Maximum Power Point Tracker Using Fuzzy Logic Controller with Reduced Rules

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 9, No. 3, September 2018, pp. 1381~1390
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v9.i3.pp1381-1390  1381
Journal homepage: http://iaescore.com/journals/index.php/IJPEDS
Maximum Power Point Tracker Using Fuzzy Logic Controller
with Reduced Rules
Adel Haddouche1
, Mohammed Kara2
, Lotfi Farah3
1,2
Department of Mine Engineering, University Larbi tebessi Tebessa, Algeria
3
Génie Electromécanique Laboratory Department of Mine Engineering, University Badji Mokhtar Annaba, Algeria
Article Info ABSTRACT
Article history:
Received Feb 10, 2018
Revised Jul 20, 2018
Accepted Aug 6, 2018
This paper presents a fuzzy logic controller for maximum power point
tracking (MPPT) in photovoltaic system with reduced number of rules
instead of conventional 25 rules to make the system lighter which will
improve the tracking speed and reduce the static error, engendering a global
performance improvements. in this work the proposed system use the power
variation and current variation as inputs to simplify the calculation, the
introduced controller is connected to a conventional grid and simulated with
MATLAB/SIMULINK. The simulation results shows a promising indication
to adopt the introduced controller as an a good alternative to traditional
MPPT system for further practical applications.
Keyword:
High efficiency
Maximum power point tracking
PV systems
Reduced fuzzy logic
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Adel Haddouche,
Department of Mine Engineering,
Larbi Tebessi University, Tebessa Algeria
Email: adelhaddouche@yahoo.fr
1. INTRODUCTION
During the last years, urgent needs for a new energy alternative in order to overcome the energy
crisis and global warming issues. Those problems have significantly promoted the development of renewable
energies. In this context, the photovoltaic systems represent a very competitive solution. Unfortunately, this
solution is not perfect due to low energy conversion efficiency, and to overcome this problem it's necessary
to provide the PV system with an MPPT controller to gather the maximum electrical power from the
photovoltaic modules under different working conditions. Therefore, Many methods of MPPT were
completed in previous studies, as perturb and observe (P&O) [1], fractional open-circuit voltage [2],
fractional short-circuit current, incremental [2] conductance (IncCon), line approximation, ripple correlation
control (RCC), PID control, fuzzy logic control (FLC)[1], genetic algorithm[3], neural network and neuro-
fuzzy approaches [4]. On the other hand, intelligent systems like FLC, neural network, and neuro-fuzzy
systems are able to determine their parameters and are capable of operating under the highly nonlinear
system. As a result, the FLC-based MPPT algorithm attracts many research interests. Recently in literature,
numerous MPPT techniques based on these techniques have been proposed [1-6]. In comparison with the
P&O algorithm, they provide superior tracking performance. However, the design consideration and
realization complexity for different kinds of intelligent based MPPT techniques vary widely.
Regarding the design of input/output membership functions (MFs), it is known that the input/output
MFs design has a great impact on FLCs’ performance in terms of tracking time. In order to deal with this
issue, genetic algorithm (GA)[7], particle swarm optimization (PSO)[8] and artificial neural network
(ANN)[9] are proposed in the literature to optimize the FLC MFs. In this work, we propose an MPPT based
on fuzzy logic with a reduced number of rules instead of the traditional high number of rules. The goal of this
reduction is to make the whole system lighter and more reactive in order to improve global performance

 ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 9, No. 3, September 2018 : 1381 – 1389
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especially tracking time and to offer a low cost, high efficiency FLC-based MPPT algorithm, with power
variation (ΔPpv) and output current variation (ΔIpv) as the inputs of the proposed FLC. The design of the
FLC scheme and rule table with results will be introduced later in this paper.
2. MAXIMUM PERFORMANCE POINT TRACKER OPERATION PRINCIPLE
The connected load characteristics have an important influence on the photovoltaic operating
behavior as shown in Figure 1, [10, 11]. Indeed, for a load, with an internal resistance Ri, the optimal
adaptation occurs only at a particular operating point, referred to as the maximum power point (MPP) and
noted in our case P max, as shown in Figure 2. Thus, when a direct connection is made between the source
and the load, the output of the PV module is rarely maximum and the operating point is not optimal. To
remedy this problem, it is necessary to add an MPPT controller with a DC-DC converter, between the source
and the load, as shown in Figure 3 [12]. the characteristics of a PV system vary with temperature and
irradiance, as shown in Figure 4, and 5 [13,14]. Therefore, an MPPT controller is also required to track the
new modified maximum power point in its corresponding curve whenever a variation in temperature and/or
irradiance occurs many MPPT control techniques have been conceived for this purpose in the last decades
[15, 16]. They can be classified as:
1. Voltage feedback based methods, which compare the PV operating
2. Voltage with a reference voltage in order to generate the PWM control signal to be applied to the DC-
DC converter [17].
3. Current feedback based methods that use the PV module
4. Short circuit current as a feedback in order to estimate the optimal current corresponding to the
maximum
Figure 1.Current–voltage characteristic of a PV module
Figure 2. P-v characteristic of a PV module Figure 3. Photovoltaic system

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Maximum Power Point Tracker Using Fuzzy Logic Controller with Reduced Rules (Adel Haddouche)
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Figure. 4. Influence of the solar radiation for constant temperature
Figure 5.The boost DC-DC converter circuit
3. DC-DC CONVERTER
In order to always ensure the operation point on the maximum power point, or close to it, specific
circuit, called Maximum Power Point Tracker (MPPT), is employed. Usually, the MPPT is achieved by
interposing a power converter (DC-DC converter) between the PV generator and the load (battery), thus,
acting on the converter duty cycle (D) it is possible to guarantee the operation point as being the MPP [18],
Figure 4 shows the circuit of the buck converter, whose output voltage (Vb) is less than or equal to the input
voltage Vi (PV generator voltage).
A boost converter (step-up converter) is a DC-to-DC power converter that steps up voltage (while
stepping down current) from its input (supply) to its output (load). It is a class of switched-mode power
supply (SMPS) containing at least two semiconductors (a diode and a transistor) and at least one energy
storage element: a capacitor, inductor, or the two in combination. To reduce voltage ripple, filters made of
capacitors (sometimes in combination with inductors) are normally added to such a converter's output (load-
side filter) and input (supply-side filter). [20]. the boost converter can come from any suitable DC sources,
such as batteries, solar panels, rectifiers and DC generators. A process that changes one DC voltage to a
different DC voltage is called DC-to-DC conversion. A boost converter is a DC-to-DC converter with an
output voltage greater than the source voltage. A boost converter is sometimes called a step-up converter
since it "steps up" the source voltage. Since power (P=VI) must be conserved, the output current is lower
than the source current. The dynamic model of the used boost converter as shown in Figure 4.
4. FUZZY LOGIC MPPT CONTROLLER
Fuzzy logic controllers have been introduced in the tracking of the MPP in PV systems [12–14].
Due to their advantages and being robust and relatively simple to design since the knowledge of the exact
model is not required. On the other hand, the designer needs complete knowledge of the PV system
operation.

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In this paper, the inputs of the proposed MPPT controller are the power variation (ΔPpv) and the
current variation (ΔIpv). The MFs of the utilized input and output variables for the proposed controller are
illustrated in Figure 6 shows the MFs of the input variables; both ΔPpv and ΔIpv MFs are in triangular form.
Fig.7 is the MF of the output (duty cycle step size D), which is also in triangular form. In (Fig.7), DP stands
for power variation, DI represents current variation and D denotes duty cycle variation. For linguistic
variables, P represents positive, N represents negative, B, S and Z are defined as big, small and zero,
respectively. From Figure 6, each of the input variables ΔPpv and ΔIpv is mapped into five different
linguistic values. Instead usual proposed fuzzy system our model proposes a limited number of rules the
fuzzy inference is carried out by using Mamdani’s method, (Table 1), and the defuzzification uses the centre
of gravity to compute the output of this FLC, which is the duty cycle, the control rules are indicated in Table
1. Figure 8 shows the structure of the fuzzy controller.
Figure 6. Membership functions for inputs (input1 DPv) (input2 DIv)
Figure 7. Membership function for output D (duty cycle)

