A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker

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1 International Journal of Sustainable Energy ISSN: (Print) X (Online) Journal homepage: A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker Md Fahim Ansari, S. Chatterji & Atif Iqbal To cite this article: Md Fahim Ansari, S. Chatterji & Atif Iqbal (2010) A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker, International Journal of Sustainable Energy, 29:4, , DOI: / To link to this article: Published online: 19 Apr Submit your article to this journal Article views: 1153 View related articles Citing articles: 11 View citing articles Full Terms & Conditions of access and use can be found at Download by: [ ] Date: 09 January 2017, At: 03:05

2 International Journal of Sustainable Energy Vol. 29, No. 4, December 2010, A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker Md Fahim Ansari a *, S. Chatterji b and Atif Iqbal c a Graphic Era University, Dehradoon, India; b Department of Electrical Engineering, NITTTR, Chandigarh, India; c Department of Electrical & Computer Engineering, Texas A&M University at Qatar, Doha, Qatar (Received 30 December 2009; final version received 22 March 2010 ) Many maximum power point (MPP) tracking techniques for photovoltaic systems have been developed to maximize the produced energy and many of these are well established in the literature. These techniques vary in many aspects such as simplicity, convergence speed, digital or analogue implementation, sensors required, cost and range of effectiveness. This paper proposes an artificial intelligence-based fuzzy logic control scheme for the MPP tracking of a solar photovoltaic system under variable temperature and insolation conditions. The method uses a fuzzy logic controller (FLC) applied to a dc dc converter device. The different steps of the design of this controller are presented together with its simulation. Simulation results are compared with those obtained by the perturbation and observation controller. The results show that the FLC exhibits a much better behaviour. Keywords: dc dc converter; fuzzy logic; photovoltaic system; MPPT 1. Introduction Annual world solar photovoltaic (SPV) production is growing at an almost exponential rate and had reached 1727 MW in 2005 (Xiao et al. 2006). Today the contribution of solar power with an installed capacity of 9.84 MW is a fraction less than 0.1% of the total renewable energy installed, 13, MW. As of 31 October 2008, India s power sector has a total installed capacity of approximately 146,753 MW of which 54% is coal-based, 25% is hydro, 8% is renewable, and the balance is gas and nuclear-based. Power shortages are estimated at about 11% of total energy and 15% of peak capacity requirements and are likely to increase in the coming years ( India lies in sunny regions of the world where most parts receive 4 7 kwh of solar radiation per square metre per day, with sunny days in a year. India has abundant solar resources, as it receives about 3000 h of sunshine every year, equivalent to over 5000 trillion kilowatt hours (Solar India website, 2009; India can easily utilize solar energy or solar power. Solar power generation has lagged behind other sources like wind, small hydropower, biomass, etc., due to its cost (Kolhe 2009, Afzal *Corresponding author. fahim402001@yahoo.co.in ISSN print/issn X online 2010 Taylor & Francis DOI: /

3 246 F. Ansari et al. et al. 2010). Photovoltaic power systems are usually integrated with some specific control algorithms to deliver the maximum possible power (Hussein et al. 1995). Several maximum power point tracking (MPPT) methods that force the operating point to oscillate have been presented in the past few decades such as (1) perturb and observe (Liu and Lopes 2004, Li and Wolfs 2008), (2) incremental conductance (Hohm and Ropp 2000), (3) parasitic capacitance (Hussein et al. 1995), (4) voltage-based peak power tracking (Mohammad et al. 2002, Xiao et al. 2006), and (5) current-based peak power tracking (Mohammad et al. 2002, Noguchi et al. 2002). The algorithm implemented in the present work is perturb and observe with a fuzzy logic controller (FLC) (Hohm and Ropp 2000, Koutroulis et al. 2001, Valenciaga et al. 2001). Photovoltaic (PV) systems produce electricity without producing CO 2. This property has led to worldwide government policies aimed at increasing the deployment of PV systems that are connected with, and can export power to, utility power networks. Due to the energy crisis and environmental issues such as pollution and the global warming effect, PV systems are becoming a very attractive solution (Ansari et al. 2009). Unfortunately the actual energy conversion efficiency of a PV module is rather low. So to overcome this problem and to get the maximum possible efficiency, the design of all the elements of the PV system has to be optimized (Ansari et al. 2008). In order to increase this efficiency, MPPT controllers are used. Such controllers are becoming an essential element in PV systems. A dc/dc converter (step up/step down) serves the purpose of transferring maximum power from the solar PV module to the load (Ansari et al. 2010). A significant number of MPPT control schemes have been elaborated since the 1970s, starting with simple techniques such as voltage and current feedback based MPPT to more improved power feedback-based MPPT such as the perturbation and observation (P&O) technique or the incremental conductance technique (Boehinger 1968, Knopf 1999, Salas et al. 2006). Recently, intelligence-based control schemes MPPT have been introduced. In this paper, an intelligent control technique using fuzzy logic control is associated with an MPPT controller in order to improve energy conversion efficiency. Many MPTT control techniques have been conceived for this purpose over recent decades (Knopf 1999, Salas et al. 2006). They can be classified as voltage feedback based methods, which compare the PV operating voltage with a reference voltage in order to generate the PWM control signal of the dc dc converter (Veerachary et al. 2002) and current feedback-based methods which use the PV module short circuit current as a feedback in order to estimate the optimal current corresponding to the maximum power. Power-based methods are based on iterative algorithms to track continuously the MPP through the current and voltage measurement of the PV module. In this category, one of the most successful and commonly used methods is P&O, which is presented in the next section. Figure 1. I V characteristic of a PV module.

