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Procedia Technology 19 (2015) 615 - 622 ^^^^^^^^^^^^^^

8th International Conference Interdisciplinarity in Engineering, INTER-ENG 2014,9-10 October

2014, Tirgu Mures, Romania

Intelligent control of photovoltaic grid-connected using fuzzy logic

based incremental conductance

Mohamed Louzaznia'*, Elhassan Aroudama

a'Modeling & Simulation of Mechanical System Laboratory, Faculty of Sciences, Abdelmalek Essaadi University,93030, Tetouan, Morocco. Abstract

This paper presents an intelligent control of the output power energy from the three-phase grid connected photovoltaic (PV) array, based on fuzzy logic controller and incremental conductance to optimize and track the maximum power point tracking (MPPT) from the PV under varying temperature and irradiance conditions. The control shown better performances and effectiveness compared to Perturb & Observe (P&O). The fuzzy logic control, enhanced by the algorithm can respond quickly to changes in the external environment and make sure the PV is always working at the MPPT and improve the efficiency and the stability of three-phase photovoltaic grid-connected. The intelligent controller improves the incremental conductance search method by fuzzifying, the rules of such techniques and will find exactly the MPPT. Finally, the results verify the correctness and availability of the PV system under various weather conditions.

© 2015 The Authors.PublishedbyElsevierLtd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of "Petru Maior" University of Tirgu Mures, Faculty of Engineering

Keywords: Photovoltaic; MPPT; fuzzy logic controller; P&O; Incremental Conductance.

1. Introduction

Many different techniques and algorithm have been presented in the research to track the MPPT, such as perturb and observe, the hill climbing, the incremental conductance, the constant voltage and the short circuit Ripple correlation control, but they are not able to track the MPPT effectively under rapidly changing conditions [1].

* Corresponding author. Tel.: +212-615-849-647. E-mail address:\oumzm@mm.com

2212-0173 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of "Petru Maior" University of Tirgu Mures, Faculty of Engineering doi: 10.1016/j.protcy.2015.02.087

Nomenclature

PV Photovoltaic

FLC Fuzzy logic controller

IncCon Incremental conductance

MPPT Maximum power point tracking

P&O Perturb & Observe

Vmppt Voltage at MPPT

Imppt Current at MPPT

Voc Open-circuit voltage

Ipv Current of photovoltaic

VPV Voltage of photovoltaic

Ppv Power of Photovoltaic

Isc Short-circuit current

asc Current temperature coefficient

Poc Voltage temperature coefficient

The hill climbing and perturb and observe are based in perturbing the PV changes the duty cycle and observing its impact on output power and deciding the new direction of the duty cycle to extract the maximum power. Many works have used the both techniques in using duty cycle perturbation in P&O, in [2] the comparison between hill climbing and P&O proves that P&O is more efficient under varying weather conditions. In general the both techniques are used the same concept but with different control structures. The incremental conductance is used because of the simplicity in the implementation and high tracking efficiency, it based on the derivative of power voltage. The tracking time is relatively long since the step size is tuned to be small enough to reach the desired MPPT. The constant voltage used the fact that the ration between the maximum power voltage and the open circuit voltage under different weather conditions are linearly proportional [3] and the approximation of constant ration. The fuzzy logic controller is an intelligent for nonlinear control and it has no complex mathematical. This control depends on the membership functions, their distribution, and depends on careful selection of parameters, involves expert knowledge and experimentation in selecting parameters, membership functions and fuzzy rules.

2. Three-Phase Photovoltaic System

The photovoltaic panel connected to a boost converter to enhance and regulate the output voltage; the converter is connected to the inverter. The block diagram of the proposed system is shown in Fig.l.

