Simulation and Implementation of Perturb and observe Fuzzy based

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 3761-3767

© Research India Publications. http://www.ripublication.com

3761

Simulation and Implementation of Perturb and observe Fuzzy

based DC-DC Converter in PV-Battery Hybrid System

Lakshman Rao S. Paragond

Assistant Professor, Department of Electrical & Electronics Engineering, Manipal Institute of Technology (MIT), Manipal, Karnataka, India.

Dr. Ciji Pearl Kurian SMIEE

Professor, Department of Electrical & Electronics Engineering, Manipal Institute of Technology (MIT), Manipal, Karnataka, India.

Dr. B. K. Singh Professor, Department of Electrical & Electronics Engineering,

Manipal Institute of Technology (MIT), Manipal, Karnataka, India.

Mr. Aswanth V

PG Student, Department of Electrical & Electronics Engineering, Manipal Institute of Technology (MIT), Manipal, Karnataka, India.

Abstract

This paper explains Design, simulation and implementation of

Perturb and Observe (P&O) Fuzzy Based control of DC-DC

Converter in Photo-Voltaic (PV) System. A standalone PV

system connected to a Boost converter with a resistive load

and Maximum Power Point Tracking (MPPT) is modeled and

implemented. A battery is used for storage purpose. The

simulations were done in MATLAB/SIMULINK

Environment. A prototype of BOOST converter with

conventional P&O Method is developed. Performance

Analysis of P&O and Fuzzy based MPPT is done.

Keywords: PV, MPPT, Perturb and Observe (P&O), Fuzzy

logic Control, Boost converter.

Introduction Fossil fuels like petrol, diesel, coal etc. are the main sources of

world energy at present. The exhaust nature of the fossil fuels

and their adverse effect on nature are the primary concern.

World is depending more and more on renewable energy

sources due to their abundant availability less impact factor on

environment pollution. The Availability of solar energy all

over the world and its setup cost compared to other types of

renewable energies makes it attractive. Resources are focusing

on low cost models for the extraction of solar energy. The total

power demand of entire world is estimated to be 30 Giga tons

of oil equivalent by 2050. To avoid energy crisis, the solar

energy 460wm-2 which is available for conversion is to be

utilized to the maximum.Even 1% utilization of the total solar

energy available can greatly reduce the energy crises[1]

The solar panel output depends on the light falling on the

panels, time of the day, panel position and orientation. If these

factors are kept constant, the power output depends on the load

connected [2]. (ICT), Fuzzy logic Control (FLC), Perturb and

observation method (P&O), Hill Climbing method, neural

network based MPPT, Linear current control based MPPT,

Temperature based MPPT, Array reconfiguration based

MPPT.

Among these theperturb and observation technique and fuzzy

logic control are employed in our simulation model such that a

resistive load of about 90Ω is connected, its output voltage is

compared and checked with the hardware output. The lead acid

battery is also simulated and its appropriate % State of Charge

(SOC), voltage, current is provided for actual reference.

In the proposed PV-Battery hybrid systems the stand alone PV

panel is connected to an Energy storage system or lead acid

battery such that the excess energy is stored and can be used as

backup during black out conditions or line fault conditions.

System Configuration The basic block diagram of PV-Battery system connected to a

load through BOOST converter implementing an MPPT

algorithm as shown in Figure 1.The system is designed to

operate in MPPT mode transferring maximum power

available from the PV panel and transferring it to the load

under all operating conditions. The 36 solar cells are

connected in series and the PV panel output is 20 watts.The

function of the BOOST

mailto:[email protected]



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Figure 1: General Block Diagram

To improve the efficiency of the PV modules a MPPT

technique is proposed. MPPT it is meant to draw maximum

power from PV panels irrespective of temperature, irradiance

and load conditions. Many MPPT algorithms are available and

some of them are mentioned such as Incremental conductance

algorithm converter is to match the internal resistance of the

PV and to that of the load such that a suitable PWM output is

generated from the MPPT algorithm to drive the BOOST

converter and such that the excess energy is stored in the lead

acid battery.

Modelling of PV System Diode model of a PV cell

Figure 2. Shows the diode model of Pv system based on general

equations (3) and the corresponding Simulink model is given

figure 3.

Figure 2: Diode model of PV System

The equations related to two diode modeling of PV cell are

explained [3].The output current of the PV cells given by

equation (1)-(4)

𝐼𝑑1 = 𝐼01 [exp (𝑉+𝐼𝑅𝑠

𝛼1𝑉𝑇1) − 1] (1)

𝐼𝑑2 = 𝐼02[exp(𝑉+𝐼𝑅𝑠

𝛼2𝑉𝑇2)-1] (2)

For the diode D1 and D2 reverse saturation currents are Io1 and

I02 respective The thermal voltages of diodes are 𝑉𝑇1and

𝑉𝑇2. 𝐼𝐷1 is diode current due to diffusion loses in PV cell and

𝐼𝐷2 corresponds to diode current due to recombination loses in

PV cell.𝑅𝑠is series resistance due to material contacts and

other miscellaneous loses.𝑅𝑝is the parallel Resistance due to

leakage current to ground.