4 2. Control of maximum power point tracking International Journal of Sustainable Energy 247 The photovoltaic module operation depends strongly on the load characteristics (Figures 1 and 2) to which it is connected (Lu et al. 1995). Indeed, for a load with an internal resistance R i, the optimal adaptation occurs only at one particular operating point, called the maximum power point (MPP) and noted in our case as P max. Thus, when a direct connection is carried out between the source and the load (Figure 1), the output of the PV module is seldom maximum and the Figure 2. Figure 3. P V characteristic of a PV module. Solar photovoltaic system. Figure 4. Effect of the solar radiation for constant temperature.

5 248 F. Ansari et al. Figure 5. Effect of temperature for constant insolation. operating point is not optimal. To overcome this problem, it is necessary to add an adaptation device, an MPPT controller with a dc dc converter, between the source and the load (Figure 3) (Boehinger 1968). Furthermore the characteristics of a PV system vary with temperature and insolation, (Figures 4 and 5) (Möller 1993, Gottschalg et al. 1997). So, the MPPT controller is also required to track the new modified MPP in its corresponding curve whenever temperature and/or insolation variation occurs. 3. P&O controller This controller is introduced briefly here (Liu and Lopes 2004,Yu et al. 2004, Santos et al. 2006). The principle of this controller is to provoke perturbation by acting (decrease or increase) on the PWM duty cycle and observing the effect on the output PV power. If the instant power P(k) is greater than the previously computed power P(k 1), then the direction of perturbation is maintained, otherwise it is reversed. Referring to Figure 2 this can be detailed as follows: when dp/dv >0, the voltage is increased; this is done through D(k) = D(k 1) + C (C, incrimination step) when dp/dv <0, the voltage is decreased through D(k) = D(k 1) C. To simulate this P&O algorithm, a boost chopper, such as a dc dc converter, which is described by Equations (1) (3), (Figure 6), is used: (1) i 1 = i C 1 dv/dt, (2) and i b = (1 D)i 1 C 2 dv b /dt and v = (1 D)v b + Ldi 1 /dt. Figure 6. Basic circuit of the boost converter.

6 International Journal of Sustainable Energy 249 When switch S is closed for time t 1 the inductor current rises and the energy is stored in the inductor L. If switch S is opened for time t 2, the energy stored in the inductor is transferred to the load through diode D and the inductor current falls. When switch S is turned on the voltage across the inductor is V L = L di L dt (1) Figure 7. General diagram of a fuzzy controller. Figure 8. Membership functions of (a) Error E, (b) change of error CE and (c) duty ratio D.

7 250 F. Ansari et al. and peak-to-peak ripple current in the inductor is I = V in L t 1. (2) A capacitor C is connected across the load to make the output voltage V out constant, and the output voltage is given by V out = V in + L I t 2 = V in 1 1 D. (3) Figure 9. Variation of the panel power, battery power, and the duty ratio D, under standard conditions: temperature (25 C) and solar insolation (1000 W/m 2 ). Figure 10. Wave shape in steady state of the panel and battery power and of the duty ratio signals for a sampling rate of 100 Hz (T = 25 C and S = 1000 W/m 2 ).

