Comparison of Perturb & Observe and Fuzzy Logic in Maximum Power Point Tracker for PV Systems Academic research paper on "Electrical engineering, electronic engineering, information engineering"

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Energy Procedia 50 (2014) 677 - 684

The International Conference on Technologies and Materials for Renewable Energy, Environment

and Sustainability, TMREES14

Comparison of perturb & observe and fuzzy logic in maximum power point tracker for PV systems

H. Bounechbaa, A. Bouzida, K. Nabtib and H. Benallab Laboratory of Electrical Engineering, Constantine 1 University, Constantine, Algeria

Abstract

In this paper, an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and insolation conditions is discussed. The MPPT controller for boost converter based on fuzzy logic (FLC) is developed and compared to conventional tracking algorithm (P&O). The different steps of the design of these controllers are presented together with its simulation. Results of this simulation show that the system with MPPT using fuzzy logic controller increase the efficiency of energy production from PV.

© 2014 ElsevierLtd. This is an openaccessarticleunder the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) Keywords: Maximum power point tracking (MPPT); Photovoltaic system(PV); fuzzy logic (FLC); perturb and observe (P&O);

1. Introduction

Due to the fossil fuel exhaustion and the environmental problems caused by the conventional power generation such as gasoline, coal, etc..., renewable energy sources and among them photovoltaic panels and wind-generators are now widely used [1]. Photovoltaic (PV) energy is one of the most promising renewable energy it is clean, inexhaustible and free to harvest. However, there are two main drawbacks of PV system, namely the high installation cost and the low conversion efficiency of PV modules [2]. Besides that, PV characteristics are non linear and it is very much weather dependent. Fig.1 and Fig.2 show the I-V and P-V characteristics of a typical PV module for a series of temperatures and solar irradiance levels [3, 4]. It can be noticed that PV output voltage greatly governed by temperature while PV output current has approximate linear relationship with solar irradiances. In general, there is a unique point on the I-V or P-V curve, called the Maximum Power Point (MPP), at which the entire PV system (array, converter, etc...) operates with maximum efficiency and produces its maximum output power. However, since the MPP varies with insolation and seasons, it is difficult to maintain MPP operation at all solar insolations without changes in the system parameters. To overcome this problem an intermediate DC-DC converter is proposed. The MPP tracking is applied to PV systems in order to extract maximum available power

1876-6102 © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) doi: 10.1016/j.egypro.2014.06.083

from the PV array at all solar insolations. There are several methods that have been widely implemented to track the MPP. Most control schemes use the P&O technique, which is based on iterative algorithms, because it is easy to implement but the oscillation problem is unavoidable, conductance incremental method requires complex control circuit, other intelligent based control schemes have been introduced (fuzzy logic, neural network) [5].The two last methods have some disadvantages such as high cost and complexity.

In this paper, a model of a PV has been developed using Matlab/Simulink and the model of a DC-DC boost converter in a first time with a P&O controller which oscillations around the MPP in search of the maximum power point. Therefore an intelligent control technique using fuzzy logic control associated with a MPPT controller are used to improve energy conversion efficiency of the photovoltaic system.

Module Voltage [VJ Module Voltage [V]

Fig. 1. I-V and P-V characteristics of a typical PV module for varied Temperatures.

Module Voltage [V] Module Voltage (V]

Fig. 2. I-V and P-V characteristics of a typical PV module for varied solar Irradiances.

2. Photovoltaic system

Photovoltaic cells consist of a silicon P-N junction that when exposed to light releases electrons around a closed electrical circuit. From this premise the circuit equivalent of a PV cell can be modeled through the circuit shown in Fig. 3. Electrons from the cell are excited to higher energy levels when a collision with a photon occurs. These electrons are free to move across the junction and create a current. This is modeled by the light generated current source/pft. The intrinsic P-N junction characteristic is introduced as a diode in the circuit equivalent [6].

