Nowadays solar energy is getting much attention in all the available renewable sources. Most of the solar devices consist of a solar receptor arranged to face the sun to get the maximum amount of sunlight on a photovoltaic panel using mirrors and auto tracking technology. However the main defect occurs with the day and seasonal variations. The amount of electricity produced from PV panel depends on the amount of radiation that is focused on the PV panel. More the radiation on panel results in more amount of electricity. In this present work an attempt is made to increase the amount of radiation by introducing the mirror and auto tracking arrangement to allow more rays to be concentrated on a small area of photovoltaic panel. A new platform made in shape in parabolic which is adjusted to follow the sun. Mirrors and PV panels are attached on a Flat shaped frame at an angle of 120° between them. A fuzzy logic controller is proposed to estimate the exact time for sun tracking. The closet location while getting the sunlight is considered as input taken from the database. This method minimizes the number of motors for initial start and helps for the less quantity of energy loss in partial or full cloud conditions. The comparison has studied for fixed PV panel and this new PV panel with mirror and results are tabulate. The results show an increase of 33% in average of efficiency by using mirror and tracking system

1. http://www.iaeme.com/IJMET/index.asp 360 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 9, Issue 11, November 2018, pp. 360–369, Article ID: IJMET_09_11_036
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=11
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
EXPERIMENTAL INVESTIGATION ON
EFFICIENCY ENHANCEMENT OF THE SOLAR
PANELS WITH MIRRORS AND PARABOLIC
PLATFORM USING FUZZY LOGIC
Suneetha Racharla*
Research Scholar, Department of Mechanical Engineering, St.Peter's Institute of Higher
Education & Research, Chennai-54, India
K Rajan
Professor, Dr.M.G.R.Educational & Research Institute, Chennai-95, India
M.Rajaram Narayanan
Professor, Dr.M.G.R.Educational & Research Institute, Chennai-95, India
K R Senthil Kumar
Professor, R.M.K.Engineering College, Chennai-601206, India
*Corresponding author
ABSTRACT
Nowadays solar energy is getting much attention in all the available renewable
sources. Most of the solar devices consist of a solar receptor arranged to face the sun to
get the maximum amount of sunlight on a photovoltaic panel using mirrors and auto
tracking technology. However the main defect occurs with the day and seasonal
variations. The amount of electricity produced from PV panel depends on the amount of
radiation that is focused on the PV panel. More the radiation on panel results in more
amount of electricity. In this present work an attempt is made to increase the amount of
radiation by introducing the mirror and auto tracking arrangement to allow more rays to
be concentrated on a small area of photovoltaic panel. A new platform made in shape in
parabolic which is adjusted to follow the sun. Mirrors and PV panels are attached on a
Flat shaped frame at an angle of 120° between them. A fuzzy logic controller is proposed
to estimate the exact time for sun tracking. The closet location while getting the sunlight
is considered as input taken from the database. This method minimizes the number of
motors for initial start and helps for the less quantity of energy loss in partial or full cloud
conditions. The comparison has studied for fixed PV panel and this new PV panel with
mirror and results are tabulate. The results show an increase of 33% in average of
efficiency by using mirror and tracking system.

2. Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar
http://www.iaeme.com/IJMET/index.asp 361 editor@iaeme.com
Keywords: Solar Tracking, Fuzzy Logic, Sensor, Auto Tracking, Photovoltaic Panel,
Global Warming, Irradiation, Efficiency, Fuzzy Sliding Mode Control, Boost Converter,
Dual-Axis Solar Tracking, Neural Network.
Cite this Article Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil
Kumar, Experimental Investigation On Efficiency Enhancement Of The Solar Panels With
Mirrors And Parabolic Platform Using Fuzzy Logic, International Journal of Mechanical
Engineering and Technology, 9(11), 2018, pp. 360–369.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=11
1. INTRODUCTION
Global warming is gaining more importance on the international agenda in the last few decades.
Many developed countries are started searching of new energies to compensate the fossil fuels.
In this context power generation from solar energy is proved as best and clean energy compared
to the remaining sources. The primary concept of solar panel is to convert the solar radiation
energy into electrical energy. However the process has few drawbacks of less efficiency for a
small radiation and changes through weather circumstances of sun radiation and temperature. The
efficiency of PV panel depends on many factors such as isolation, temperature on the panel,
shading effects and sun radiation etc [9]. To facilitate the maximum efficiency it is advisable to
place the solar panel perpendicular to the sun rays. Barnam Jyoti Saharia et al. [1] discussed about
the unavailability of a general simulation platform for testing and evaluation of fuzzy logic, P&O
and neural network algorithms. The experimental results cleared that the tracking effectiveness
reduces in the order of fuzzy logic controlled tracking, P&O algorithm, and neural network
controlled tracking. The fuzzy logic control is appropriate for tracking since its high performance
with the varying climate conditions. Chakravorty et al. [2] presented the design and
implementation of fuzzy logic controller for the controlling of DC motor. In this a MATLAB
simulink model is proposed for the DC motor controlling speed using fuzzy logic technique.
Huang et al. [3] proposed the implementation of a two-axis solar tracking with fuzzy logic
controller. To receive maximum efficiency of the panel, it is essential to trace the sun always. A
fuzzy logic controller is implemented to calculate the time to track the sun. The closet location
for getting the direct sunlight can be obtained from the database. This method minimizes the
number of starting motors and gives in minimum energy loss in unstable weather conditions.
Dietmar et al. [4] proposed an exact Sun tracking for more accurate measurements of direct and
diffuse solar radiation. A new KSO-STREAM with an independent, cost-effective and fully
automated platform was designed to estimate the point accuracy of the solar tracking devices.
The experimental set up consists of, KSO-STREAMS is fixed as a pyrheliometer on the tracking
system to get right image of the sun location. The results states that 72.9% of all the interpretation
prepared in periods with DIR. On the clear sky days, the BSRN requirements are fulfilled and
accuracy values for the tracking are given as 76.4% of observations. Hamzah Hijawi et al. [5]
discussed about many fuzzy logic models of dual-axis solar tracking systems and a fuzzy logic
controlled DC motor was designed. First, the system was modeled using Mamdani fuzzy logic
modeling then various cases of ANIFS models were applied. To facilitate the effectiveness of the
proposed dual-axis solar tracking models, a second stage of fuzzy inference system was used to
forecast the output power. Results from this model showed that the proposed dual-axis solar
tracking system provided 22% more power than the fixed PV system. Cong-Hui Iulia
Stamatescua et al. [6] proposed a tracking technique for the solar panel control to improve the
conversion efficiency of the system. An algorithm was developed using a tri-positional control
strategy. The solution was developed using the graphical programming environment, Lab VIEW.
Liu et al. [7] proposed a fuzzy-logic-controlled maximum power point tracking algorithm for
photovoltaic systems. The power and Output voltage had given as inputs for the fuzzy logic

