Fuzzy Logic Based Maximum Power Point Tracking

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Fuzzy Logic Based Maximum Power Point Tracking

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PROJECT REPORT

ONFUZZY LOGIC BASED MAXIMUM POWER POINT

TRACKING

Submitted by:

Krithik Kumar Chandrashekar

Nisarg M. Dave




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Abstract

This project proposes the use of a fuzzy logic based control method for maximum power point

tracking for photovoltaic array. The power output of a solar array under normal operating

considerations is dependent on the temperature and irradiation conditions. Therefore in order

to maximize the amount of power delivered to the battery/load, a control technique must be

employed. Some of the common techniques used for MPPT are Fractional Open circuit current,

Perturb and Observe, Incremental conductance. There are advantages/disadvantages to each of

these techniques .This project makes a comparison with fractional open circuit current MPPT

and one without any MPPT.

Introduction

Photo-Voltaic based methods of power generation have become the crux of future energy

needs, it is quite apparent as to why this has become so , fossil fuels have not only caused

considerable environmental damage but the sources of the fuel have also diminished to a large

extent. Hence the focus has shifted to renewable energies. However renewable energies have

in some cases shown to have little efficiency, it is in these cases that new developments must

be applied to improve the output power thus making renewable energies more viable. One of

the ways to achieve this in solar energy is called maximum power point tracking. A maximum

power point tracker (or MPPT) is a high efficiency DC to DC converter that presents an optimal

electrical load to a solar panel or array and produces a voltage suitable for the load. PV

cells have a single operating point where the values of the current (I) and Voltage (V) of the cell

result in a maximum power output. These values correspond to a particular resistance, which is

equal to V/I as specified by Ohm's Law. A PV cell has an exponential relationship between

current and voltage, and the maximum power point (MPP) occurs at the knee of the curve,

where the resistance is equal to the negative of the differential resistance (V/I = -dV/dI).

Maximum power point trackers utilize some type of control circuit or logic to search for this

point and thus to allow the converter circuit to extract the maximum power available from a

cell.

Recently fuzzy logic controllers have been introduced in the tracking of the MPPT in PV systems.

They have the advantage to be robust and relatively simple to design as they do not require theknowledge of the exact model. They do require in the other hand the complete knowledge of

the operation of the PV system by the designer. A fuzzy logic system may seem as an ideal

choice for MPPT given the non linear nature of the PV characteristics of the solar cell.




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PV Array

Duty Ratio

Voltage Power

Figure 1: Solar Array System

The overall block diagram is illustrated above. Here we can see that between the array and the

battery we have a boost converter with a fuzzy logic controller. The voltage and power from the

array is measured and fed to the controller. Based on the rate of change of error in power and

voltage, the duty ratio is adjusted automatically. The duty ratio is adjusted such that maximum

possible power is delivered to the load; in this case we have the battery.

Application

The application we have chosen is a solar powered street light .The load is a 12V, 60 watt

lamp(s). To size the battery and the panel the following things are taken into consideration

1. Hours of Operation -10hours, 600 watt-hours

2. Days of Backup in case of cloudy weather /rainy days -1 day

DC-DC

CONVERTER

FUZZY LOGIC

CONTROLLER

Battery




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3. Grid connection – Not Present, Off Grid

4. Battery Voltage -12V

5. Number of Sun Hours for Chicago: 4.4 hours

After careful calculation we arrive with the conclusion that battery and solar panels should be

sized as follows.

Battery Size/Capacity-200ah

Solar Array Size-200Watt

Solar Panel Specifications

Current/Voltage/Power ES-A-200-fa3

Pmp

Ptolerance

Pmp, max

Pmp, min

ηmin

Pptc

Vmp

Imp

VocIsc

200

-0/+4.99

204.99

200.00

12.7

180.6

18.10

11.05

22.6011.80

As it can be seen that under standard conditions, the maximum power that can be extracted is

180 watts. However the data sheet of the solar panel1

specifies that the test conditions to be at

a temperature of 20 degree centigrade and 1000 w/m2.




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Battery Specifications

Ritar 12V / 200Ah VLRA Battery

Figure 2: Discharge Characteristics of the Battery

Parameter ValueCells Per Unit

Voltage Per Unit

Capacity

Weight

Max. Discharge Current

Internal Resistance

Operating Temperature Range

Normal Operating Temperature RangeFloat charging Voltage

Recommended Maximum Charging

Current Limit

Equalization and Cycle Service

6

12

200Ah@10hr-rate to 1.75V per cell @25°C

Approx. 60.0 Kg

2000A (5 sec)

Approx. 4 mΩ

Discharge: -20°C ~ 60°C

Charge: 0°C~50°C

Storage: -20°C~60°C

25 C±5C13.6 to 13.8 VDC/unit Average at 25°C

60 A




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DC-DC boost convertor

Figure3: Boost Converter2 Figure 4: Inductor current v/s time

The design of DC-DC boost converter is done to supply the battery for a 12V battery.