The photocurrent Iph generated in the PV cell is proportional to level of solar illumination, / is the output current of photovoltaic cell. , the current Id through the diode varies with the junction voltage and the cell reverse saturation current ls, V is the output of the photovoltaic cell, Rsh and Rs are the parallel and series resistances, respectively. Parallel resistance Rsh is very large while the series resistance Rs is small. When the number of cell in series is Ns and the number of cell in parallel is Np. There are relevant mathematical equations expressing as following:

Fig. 3. Photovoltaic cell equivalent circuit.

I = NpIph-NpIs*[exp{q^}-l] Iph = Iph(Tref)[ 1 + a(r — Tref)]

Iphijref) = hcijref) * TT

„ _ Isc(T2)-Isc(T1) ^ 1

a =-* -

Isc(Ti) T2-T±

/d = /s*[exp{q^}-l])

hc{Tref)

R, = -

Xu — ¡an *

dV 1 dIV0c XV

MVoc{Tref)

exp (-—)

AkTref y V AkTrsf y

The parameters of solar array (MXS60 at 25°C and 1000 W/m2) used are given in Table 1.

Table 1. Parameter specification of MXS 60 PV module.

(1) (2)

(8) (9)

Parameter Variable Value

Maximum power PM 60 [W]

Maximum voltage VM 17. 1 [V]

Current at max power hi 3. 5 [A]

Open cct voltage voc 21. 06 [V]

Short cct current ^ sc 3.74 [A]

Total cells in series Ns 36

Total cells in parallel Nv 1

Nomenclature

Is R«

Output voltage of a PV cell [V]. Output current of a PV cell [A]. Number of modules connected in series. Number of modules connected in parallel. Light generated current in a PV cell [A]. PV cell saturation current [A]. Series resistance of a PV cell [Q].

A Ideality factor.

B Boltzman constant.

T Cell temperature [Kelvin].

q Electron charge.

Tref Reference temperature [Kelvin].

Isc PV cell short-circuit current at 25°c and 1000[w/m2].

A Short-circuit current temperature co-efficient at Isc .

Rsh Shunt resistance of a PV cell [Q].

E PV cell illumination [w/m2].

Is0 Saturation current at Tref[A].

Eg Band gap for silicon [eV]

3. DC - DC Boost Converter

Choppers are static DC-DC converters for generating variable DC voltage source from a fixed voltage source. The DC-DC converter consists of capacitors, inductors and switches. All of these devices in the ideal case do not consume power; this is the reason why the choppers have good yields. The switch is typically a MOSFET transistor which is a semiconductor device in mode (locked-saturated).

For a DC-DC boost converter, by using the averaging concept, the input-output voltage relationship for continuous conduction mode is given by:

X<L = _L_

Vir, 1-D

Where, D is the duty cycle since the duty ratio "D" is between 0 and 1.

The data sheet details of the boost DC/DC converter are given in TABLE 2.

Table 2. Component values of dc to dc boost converter.

Description Rating

L 350[yH]

C 560[|iF]

R 50[n]

4. MPPT Algorithms

There are many MPPT algorithms have been developed and implemented by researchers [7-9]. In general, MPPT techniques can be divided into two categories, namely direct and indirect methods [2]. Direct method of MPPT algorithms is independent from prior knowledge of PV modules characteristics. The MPPT algorithms that include in this category are Perturb and Observe method (P&O), incremental conductance method (INCond), feedback voltage or current, fuzzy logic method and neural network method. Indirect method requires prior evaluation of PV generator; it is based on mathematical relationship obtained from empirical data. Methods like look-up table, open-circuit PV voltage, short circuits PV current and other MPPT algorithms are included in indirect method [2].

4.1. Perturb and Observe (P&O) Method

It is the most widely used algorithm to track the maximum power from the PV, and as the name itself states, it is

based on the perturbation of the system by increasing or decreasing Vref or by acting directly on the duty cycle of the DC-DC converter, and observing the effect on the output power of the panel. If the value of the current power P (k) of the panel is longer than its previous value P(k — 1) then we keep the same direction of disturbance if not reverses the disturbance of the previous cycle.

The P&O method has slow dynamic response, when there is a small increment in the value and low sampling rate is employed. Low increments are necessary to decrease the steady state error because the P&O always makes the operating point oscillate near the MPP. Considering that a low increment is necessary to achieve a satisfactory steady state error, the algorithm speed may be increased with a higher sampling rate. So there is always a compromise between the increment and the sampling rate in the P&O method.