Fig. 1. The block diagram of the proposed system

2.1. Photovoltaic Model and MPPT submission

The power on the PV depends on solar irradiance, temperature and operating voltage and current. The exponential model Fig.2 will be used to describe and predict the behavior of the photovoltaic panel [4]:

Fig.2. Photovoltaic model

I = I 1 - C eLlVpv "

1PV 1SC -|A

All the three rated points are exactly fitted if the coefficients C1,C2 and m verify:

c, - £

C, = In

Cd = In

he (1 + Ci )-JM

1 + c,

(2) 0)

The irradiance and temperature vary according to the following equations:

MPV = a,r I — ¡AT -1 I /„

ref J y ref

=~ßoc^ - Upr

The new values of the photovoltaic voltage and current are given by

V =V +AV

Y PV,new Y PV ^ L^yPV IpV ,new = IpV + ^PV

The power of photovoltaic

P = I il - C eCÄI F

1 PV 1SC I1

The maximum output power varies with temperature and irradiation, it necessary to use the MPPT techniques to extract the maximum available power at any changes.

3. Incremental Conductance Technique

The incremental conductance based on the output current and voltage of the photovoltaic to calculate the conductance and the incremental conductance. The incremental conductance method is effective to search the MPPT [5] Fig.3. The output power of PV is given as 600

35 40 45

VpV M-LY

' • in

Fig. 3. Operating point according to the sign of dl„ on the power characteristic

d {Vpy -Ip, dVpV

dPpf ' dV„

By defining the photovoltaic conductance and incremental conductance, yields

GG = :

AGG = --

We obtain

_L. .^k = GG -AGG

' py ' py

The equation (11) explains the operating voltage is below the voltage at the maximum power point if the conductance is larger than the incremental conductance and vice versa. The task of this algorithm is to track the voltage operating point which conductance is equal to incremental conductance. Hence

dPPr = 0 Ipy _ dlpr

dlpr v ~ r PV dVpr

dPPr > 0 Ipy ^ V y Py dlpr

dVPV dVpy

dPPr < 0 ^PV ^ dlpy

dVpr V r PV dVpr

GG = AGG

= o The equations (12) are used to determine the direction in which a perturbation must occur to shift the operating point toward the MPPT and the perturbation is repeated until dPpV/dIpV=0. Once, the MPPT is reached and continues to operate at this point until a change in current is measured which will correlate with a change in irradiance on the array Fig.2. The derivative of current can be expressed by

- = -mClC2 IscVpre

4. Fuzzy Logic Controller

The inputs of the fuzzy logic controller are power and voltage varies, the output is referenced voltage variation. If the optimal point is converging, the rules are relatively simple to establish, and depend on the variations of power kPpv and voltage àVPV if the power Ppv increased the operating point should be increased as well and if is decreased the voltage ^pv,ref should the same. In the rules of the MPPT algorithm contains measurement of variation of photovoltaic power APpr and voltage AVpv variation and proposes the variation of the voltage reference &VTY, ref according to equation (14).

'APpv = Pp¥ (k)- Pp¥ (k -1)

<AVPV = Vpr (k)- Vpr (k -1) (i4)

Wrvrt (*) = Vpv (* - l)-AVpr:ref (k)

Where APPV (k) and AV„ (k) are the power and voltage at sampled times {k) , and VPPref (k) the instant of the reference voltage. The Fig.4 shows the track of Ppv {Vpv) for constant irradiance and temperature.

If a great increase in the voltage involves a great increase in the power, we continue to strongly increase the reference voltage, point A to point B or point B to point C. If a great increase in the voltage involves a reduction in the power point C to point D, we decrease the reference voltage to obtain a vast increase in the power [6]. If a reduction in the voltage involves a weak increase in the power then we get closer to the optimal reference voltage which is the beginning of stabilization. When irradiance and temperature vary, the same types of rules are applied to track the MPPT. The inputs can be measured or computed from the voltage and current of photovoltaic array. The membership functions of input and output variables in which membership functions of input variables APpr and AFpr is Gaussian and has seven subsets. The subsets are considered for membership functions of the output variable ^^pv.re/ ■ These input and output variables are expressed as BN=Big Negative, MN=Means Negative, SN=Small Negative, Z=Zero, SP=Small Positive, MP=Means Positive and BP=Big Positive. Totally we have 49 rules; the Table. 1 indicates the contribution of fuzzy control rules associative to appr and avrr as inputs, and avrrflf as the output.

