This document describes a student project to build a single axis solar tracker using a modified particle swarm optimization maximum power point tracking (PSO-MPPT) algorithm. It includes sections on maximum power point of PV panels, different MPPT algorithms including PSO, the block diagram, charge controller, buck converter, solar tracker, voltage/current sensing circuits, hardware implementation, the PSO software, and test results. The results show the solar tracker effectively tracked the sun and the PSO-MPPT algorithm found the maximum power point under different lighting conditions including partial shading. The hardware implementation matched the performance in the reference paper on PSO-based MPPT.

1. SINGLE AXIS SOLAR TRACKER WITH
MODIFIED PSO-MPPT ALGORITHM
Project Members:
Jyoti Choudhary (1MV10EE020)
Rashmi Hegde (1MV10EE040)
Vrinda K. M. (1MV10EE060)
External Project Guide:
Dr. Vijay Mishra, IISc, Bangalore.
Internal Project Guide:
Ms. Nayana B. R. , Assistant Professor.
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2. CONTENTS
 Introduction
 Maximum Power Point of PV Panels
 MPPT Algorithms
 Block Diagram
 MPPT charge controller
 Buck Converter
 Solar Tracker
 Voltage and Current Sensing Circuits
 Hardware Implementation
 Software- PSO
 Results
 Conclusion 2

3. INTRODUCTION
 Renewable sources of energy have become increasingly
popular source for electrical energy due to its obvious
advantages over non-renewable sources of energy; the
most abundant and virtuous among these sources being
solar energy.
 Solar energy can be directly harnessed to generate
electricity with the help of photovoltaic (PV) cells or solar
cells, which convert the radiant energy directly into
electricity.
 However, the efficiency of the solar panels depends heavily
on both the amount of reception off insolation and its
uniformity over the cells of the PV panel. 3

4. MAXIMUM POWER POINT OF PV PANELS
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Normal Condition Partial Shading Condition

5. MPPT ALGORITHMS
 Perturb and Observe (P&O)
 Incremental Conductance (I.C)
 Fuzzy Logic Control (FL)
 Neural Network (NN)
 Particle Swarm Optimization (PSO)
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6. BLOCK DIAGRAM
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7. MPPT CHARGE CONTROLLER
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8. BUCK CONVERTER
 The solar panels provide higher voltage levels than
their ratings during peak hours whereas the battery
charges at the level of 13.5 at the maximum.
 This leads to the current increasing to dangerously
high levels and may result in damage of the battery.
 To prevent this, the output from the solar panel is
required to be reduced before being given to the
battery.
 The converter reduces the voltage level of the input
to the battery and brings it closer to the battery
rating to maintain the current at optimum levels.
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9. CIRCUIT DIAGRAM OF BUCK CONVERTER
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10. SOLAR TRACKER
 The single axis solar tracker uses Light Detecting
Resistors(LDRs) as sensors to track the position of
the sun from east to west.
 On the basis of the difference in the intensities of
sunlight received by the LDRs, the microcontroller
is programmed to control the switching of the
relays.
 The sequence of relay switching determines the
direction of motor rotation, which in turn decides the
direction of motion of the Linear Actuator.
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11. CIRCUIT DIAGRAM OF SOLAR TRACKER
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12. CIRCUIT DIAGRAM OF VOLTAGE SENSING
CIRCUIT
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Panel Side Voltage Sensing
Battery Side Voltage
Sensing

13. CIRCUIT DIAGRAM OF CURRENT SENSING
CIRCUIT
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14. COMPLETE HARDWARE SET UP
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15. CURRENT AND VOLTAGE SENSING CIRCUIT
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16. BUCK CONVERTER CIRCUIT
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17. COMPARATOR CIRCUIT
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18. RELAY CONTROL CIRCUIT
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19. SOFTWARE - PSO
 PSO Initialization
Number of Particles (NP) =3
Cognitive Learning Co-efficient (c1) = 1.2
Social Learning Co-efficient (c2) = 1.6
Inertia Weight (w) = (iter (max) - iter)/ (iter (max))
 Update Particle Velocity and Position
 Convergence Criteria
1. Iter= max. iter
2. Δv = |v(iter + 1) − v(iter)|
 Change in Shading Conditions
P(i) < P(i-1) 19

20. 20

21. RESULTS
 To validate the proposed algorithm discussed in
previous sections, experimental studies were
carried out on two 20V, 2A, and 40W solar panels.
 The panels are connected in panels with the tracker
algorithm set in motion.
 Real-time test measurements are presented for the
following five cases.
 These real-time graphs effectively validate the main
aim of the system, which is to track the position of
the sun and simultaneously deliver maximum power
output irrespective of insolation conditions.
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22.  The solar tracker effectively tracked the position of
the sun at various points of the day from east to
west.
 The proposed PSO based MPPT, which employs
multi-search based technique; was capable of
finding the global maximum power point even under
complex partial shading conditions.
 This ensured that the battery, which is the main
load of the system, was charged as close to its
maximum voltage as possible.
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23. CHANGE IN INSOLATION
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 A sudden decrease in
insolation of one panel
with a gradual
decrease in the
insolation of the other.
Condition Graph

24. PANEL SHADING-1
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 Both panels covered
completely, one at a
time.
Condition Graph

25. PANEL SHADING-2
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 One panel is gradually
shaded.
Condition Graph

26. PANEL CELL SHADING-1
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 One horizontal strip of
both panels is shaded,
one at a time.
Condition Graph

27. PANEL CELL SHADING-2
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 One horizontal strip of
both panels is shaded,
simultaneously.
Condition Graph

28. CONCLUSIONS
 The hardware implementation of the single axis
solar tracker with modified PSO-MPPT algorithm
has been executed.
 The results obtained match the simulation results of
the reference paper “An Improved PSO based
MPPT for PV with reduced oscillations” by Ishaque
et al- An IEEE transaction on Power Electronics,
August 2012.
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29. THANK YOU!
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