The common problem in P&O algorithms is the array terminal voltage is perturbed every MPPT cycle: therefore when the MPP is reached, the output power oscillates around the maximum, resulting in power loss in the PV system. This is especially true in constant or slow-varying atmospheric conditions.

Thus, the implementation of fuzzy logic is expected to reduce the oscillation of the operating voltage and hence minimize the power loss in the PV system [10].

4.2. Fuzzy logic MPPT

Fuzzy logic was initiated in 1965 by Lotfi A. Zadeh, professor in computer science at the University of California in Berkeley. Fuzzy Logic controller (FLC) works with imprecise inputs, it does not need an accurate mathematical model and it can handle nonlinearity well. Besides, fuzzy is more robust compared to the conventional non-linear controller. The operation of fuzzy logic control can be classified into four basic elements. The four elements are fuzzification, rule base, inference engine and defuzzification.

In this study, the inputs of FLC are error, E and change in error, dE at sample time fc, which are defined by (11) and (12), while the output of FLC is the duty cycle, D [11,12]. The two input variables are described by:

E{k) = VW-V^-D (11)

Ipv(k)-Ipv(k-1)

dE(k) = E(k)-E(k- 1) (12)

Where Ppv(_k)and Ipv(k) are the power and the current of the PV module, respectively.

Fuzzification

Membership function values are assigned to the linguistic variables, using five fuzzy subsets: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). The partition of fuzzy subsets and the shape of membership function, which can adapt shape up to appropriate system, are shown in Fig 4.

Fig. 4 (a). Membership function plots for 'E'.

UembeiihO function i

-I» -SO

tViAvi'Wle'deliE'

Fig. 4(b). Membership function plots for 'deltaE'.

Fig. 4(c). Membership function plots for 'D'.

Inference method

Inference engine mainly consists of fuzzy rule base and fuzzy implication sub blocks. The inputs are now fuzzified are fed to the inference engine and the rule base is then applied. The output fuzzy set are the identified using fuzzy implication method. Here we are using MIN-MAX fuzzy implication method [13].

Table 3. Fuzzy inference table.

E dE NB NS ZE PS PB

NB ZE ZE PB PB PB

NS ZE ZE PS PS PS

ZE PS ZE ZE ZE NS

PS NS NS NS ZE ZE

PB NB NB NB ZE ZE

Defuzzification

For this system is the centre of gravity to compute the output of this FLC which is the duty ratio (cycle). The centre of gravity method is both very simple and very fast method. The centre of gravity defuzzification method in a system of rules by formally given by:

£"=i MK)-(Pj)

Duty ratio, the output of fuzzy logic control uses to control through PWM which generated pulse to control MOSFET switch in DC-DC converter.

The configuration of the two MPPT algorithms perturb & observe (P&O) and fuzzy logic controller (FLC) are presented in Figure 5(a) and Figure 5(b); where the inputs are the courant and the voltage of the PV and the output is the duty cycle of the DC-DC boost converter .

Fig.5. Configurations of MPPT algorithms in Simulink: (a) P&O MPPT algorithm, (b) FLC algorithm.

5. Simulation Results and Discussion

Fig 6. shows the PV power waveforms simulated using the Perturb and Observe (P&O) algorithm and the fuzzy logic control, respectively. The performances of P&O MPPT and fuzzy P&O MPPT have been investigated and compare at 1000[W/m2] and 25 [0C].

0.1 0-2 0.3 0.4 0 5 0.6 0.7 0.8 0.9

0-1 0.2 0.3 04 0-5 0,6 07 08 09 Time [s] Time [s]

(a) (b)

Fig.6. PV power curves generated by (a) Perturb and Observe algorithm and (b) Fuzzy Logic controller.

To highlight the proposed system good performances, the following simulation were presented for many solar irradiance values (500[W/m2], 800[W/m2] and 1000[W/m2]) at fixed temperature of 25[0C].

