated by using MATLAB Simulink fuzzy logic toolbox.Figure 8. Fuzzy controller s surface of rule base.Figure 9. (a) social station function for PPV (b) Membership function for VPV (c) Membership function for increment of duty ratio command.Figure 10. Asymmetrical input membership functions (a) Membership function for PPV (b) Membership function for VPV (c) Membership function for increment of duty ratio command.Purposed MPPT system is simulated by using different types of membership functions for comparison and validation. MATLAB/Simulink framework block diagram of the system is shown in Figure 11.Figure 11. MATLAB/Simulink position block diagram of the systemBy using membership functions shown in Figure 9 the simulation results obtained as shown in Figure 12 and by using membership functions shown in Figure 10 the simulation results obtained as shown in Figure 13.Figure 12. Simulation results (symmet rical membership functions used)Figure 13. Simulation results (asymmetrical membership functions used)As shown in figures above when symmetrical membership functions used the fuzzy controlling system was unable to respond cursorily to the rapid changes of irradiation so that fitting time of output power curve was longer and this is a disadvantage of the controlling system that is undesired. Beside at low irradiance levels MPPT controller couldnt produce accurate control signals and PV panels operating point was different than the maximum power point.The performance difference of the designed system when two types of membership functions used respectively can be seen more clearly from Figure 14.Figure 14. Output power of PV panel.Here we can see the disadvantages of using symmetrical membership functions. To overcome this problem and improve the system response speed to the changes of irradiation then asymmetrical membership functions has developed. Another benefit of developed syst em is that, at low irradiation levels precision of MPPT is more higher.In this paper a fuzzy logic based solar maximum power point tracking system was designed and different types of membership functions were used to optimize system power generation performance. To achieve the goal of higher precision and fastest system responses to the changes of irradiation levels different types of membership functions of inputs of fuzzy inference system were researched and compared with from each one other. It was revealed that using asymmetrical membership functions in the fuzzy logic controller had improved system performance at all operating conditions. From the results of simulations it can be inferred that the system performance is directly related with the optimization of the membership functions of fuzzy inference system. This study also leads to the study of the designing methodology of optimization of asymmetrical membership functions for better system performance.References1 planeta ry vigor Agency. Technology Roadmap Solar photovoltaic Energy, IEA Publications Paris, France, 2014.2 K. Tomabechi, Energy Resources in the Future. Energies 2010, pp. 686-6953N. Femia, G. Petrone, G. Spagnuolo, M. Vitellio, Optimization of put out and Observe Maximum Power Point Tracking Method. IEEE Trans. Power Electron. 2005, pp. 963-973.4T. Yong, B. Xia, Z. Xu, W. Sun, circumscribed Asymmetrical Variable Step Size Incremental Conductance Maximum Power Point Tracking Method for Photovoltaic formations. J. Power Electron. 2014, pp. 156-164.5C.S. Chin, P. Neelakantan, H.P. Yoong, K.T.K. Teo, Optimisation of Fuzzy Based Maximum Power Point Tracking in PV System for Rapidly Changing Solar Irradiance. Trans. Sol. Energy Plan. 2011, pp. 130-1376T. Radjai, P.J. Gaubert, L. Rahmani, The new FLC-Variable Incremental Conductance MPPT orchestrate Control Method Using Cuk Converter. In Proceedings of the 2014 IEEE 23rd International Symposium on Industrial Electronics (IEIE), Istanbul, 2014, pp. 2508-2513.7R. Rahmani, M. Seyedmahmoudian, S. Mekhilef, R. Yusof, Implementation of Fuzzy Logic Maximum Power Point Tracking Controller for Photovoltaic System. 2013, pp.209-218.8J-K. Shiau, Y-C. Wei, B-C. C, A Study on the Fuzzy Logic Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables. ISSN 1999-4893, 2015.9T.J. Ross, Fuzzy Logic With Engineering Applications, vol 2, pp. 1-652.10R. Hernanz, C. Martin, Z. Belver, L. Leseka, Z. Guerrero, P. Perez, Modelling of Photovoltaic Module., International Conference on Renewable Energies and Power Quality, 2010.