Fuzzy Logic based Maximum Power Point Tracking for Photovoltaic system Mrs.P.S.Gotekar 1 , Dr.S.P.Muley 2 Asst Prof F.I.E, Professor Electrical Department Priyadarshini College of Engineering [email protected][email protected]Dr. D. P. Kothari 3 F.I.E.E.E, Dean (Research), J.D.College of Engineering, Nagpur, India Abstract A novel modeling technique is presented in this paper for maximum power point tracking using fuzzy logic of PV system. The main focus of this paper is to simplify modeling of single phase PV system . Then a fuzzy based maximum power point has been implemented which helps to track power efficiently. Keywords—Fuzzy Logic, MPPT, PV model, MATLAB/SIMULINK I. INTRODUCTION In India the application of solar energy is increasing rapidly. As of September, 2017 the country's solar grid had a cumulative capacity of 16.20 GW[1-2]. India quadrupled its solar-generation capacity from 2,650 MW on 26 May 2014 to 12,289 MW on 31 March 2017. 3.01 GW of solar capacity was added in 2015-2016 and 5.525 GW in 2016- 2017, the highest of any year, with the average current price of solar electricity dropping to 18% below the average price of its coal-fired counterpart [3-5]. The characteristic I-V is a non-linear equation with multiple parameters. Sometimes, researchers develop simplified methods where, some unknown parameters cannot be calculated. They are assumed to be constant. For example, in [6] the series resistance Rs was included, but not the parallel resistance for a model of moderate complexity. In some literature these two have been identified more accurately like in [7-10]. The main objective of this paper is to focus on the PV module or array as one device in a complex ‘‘electro- energetic system’’ where apart from series and parallel resistances, ideality factor, photocurrent, diode current have also been considered. So, the goal is to obtain at any time, the maximum power more precisely , which is the closest to the experimental value. II. MODELLING OF PV CELL A solar cell is the electronic device which converts sunlight into electricity. Combination of PV cells connected in series and parallel forms a PV module. A voltage of PV module is selected such that it is compatible with the voltage of the battery. Single diode model or a two diode model may be used to represent a PV cell. For simplicity single diode model has been selected in this paper. A. Design of PV Cell The five parameters on which the solar irradiation and cell temperature depends are Io, Rs, Rp, Iph, and ideality factor (refer Table1). Figure 1 shows practical model of the PV cell. This model is also called as five parameter model (Io, ideality factor, Rs, Rp, Iph) and has following properties: * Rs is introduced in series to consider voltage drop and internal losses due to the flow of current. * Rp is introduced to consider leakage current to ground when the diode is reverse biased. B. Mathematical equations To predict the behavior of the solar cell under varying atmospheric conditions mathematical modeling is required. The key factor that affects the accuracy of the simulation is the accurate representation of nonlinear characteristics of the PV system modeling . PV saturation current is given by, (1) 1] ) T * K * A * Ns Rs * Ipv Vpv * q [exp( * Io * Np Iph * Np Ipv P S Gotekar et al, International Journal of Computer Technology & Applications,Vol 8(6),653-657 IJCTA | Nov-Dec 2017 Available [email protected]653 ISSN:2229-6093
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Fuzzy Logic based Maximum Power Point Tracking for ... · PDF fileIV. Results The complete system includes collecting data pertaining the irradiation. Designing a PV module, and fuzzy
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Fuzzy Logic based Maximum Power Point Tracking for Photovoltaic system
Mrs.P.S.Gotekar1, Dr.S.P.Muley
2
Asst Prof F.I.E, Professor
Electrical Department Priyadarshini College of Engineering
To implement the equations (1-5), different matlab functions have been used. Different blocks represent different mathematical equations for Iph, Ipv, Io and Co. Input parameters chosen are Np, Ns, temperature, while output parameters are output power of PV module, current and voltage of PV module. Figure 2 represents matlab simulink model of PV module .
III. MPPT System
As the direction of the Sun changes there is change in
insolation level therefore the output power of PV module
also changes. The peak value of the product of voltage and
current represents the maximum power point Pmax of the
solar module. The solar module should always be operated
in this region so as to extract the maximum power for a
given input conditions. For this purpose various power
point algorithms are used. It is desirable that PV module
operates near maximum power point. Many MPPT
algorithms had been proposed in the past. Most commonly
used algorithms are Perturb and Observe, Incremental
conductance method, neural network and fuzzy logic
method. The input parameters are chosen as : input current and
voltage of PV module. The output will be the duty cycle.
A. Fuzzy Logic MPPT Controller
Figure 3 represents block diagram of Fuzzy Logic Based MPPT .From Fig 4 the basic concept of fuzzy algorithm is clear. The equations associated with calculation of error and change of error have been given below.
E(k) =ΔP
ΔI (6)
𝐶E k = E k − E(k − 1) (7)
ΔI = I k − I(k − 1) (8)
ΔV = V k − V(k − 1) (9)
ΔP = P k − P(k − 1) (10)
For the input variables, error and change of error and duty
cycle, triangular membership functions have been chosen as
shown in fig 5 and fig 6.
Rules for MPPT for the fuzzy logic controller have
different subsets. In all forty nine rules are written to
operate the fuzzy logic controller. For the improvement in
accuracy of the system forty nine rules are written.
(2)
T
1-
Tr
1Ak
Eg*qexp
3
Tr
T*IrsIo
Input u1
Input u2
Decision
Making
Logic
KNOWLEDGE
BASE
Fuzzyfication
Interface
Inference
Engine
Defuzzyfication
Fig 4. Fuzzy Logic algorithm
0.1 0.4 0.7
NB NM NS ZE PS PM PB
Fig 5. Membership function for error
-6 0-3 3 6
N B N M N S Z E PS PM PB
Fig 6.Membership function for change of error
Input
current and
voltage of
PV
Calculation
of error and
change of
error signal
Fuzzification
(Membership
Functions)
Decision
making
Logic
Defuzzification
Calculation
of Duty
cycle
Fig 3. Block diagram of fuzzy logic controller.
P S Gotekar et al, International Journal of Computer Technology & Applications,Vol 8(6),653-657
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[10] Ali Algaddafi, Saud A. Altuwayjiri, Oday A. Ahmed, and Ibrahim Daho,"An Optimal Current Controller Design for a Grid Connected Inverter to Improve Power Quality and Test Commercial PV Inverters" Hindawi, The Scientific World Journal,Volume 2017,
[11]Majid Jamil,M.Rizwan,D.P.Kothari, "Grid Integrationof Solar Photovoltaic systems" CRC Press, New York 2017
[12] D.P.Kothari, K.C.Singhal and Rakesh Ranjan, " Renewable Energy Sources and Emerging Technologies" PHI Learning Pvt Ltd.
Fig.2. Simulink model of PV module
P S Gotekar et al, International Journal of Computer Technology & Applications,Vol 8(6),653-657