100C (wTr

C-80C Iw/m'l

G=500(w/tr¿)

0.2 0.3 0.4 05 06 0.7 0.8 0.9 Time [s]

Fig 7. PV power curves generated by (a) Perturb and Observe algorithm and (b) Fuzzy Logic controller at temperature 25[°C] and solar irradiance

change \[kW/m2], 0.8[fcW/m2] and 0.5 [kW/m2].

From Fig. 7, it is noticed that both P&O MPPT and fuzzy logic MPPT can track the maximum power operating voltage point. For practical implementation, the FLC must be selected for its higher performance compared to the P&O controller. Hence the FLC has better performance and closed to the P&O and this is shown in Fig 7. FLC has better response time, less oscillation and much more accurate tracking at each step.

Conclusion

This paper presents P-V and I-V characteristics of MXS60 solar array, the comparison of fuzzy logic MPPT and conventional P&O MPPT have been developed to examine the performance of both controllers. In this work, the aim was to control the duty cycle of the boost converter in order to obtain the maximum power possible from a PV generator, whatever the solar insolation and temperature conditions. Based on the simulation it can be concluded that with the both controllers the PV panel can deliver the maximum power. However, the performance of fuzzy MPPT is better than the traditional controllers for the nonlinear systems, it has the capability of reducing perturbed voltage when MPP has been recognized. This action directly preserves a more stable output power compared to the conventional MPPT where the output power fluctuates around MPP.

References

[1] Eftichios Koutroulis, Kostas Kalaitzakis and Nicholas C. Voulgaris, "Development of a Microcontroller-Based, Photovoltaic Maximum Power Point Tracking Control System," IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 16, NO. 1, JANUARY 2001.

[2] Mei Shan Ngan, Chee Wei Tan, "A Study of Maximum Power Point Tracking Algorithms for Stand-alone Photovoltaic Systems," IEEE applied powet electronics colloquium, 2011.

[3] V. Salas, E. Oli's, A. Barrado and A. La'zaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems," Solar Energy Materials & Solar Cells 90 (2006) 1555-1578.

[4] D. P. Hohm and M. E. Ropp, "Comparative Study of Maximum Power Point Tracking Algorithms," Prog. Photovolt: Res. Appl. 2003; 11:4762 (DOI: 10.1002/pip.459).

[5] S. Lalouni, D. Rekioua, T. Rekioua and E. Matagne, "Fuzzy logic control of stand-alone photovoltaic system with battery storage," Journal of Power Sources 193 (2009) 899-907.

[6] Jiyong Li, and Honghua Wang, "Maximum Power Point Tracking of Photovoltaic Generation Based on the Fuzzy Control Method," IEEE SUPERGEN'09, pp. 1-6, 2009.

[7] Yeong-Chau Kuo, Tsorng-Juu Liang, and Jiann-Fuh Chen, "Novel Maximum-Power-Point-Tracking Controller for Photovoltaic Energy Conversion System," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 48, NO. 3, JUNE 2001.

[8] Yeong-Chau Kuo, Tsorng-Juu Liang, and Jiann-Fuh Chen, "Novel Maximum-Power-Point-Tracking Controller for Photovoltaic Energy Conversion System," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 48, NO. 3, JUNE 2001.

[9] Yinqing Zoua , Youling Yua, Yu Zhangb and Jicheng Luc, "MPPT Control for PV Generation System Based on an Improved Inccond Algorithm," Procedia Engineering 29 (2012) 105-109.

[10] Ahmad Al-Diab, Constantinos Sourkounis, "Variable Step Size P&O MPPT Algorithm for PV Systems," 2010, 12th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2010.

[11] F. Chekired, C. Larbes, D. Rekioua and F. Haddad, "Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit," Energy Procedia 6 (2011) 541-549.

[12] F.Bouchafaa, I.Hamzaoui, A.Hadjammar, "Fuzzy Logic Control for the tracking of maximum power point of a PV system," Energy Procedia 6 (2011) 633-642.

[13] G. Balasubramanian, S. Singaravelu, "Fuzzy logic controller for the maximum power point tracking in photovoltaic system," International Journal of Computer Applications (0975 - 8887) Volume 41- No.12, March 2012.