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Figure 8. The structure of the fuzzy controller
5. IMPLEMENTATION AND RESULTS
5.1. IMPLEMENTATION
The proposed FLC has been implemented and tested using SIMULINK (MATLAB) to a100-kW
Grid-Connected PV Array as shown in Figure 11 (Detailed Model). A step change in solar radiation applied
to assess the robustness of the proposed controller. Irradiation pattern is shown in Figure 9, for the PV model
we chose the SUNPOWER SPR-305-WHT as shown in Figure 10.
Figure 9. Irradiation pattern
Figure 10. Array proprieties

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Figure 11. 100-kW grid-connected PV array
5.2. RESULTS
The simulation results of the PV generator output power as shown in Figure 12, operating voltage as
shown in Figure 13, operating current as shown in Figure 14, and the duty ratio as shown in Figure 15 and
grid voltage (Figure 16 using a boost converter under standard test conditions has shown that the proposed
FLC controller shows better static error (1.62 kW so 1.62/100.71= 0.016%) and less Tracking time error
(less than 0.005s) comparing to conventional MPPT methods seen in previous research as shown in Table 2
[21].
Figure 12. Generator output power

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Figure 13. Operating voltage
Figure 14. Grid currents
Figure 16. Grid voltage
Table 2. Comparison of Results
FLC P&O FLC with reduced rules
Tracking time (Sec) 0.018s 0.015 0.005s
Static error (%) 0.25% 0.74% 0.016%

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6. CONCLUSION
This paper presents a different control strategy of MPPT for the PV system using the FLC with a
reduced number of rules. Simulation results show that the proposed fuzzy can track the MPP faster when
compared to the conventional FLC. In conclusion, the proposed MPPT using fuzzy logic with 8 rules can
improve the performance of the system and had a better response than a conventional controller in terms of
the maximum power tracking time and static error.
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BIOGRAPHIES OF AUTHORS
Haddouche Adel born in Moscow on 23 January 1986. he received his master degree in robotics
and industrial computing from University of badji Mokhtar Annaba Algeria in 2012, actually he
is preparing his thesis in order to obtain a Ph.D. degree in university Larbi tebessi Tebessa
Algeria. His current research interests include AI, Fuzzy System, Genetic algorithm, Neural
Network, PSO and Photovoltaic modeling and control, energy conversion and power electronics.
He has authored and co-authored different seminar papers.
Kara Mohammed born in Oued Zenati on 20 November 1959.he received his Ph.D. degree in
electromechanical science from University of Badji Mokhtar Annaba Algeria in 2007. since
1989 he held lecturing positions at The Larbi Tebessi university. In 2007 he is graduated to
Senior Lecturer at the same university, he held administrative positions within the university
such as Head of Department (mines department ) from 2009 to 2011 and Vice-Rector of Higher
Education, Continuing Education and Diplomas from 2011 to 2016, nowadays he is a Vice-
rector of higher education, first and second cycle of continuing education, diplomas and higher
education in graduation. His current research interests include maintenance and industrial safety,
Applied Automation And Industrial Diagnostic. Dr Kara authored and co-authored different
seminar papers.
Farah Lotfi received the B.Eng. and Ph.D. degrees in Arabic hand written recognition from the
University of BadjiMokhtar Annaba, Algeria, in 1995 and 2000, respectively. From 2000 to
2012, he was a Research Associate with the University of Cherif Messadia, Algeria. He is
currently a Research with BadjiMokhtar University in GénieElectromécanique Laboratory,
Annaba, Algeria. His current research interests include AI, Fuzzy System, Neural Network and
Photovoltaic modeling and control, energy conversion and power electronics. He has authored
and co-authored different seminar papers. Dr. Lotfi serves as a Reviewer for international
journals in his research field, AI such as Journal of Computer ScienceUSA

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