The Photo-current 𝐼𝑃𝐻 is given by

𝐼𝑃𝐻 = (𝐼𝑃𝑉𝑆𝑇𝐶 +𝐾1∆𝑇)𝐺

𝐺𝑆𝑇𝐶 (3)

𝐼𝑃𝑉𝑠𝑡𝑐 is the light generated current at STC. ∆T=T-𝑇𝑆𝑇𝐶

(In Kelvin, 𝑇𝑆𝑇𝐶=25C, G is the solar irradiance on the surface

of the PV cell in 𝑊

𝑚2 and 𝐺𝑆𝑇𝐶 irradiance at STC (1000𝑊

𝑚2).The

constant K1 is the short circuit current coefficient in 𝑚𝐴

𝐾 which

is provided by the manufacturer. The diode saturation current

with variation in temperature is given by

𝐼𝑜 = (𝐼𝑆𝐶,𝑆𝑇𝐶 + ∆Tk1)/exp[𝑉𝑂𝐶𝑆𝑇𝐶 +𝐾𝑣∆𝑇

𝛼𝑉𝑇] − 1

(4)

𝐾𝑣 Is the open circuit voltage coefficient in 𝑚𝑉𝐾 provided in

the data sheet.𝑉𝑜𝑐𝑆𝑇𝐶 is the open circuit voltage of PV cell at

STC.𝐼𝑆𝑇𝐶 is the short circuit current of PV cell at STC.

Figure 3: Two diode Simulink model of PV System.

Perturb and Observation (MPPT)

Inputs given to the P&O MPPT algorithm are the output

currents and output voltage from the PV panel which also

happens to be the input current and input voltage to the DC-

DC Boost converter. In this conventional Perturb and

Observation Technique shown in Figure 4 contains power

which is the product of voltage and current of the converter,

every increment or decrement of the power in the Power-

Voltage curve is tracked until maximum power point is

obtained. For instance the current value of power is compared

with the previous value and the value of 𝑉𝑟𝑒𝑓 is incremented or

decremented until the entire loop is returned to the beginning

when the Power is checked once again from its previous value.

Figure 4: Flowchart of P&O MPPT algorithm.



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Fuzzylogic control.

The three basic components of a typical FLC are fuzzification

module, defuzzification module, inference engine The Figure

5 as shown the basic flowchart of FLC.

𝑃(𝑘) = 𝑉(𝑘) ∗ 𝑖(𝑘) (5)

𝐸(𝑘) =𝑃(𝑘)−𝑃(𝑘−1)

𝑉(𝑘)−𝑉(𝑘−1)=

∆𝑝

∆𝑣 (6)

∆𝐸(𝑘) = 𝐸(𝑘) − 1 (7)

Figure 5 explains the concept of the control system. The

objective of the system to push the operating point towards the

point 𝑃𝑚𝑎𝑥 using controller. The advantage of the fuzzy control

is that it is robust, fast and responds instantaneously to

atmospheric changes. The inputs to the fuzzy logic system will

be error (E) and change in error(C).The output will be the

change in Duty cycle (∆d) at sampling instant (k) [3].

Every iteration the duty cycle is updated as per the fuzzy rule

base. Then the next sample of voltage and current is the fuzzy

variables are divided into 5 linguistic hedges: Negative Big

(NB), Negative Small (NS), and Zero (ZE), Positive small

(PS), Positive Big (PB).The membership functions are shown

in Figure 6.

Table 1 shows the fuzzy rule base. The output for each fuzzy

and input is tabulated in the form of a rule base the it uses

Mamdani implication. Now the load requires non fuzzy value

of control, a defuzzification stage is needed, by using the height

method Defuzzification is done

The fuzzy logic is shown to be more stable and reliable than

the perturb and observation method. The results output show

that the fuzzy output is more linear than P&O MPPT.The

results are shown in the Simulation results and validation

section.

Figure 5: Flowchart of Fuzzy logic controller

Figure 6: Membership functions of change in error

Table 1: Fuzzy rule Base

Boost converter design

The Boost converter is designed based on the general

equations in MATLAB/Simulink environment, duty cycle of

the boost converter is changing according to MPPT

𝑉𝑜

𝑉𝑠=

1

1−𝐷 (8)

Simulation Results and Validations This section consists of the simulation and hardware results of

the PV system. In Figure 7. ‘a’ ‘b’ and ‘c’ are shown the PV

output voltage (20V), current and power. In Figure 8. shows

the boost converter output voltage. The output voltage is

fluctuating between 38 to 40 volts. The output power of the

boost converter is varying between 16 watts to 18 watts,

getting these results by using perturb and observed method of

MPPT.



