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

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A fuzzy logic control scheme for a solar photovoltaic system for amaximum power point trackerMd Fahim Ansaria; S. Chatterjib; Atif Iqbalca Graphic Era University, Dehradoon, India b Department of Electrical Engineering, NITTTR,Chandigarh, India c Department of Electrical & Computer Engineering, Texas A&M University atQatar, Doha, Qatar

First published on: 19 April 2010

To cite this Article Ansari, Md Fahim , Chatterji, S. and Iqbal, Atif(2010) 'A fuzzy logic control scheme for a solarphotovoltaic system for a maximum power point tracker', International Journal of Sustainable Energy, 29: 4, 245 255,First published on: 19 April 2010 (iFirst)

To link to this Article: DOI: 10.1080/14786461003802118URL: http://dx.doi.org/10.1080/14786461003802118

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International Journal of Sustainable Energy

Vol. 29, No. 4, December 2010, 245255

A fuzzy logic control scheme for a solar photovoltaic system

for a maximum power point tracker

Md Fahim Ansaria*, S. Chatterjib and Atif Iqbalc

a Graphic Era University, Dehradoon, India; bDepartment of Electrical Engineering, NITTTR,Chandigarh, India; cDepartment 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 tomaximize the produced energy and many of these are well established in the literature. These techniquesvary in many aspects such as simplicity, convergence speed, digital or analogue implementation, sensorsrequired, cost and range of effectiveness. This paper proposes an artificial intelligence-based fuzzy logiccontrol scheme for the MPP tracking of a solar photovoltaic system under variable temperature and insola-tion conditions. The method uses a fuzzy logic controller (FLC) applied to a dcdc converter device. Thedifferent steps of the design of this controller are presented together with its simulation. Simulation resultsare compared with those obtained by the perturbation and observation controller. The results show that theFLC exhibits a much better behaviour.

Keywords: dcdc 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, 13,242.41 MW. As of 31 October 2008, Indias power sector has a total installed

capacity of approximately 146,753 MW of which 54% is coal-based, 25% is hydro, 8% is renew-

able, 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 (www.mnre.gov.in). India lies in sunny regions of the world where most parts receive 4

7 kWh of solar radiation per square metre per day, with 250300 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; http://www.solarindia.com).

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. Email: [email protected]

ISSN 1478-6451 print/ISSN 1478-646X online 2010 Taylor & FrancisDOI: 10.1080/14786461003802118http://www.informaworld.com


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246 F. Ansari et al.

et al. 2010). Photovoltaic power systems are usually integrated with some specific control algo-

rithms 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 CO2. This property has led to worldwide gov-

ernment 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 environmen-

tal 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 possi-ble 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 intelligentcontrol 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 dcdc 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. IV characteristic of a PV module.


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International Journal of Sustainable Energy 247

2. Control of maximum power point tracking

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 Ri , the

optimal adaptation occurs only at one particular operating point, called the maximum power

point (MPP) and noted in our case as Pmax. 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. PV characteristic of a PV module.

Figure 3. Solar photovoltaic system.

Figure 4. Effect of the solar radiation for constant temperature.


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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 dcdc 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) (Mller 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 dcdc converter, which is described by Equations

(1)(3), (Figure 6), is used: (1) i1 = i C1dv/dt, (2) and ib = (1D)i1 C2dvb/dt and v =

(1D)vb + Ldi1/dt.

Figure 6. Basic circuit of the boost converter.


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When switch S is closed for time t1 the inductor current rises and the energy is stored in the

inductor L. If switch S is opened for time t2, 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

VL = LdIL

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.


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and peak-to-peak ripple current in the inductor is

I=Vin

L t1. (2)

A capacitor C is connected across the load to make the output voltage Vout constant, and the output

voltage is given by

Vout = Vin + LI

t2= Vin

1

1D. (3)

Figure 9. Variation of the panel power, battery power, and the duty ratio D, under standard conditions: temperature(25C) and solar insolation (1000W/m2).

Figure 10. Wave shape in steady state of the panel and battery power and of the duty ratio signals for a sampling rateof 100 Hz (T = 25C and S = 1000W/m2).


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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 MPPin PV systems (Takagi andSugeno

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 (5001000 W/m2 in 5 s at 25C).

Figure 12. Fuzzy and P&O controller responses, for a slow (120 s) solar insolation increase (8001000W/m2 at 25C).


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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) =Pph(k) Pph(k 1)

Vph(k) Vph(k 1), (4)

CE(k) = E(k)E(k 1), (5)

where Pph(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 Madanis method, and there are many methods of defuzzification

Figure 13. Fuzzy and P&O controller responses, for a slow (120 s) solar insolation decrease (1000800W/m2 at 25C).

Figure 14. Fuzzy and P&O controller responses, for a fast temperature decrease (4020C) at 1000W/m2 of solarinsolation.


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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 =

nj=1Dj Dj

nj=1Dj . (6)

The two variables such as , membership function and Dj, the universe, may be discretized

into j equal numbers of subintervals by the points D1, D2, D3, . . . , Dn. 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 (2030C) at 1000W/m2 ofsolar insolation.

Figure 16. Fuzzy and P&O controller responses, for a slow (120 s) temperature decrease (3020C) at 1000W/m2 ofsolar insolation.


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Figure 17. Fuzzy and P&O controller responses, for a fast (5 s) temperature increase (2045C) at 1000W/m2 of solarinsolation.

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 dcdc 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 1000W/m2; constant temperature with a rapid and slow decrease in the insolation

from 1000 to 800W/m2; constant insolation with a rapid and slow increase in the temperature

from 20C to 30C, and constant insolation with a rapid and slow decrease in the temperature

from 40C to 20C. 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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MPPT controller was obtained. It makes it possible indeed to find the point of maximum power

in shorter time runs compared with the well-known P&O controller.

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