8 International Journal of Sustainable Energy 251 The parameter D indicates the duty cycle of this chopper, which is the closing time of the switch over one period. 4. Fuzzy logic MPPT controller Recently, FLCs have been introduced in the tracking of the MPP in PV systems (Takagi and Sugeno 1985, Passino andyurkovich 1998). They have the advantage of being robust and relatively simple Figure 11. Fuzzy and P&O controller responses, for a fast solar insolation increase ( W/m 2 in 5 s at 25 C). Figure 12. Fuzzy and P&O controller responses, for a slow (120 s) solar insolation increase ( W/m 2 at 25 C).

9 252 F. Ansari et al. to design as they do not require knowledge of the exact model. They do require, on the other hand, complete knowledge of the operation of the PV system by the designer. The proposed fuzzy logic MPPT controller, shown in Figure 7, has two inputs and one output. The two FLC input variables are the error SE and change of error SCE at sampled times k defined by E(k) = P ph(k) P ph (k 1) V ph (k) V ph (k 1), (4) CE(k) = E(k) E(k 1), (5) where P ph (k) is the instant power of the photovoltaic generator. The input E(k) shows whether the load operation point at the instant k is located on the left or on the right of the MPP on the PV characteristic, while the input CE(k) expresses the moving direction of this point. The fuzzy inference is carried out by using Madani s method, and there are many methods of defuzzification Figure 13. Fuzzy and P&O controller responses, for a slow (120 s) solar insolation decrease ( W/m 2 at 25 C). Figure 14. insolation. Fuzzy and P&O controller responses, for a fast temperature decrease (40 20 C) at 1000 W/m 2 of solar

10 International Journal of Sustainable Energy 253 such as the centre of area and centre of gravity method, etc. Equation (6) uses the centre of gravity method for defuzzification to compute the output of this FLC with the duty cycle: D = n j=1 μ D j D j j=1 n μ D. (6) j The two variables such as μ, membership function and D j, the universe, may be discretized into j equal numbers of subintervals by the points D 1, D 2, D 3,...,D n. D is a crisp value, that is a defuzzified value of the duty cycle D. Figure 8(a) is a plot between membership function and error, Figure 8(b) is a plot between membership function and change of error, and Figure 8(c) is a plot between membership function and duty ratio (defuzzified value). Figure 15. Fuzzy and P&O controller responses, for a slow (120 s) temperature increase (20 30 C) at 1000 W/m 2 of solar insolation. Figure 16. Fuzzy and P&O controller responses, for a slow (120 s) temperature decrease (30 20 C) at 1000 W/m 2 of solar insolation.

11 254 F. Ansari et al. Figure 17. insolation. Fuzzy and P&O controller responses, for a fast (5 s) temperature increase (20 45 C) at 1000 W/m 2 of solar In Figure 8, NB is negative big, NS is negative small, ZE is zero, PS is positive small, and PB is positive big. 5. Simulation of P&O with fuzzy logic MPPT controllers and their results Figure 3 shows the functional diagram of the simulated photovoltaic system. The dc dc converter is the boost chopper of Figure 6. The previous MPPT controllers P&O and FLC were simulated under the following tests: constant temperature with a rapid and slow increase in the insolation from 500 to 1000 W/m 2 ; constant temperature with a rapid and slow decrease in the insolation from 1000 to 800 W/m 2 ; constant insolation with a rapid and slow increase in the temperature from 20 Cto30 C, and constant insolation with a rapid and slow decrease in the temperature from 40 Cto20 C. Figures 9 17 show the respective results of these tests. 6. Conclusion Figure 9 shows that there are better performances obtained by the fuzzy controller than the P&O controller with regard to time in reaching steady values of the battery power and panel power. Obviously, it can be deduced that the fuzzy controller is faster than the P&O controller in the transitional state (Figures 11, 13, 14 and 17), and presents also a much smoother signal with fewer fluctuations in the steady state. Figure 10 shows that the wave shape is improved by the fuzzy controller in steady state and panel power, battery power, and control signal get constant value. In this work, the aim was to control the voltage of the solar panel in order to obtain the maximum power possible from a PV generator, whatever the solar insolation and temperature conditions. Since quite a few control schemes had already been used and had shown some defects, it was necessary to find and try some other methods to optimize the output, and the FLC seemed to be a good idea. The controllers by fuzzy logic can provide an order more effective than the traditional controllers for nonlinear systems, because there is more flexibility. A fast and steady fuzzy logic

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