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Figure 7a: PV panel output Voltage

b). PV panel output current

c). Output Power of PV panel

Figure 8a: Boost converter output Voltage

b). Boost converter output power

The Figure. 9a, 9b, and 9c as shown the output voltage, current

and power of the PV panel. The current is fluctuating between

0.6 Amp to 0.8 Amp and corresponding power is 15 to 18

watts. The Figure 10a, 10b and 10c as shown the output

voltage (38V), current (0.42A) and power(16 Watts) of the

boost convert based on the fuzzy based MPPT

Figure 9a: Output Voltage of PV panel

b). Output Current of PV panel

c). Output Power of PV panel



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Figure 10.a: Boost converter output voltage

b). Boost convert output current

c). Boost converter output Power

System connected to battery through MPPT

Simulation of a standalone PV system connected to a lead acid

battery operating in maximum power point is carried out in

SIMULINK/MATLAB environment. The converter operates

in MPPT mode and charges the battery completely. In the

simulation the DC bus is connected to the battery to measure

the battery SOC, voltage and current. The output waveform of

the lead acid battery is shown in Figure 11. The negative

current depicted in the current waveform shows that the

battery is charging under STC i.e at constant temperature of

25°C and irradiation of 1000W/𝑚2.

( a)

(b)

(c)

Figure 11.a: Battery SOC, b) Voltage and c) current when

completely charged

( a)

( b)

Figure 12a: PV output Voltage, b) current of battery during

charging

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 159.9995

60

60.0005

60.001

60.0015

60.002

Time (seconds)

<SO

C (%

)>

Time Series Plot:<SOC (%)>

0 0.02 0.04 0.06 0.08 0.1 0.12

33.5

34

34.5

35

35.5

36

36.5

37

37.5

38

Time (seconds)

<V

olta

ge (

V)>

Time Series Plot:<Voltage (V)>

0 0.02 0.04 0.06 0.08 0.1 0.12

-300

-200

-100

0

100

200

300

Time (seconds)

<C

urre

nt (

A)>

Time Series Plot:<Current (A)>

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

5

10

15

20

25

Time (seconds)

Voltage

Time Series Plot:

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.2

0.4

0.6

0.8

1

1.2

1.4

Time (seconds)

Curre

nt

Time Series Plot:



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Hardware Prototype:

Figure 13: Hardware setup for MPPT with Boost converter

Figure 14: Duty cycle as during P&O MPPT operation

Figure 15: Output Voltage curve

Figure 16: Output Power curve

Table 2: Hardware results with resistive load (80Ω)

TIME Output

Voltage(Volt)

Resistive

Load(Ohm)

Power(watts)

10AM 15 80 2.81

11AM 20 80 5

12AM 24 80 7.2

1PM 28 80 9.8

2PM 32.4 80 13.1

3PM 27 80 9.11

4PM 18 80 4.05

5PM 14 80 2.45

In boost converter the inductor is designed for 0.5mH and

MOSFET IRFZ44 is used as switch. By using voltage resistive

divider circuit and Hall Effect current sensor to sense the

output voltage and current of the PV panel, Here 20watts PV

panel is used. In hardware design the perturb and observation

method algorithm is coded in MICROCHIP PIC16f877a.

TLP250 is used to provide the control signal between

MOSFET and microcontroller the PWM pulse output is

shown if Figure 14.

Conclusion The paper presents two MPPT methods P&O and FLC whose

simulation results are compared and verified. The FLC based

MPPT method is more accurate compared to P&O method. A

20 Watts hardware prototype is developed of the proposed

P&O system. With an constant load (80 Ohm) the output

power is calculated from 10AM to 5 PM 10th August 2015.

The maximum power is getting 13.1 watts and its related

voltage is 32.4 volt at 2PM.The minimum power getting 2.45

watts its related voltage is 14 Volt at 5pm.

References

[1] Md. Imran Khan, M. Rihanul Islam, Md. Zahanagir

Mozumder and K.M Rahman, “Photovoltaic

Maximum Power Point Tracking Battery Charge

Controlle”1st International Conference on the

Developments in Renewable Energy

Technology(ICDRET).2009.

[2] Mahalakshmi, R., Aswin Kumar A., and Aravind

Kumar. "Design of Fuzzy Logic based Maximum

Power Point Tracking controller for solar array for

cloudy weather conditions" power and energy

systems conference towards sustainable energy,

march 2014.

[3] I.H Atlas and A.M. Sharaf, “A novel maximum

power fuzzy logic Controller for photovoltaic solar

energy systems” Renewable energy, vol.33, no.3,

pp.388-399, 2008.

[4] I.H Atlas and A.M Sharaf, “A generalized direct

approach for designing fuzzy logic controllers in Mat

0

10

20

30

40

10AM 11AM 12AM 1PM 2PM 3PM 4PM 5PM

Output Voltage(Volt)

0

5

10

15

10AM11AM12AM 1PM 2PM 3PM 4PM 5PM

Output Power (watts)

Power(watts)



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[6] Rao, S. P. Lakshman, Ciji Pearl Kurian, B. K. Singh,

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Simulation and Implementation of Perturb and observe Fuzzy based