Table 1: Fuzzy rule table

BN MN SN ZE SP MP BP

BN BP BP MP ZE MN BN BN

MN BP MP SP ZE SN MN BN

SN MP SP SP ZE SN SN MN

AVpp ZE BN MN SN ZE SP MP BP

SP MN SN SN ZE SP SP MP

MP BN MN SN ZE SP MP BP

BP BN BN MN ZE MP BP BP

The input APpv and AFpf are configured for 7 degrees, the Fig. 5 and 6 shows the memberships of the inputs and output variables. The output of the fuzzy logic is mentioned at Fig.7.

Fig. 5. Memberships of the input variable

M MN SN ZE SPi i MP 1 BP

-/-V-fl ~ / \ - \ /

—A V V V

i i i i i i i

-80 -60 -40 -20 0 20 40 60 80

DeltaPpv

Fig. 6. Memberships ofthe output variable

DeltaVpv,ref

Fig. 7. Memberships ofthe output variable AVpr,mf

5. Simulation Results

The theoretical analysis of the proposed intelligent control of input power and voltage three-phase grid connected is to be validated and done by simulation using Simulink platform. Firstly, the step response of the three-phase grid-connected photovoltaic using the proposed controller is studied in comparison with the separated fuzzy logic controller with incremental conductance and compared to a classical perturb and observe algorithm. The intelligent controller using fuzzy logic and algorithm is used to improve the dynamic response. Increasing the temperature always involves a decrease in power and the fuzzy-IncCond controller has a response almost perfect continuation algorithm while P&O are late and they present some fluctuations. The both control strategies in MPPT, P&O and IncCond have ripples again the method by fuzzy logic presents better results and without undulations reflecting the non sensitivity to temperature variations. The Fig.8 shows the behaviour of the current and voltage output of the three-phase grid-connected photovoltaic using the P&O algorithm with variation at irradiation and temperature.

The results obtained show the fuzzy logic controller following the deposit with less fluctuation and stabilization while the Fuzzy logic and incremental conductance give good speed performance. As an algorithm P&O the transitional regime is characterized by a distance of MPPT which explains the delay to reach the new value of power. We found that both methods for MPPT control at P&O and incremental conductance are very affected by this variation they exhibit significant differences to achieve the MPPT by cons method by fuzzy logic presents better results and without undulations reflecting the not sensitive to changes in illumination.

Fig.8. (a), Output voltage, (b), output current of three-phase grid-connected using P&O

Fig. 9. (a), Output current; (b) output voltage of three-phase grid-connected using Fuzzy Logic and Incremental Conductance

6. Conclusion

The different results obtained with robustness test confirms the proposed fonctionnement of fuzzy controller with good performance in atmospheric variations of illumination and temperature change, thereby reducing power losses, with better dynamics than conventional numerical methods. The fuzzy logic controller with satisfaction at the sharp variations of temperature, illumination and a fast response time and less than of conventional algorithms P&O and hill climbing. This eliminates the fluctuations in the power, voltage in steady state. The intelligent controllers with fuzzy logic and incremental conductance can provide and order more effective than the traditional controllers for the nonlinear systems, there is more flexibility, a fast and steady fuzzy logic MPPT controller was obtained.

References

[1] Altin N, Özdemir S. Three-phase three-level grid interactive inverter with fuzzy logic based maximum power point tracking controller, Energy Conversion and Management. 2013. p. 17-26.

[2] Masoum MA, Dehbonei H, Funchs EF. Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power pointtracking, Power Eng. Rev., IEEE, 2002; 22(8):62-62.

[3] Esram T, Kimball JW, Krein PT, Chapman PL, Midya P. Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control, Power Electron, IEEE Trans, 2006; 21(5):1282-1291.

[4] Ortiz-Perez E, Maldonado R, O'Neill H, Ortiz-Rivera EI. Proposed System Model and Simulation for Three Phase Induction Motor Operation with Single PV Panel, IEEE Power and Energy Society General Meeting . 2011 .p. 1-6.

[5] Tan CW, Green TC, Hernandez-Aramburo C. Analysis of Perturbation and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Applications, IEEE 2nd International Power and Energy Conference. 2008. p. 237-242.

[6] De Battista H, Mantz RJ. Variable structure control of a photovoltaic energy converter, IEEE Control Theory and Application. 2002; 149(4):303-310.