Inductance= 0.000128Henry

Capacitor=0.006 farad

Switching Device=MOSFET

Switching Frequency=1 kHz

A boost converter is part of a subset of DC-DC converters called switch-mode converters. The

circuits belonging to this class, including buck, flyback, buck-boost, and push-pull converters are

very similar. They generally perform the conversion by applying a DC voltage across an inductor

or transformer for a period of time which causes current to flow through it and store energy

magnetically, then switching this voltage off and causing the stored energy to be transferred to

the voltage output in a controlled manner.

Applying Kirchhoff’s rules around the loops and rearranging the terms gives an intuitive resultas shown below.

Vout/Vin=1/ (1-D)




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Fuzzy logic controller3

Figure 5: Fuzzy logic controller –look up table




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MPPT attempts to move an operation point of a solar array as close to the maximum power

point or the knee of the I-V curve shown in Figure as possible. Mathematically, this is equivalent

to finding the point where the derivative dP/dV is equal to zero. Alternatively, when

implemented in digital system, output voltage and current at consnsecutive time interval n-1

and n are sampled, to search the peak power point of a solar array, firstly defined as error

function is

E (n) =P (n)-P (n-1) / V (n) - V (n - 1).

Also, the associated change of error is defined as

∆E (n) =E (n)-E (n-1).

The controller works to force the error function, which is the derivative of power with respect

to the measured voltage, and its associated change of error to zero. Thus an optimal operation

point can be obtained. Instead of finding the underlying derivative, MPPT can also be achieved

by means of fuzzy logic. Let us denote a duty ratio of the switch in Fig.2 as D(n). With reference

to the I-V and power curve, the fuzzy meta rule for MPPT can be stated as “If the last change in

the duty ratio D(n) has caused the power to rise , keep moving the duty ratio D(n) in the same

direction otherwise , if it has caused the power to drop, then move in the opposite direction”

Rule (i) : if E(n) is A; and ∆E(n) is B; then ∆D(n) is C

Where A, B, and C represent fuzzy sets including positive big (PB), positive small (PS), zero (ZE),

negative big (NB), and negative small (NS). Figure 5 shows the membership functions of the

input variables E(n) and ∆E(n) and the output variable D(n).

E(n)\ ∆E(n) NB NS ZE PS PB

NB PB PB PS PB PB

NS PB PS PS PS PB

ZE NS NS ZE PS PS

PS NB NS NS NS NB

PB NB NB NS NB NB

Figure 6: Fuzzy rule table for ∆D(n)




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Results

Figure 7: I-V and I-P curves under 1000 w/m2

at 25 degree centigrade




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Figure 8 Power supplied for no MPPT

Figure 9 Power Supplied for fuzzy logic controller




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Figure 10 Power Supplied for Fractional Open Circuit case

Controller Type Power Delivered Watts State Of Charge

No MPPT 140 74.89%

Fractional Open Circuit

Current

173.0114 87.51%

Fuzzy Logic Based 173.5729 87.44%




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Conclusion

As it can be observed that there is an improvement of 23% from the case of no mppt to

that of fractional open circuit voltage and fuzzy logic based controller. Fuzzy logic offers only a

marginal improvement in power delivered to the battery. However the state of charge reached

by both the cases is similar. Though fuzzy logic may offer benefits in the long run, there is a

tradeoff between the processing time and cost.

References

[1]http://www.altestore.com/store/Solar-Panels/150-Watts-Up-SolarPanels/Evergreen-

ES-A-200-FA3-200W-12V-Solar-Panel-Black/p7348/

[2] B. M Hasaneen, Adel A. Elbaset , ”Design And Simulation of DC/DC Boost Converter” The

Twelfth International Middle East Power System Conference, MEPCON'2008, South Valley University,

Faculty of Eng., Aswan, Egypt, Vol. I, March 13-15, 2008

[3] N. Khaehintung , P. Sirisuk “Implementation of Maximum Power Point Tracking Using

Fuzzy Logic Controller for Solar-Powered Light-Flasher Applications”

Documents

Fuzzy Logic Based Maximum Power Point Tracking