3. Experimental Investigation On Efficiency Enhancement Of The Solar Panels With Mirrors And
Parabolic Platform Using Fuzzy Logic
http://www.iaeme.com/IJMET/index.asp 362 editor@iaeme.com
controller and these are compared with the values obtained using conventional perturb and
observe (P&O) method by designing an asymmetrical membership function (MF) concept. The
results showed the PV system with the transient time and the MPPT tracking accuracy are
increased by 42.8% and 0.06% respectively. Makhloufi et al. [8] implemented the mathematical
modeling and simulation of the solar system using Matlab with Simulink [9]. In this the
photovoltaic system for variable temperature and solar radiation are studied using maximum
power point tracking using an intelligent control method. A DC-DC boost converter is used with
s a fuzzy logic controller in the system. Roshan et al. [10] implemented the maximum power
point tracking of a solar system by controlling the input resistance value of a switching power
converter. In this, an inversion control method is proposed with the nonlinear input resistance of
a boost converter. The results show that the solar system tracks various maximum power points
under varying irradiance and load conditions. Sabah Miqoi et al. [11] compared the P&O control,
sliding mode control method and fuzzy sliding mode control for a solar water pumping system
with a DC/DC boost converter. The simulations results indicate the higher performance of the
developed fuzzy sliding mode control. Seera et al. [12] developed a modified fuzzy min–max
clustering neural network. The experimental results indicate the performance of MFMM for data
clustering tasks and its applicability to the power systems area. Usta et al. [13] designed a
photovoltaic tracking system to optimize the process of solar energy receivers. The solar tracking
system is designed using Matlab with a fuzzy logic toolbox. And also, PI control is used and the
results are compared with the results of fuzzy logic controller. Keke Zhang et al. [14]
implemented a solar system with panels attached to the two-dimensional platform with a tracking
controller to work in limited satellite attitude coupling control capability. Depending on the solar
vector variation two-dimensional solar tracking stationary guidance system is designed and a
mathematical simulation was conducted, The results state that the solar tracking accuracy of two-
dimensional stationary guidance reaches 10∘ which can meet engineering application
requirements. Aymen Jemaa et al.[15] discussed about the two main elements for the power flow
between a wind turbine and a solar system. One is the fuzzy logic controller applied to the
maximum power point tracking and second is the real-time controlling system for better
performance by implementing a new algorithm using the Xilinx System Generator. The results
show the presented system and its controlling accuracy gives a better tool for optimizing the solar
system. Yaqin et al.[16] proposed a new solar tracker with Maximum Power Point Tracking
(MPPT) using Fuzzy Logic technique. Simulations are done using PSIM as the main circuit and
Simulink as the controlling circuit. The results state that the maximum power can be obtained for
difference irradiance and temperature conditions, for both load and battery.
2. DESIGN OF SOLAR TRACKING SYSTEM
The proposed solar tracking system is designed based on the technical requirements like
minimum energy consumption, reliability in operation, simplicity of movement solution,
possibility of system combined with monitoring and control. For this implicitly the primary
technical requirements are chosen based on a DC motor, voltage and current monitoring, without
sensor motion i.e. speed or position, parabolic platform, and mirrors.
The parabola used as the main tilting part of the system. This carries the entire system i.e. the
motors, controlling circuit, panels and mirrors. The parabola is designed using a software
parabolic calculator2.0 as shown in fig 4.1.The Length and Depth are calculated as 150cm and
50cm.

4. Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar
http://www.iaeme.com/IJMET/index.asp 363 editor@iaeme.com
Figure 1 Parabolic calculator
Figure 2 Working model of parabolic platform
Two photovoltaic panels of 20 watt are selected for the experiment. For these panels, two
highly polished mirrors are selected to double the concentrated radiation on the panel. The panel
and mirror dimensions are considered as smaller panel and mirror length and width as 45 cm&35
cm respectively. Round plate diameter is taken from calculation as 70 cm and angle of panel from
Ground is considered as 45° & 15°.Angle between panel and mirror is calculated as 120° to avoid
shading loss

5. Experimental Investigation On Efficiency Enhancement Of The Solar Panels With Mirrors And
Parabolic Platform Using Fuzzy Logic
http://www.iaeme.com/IJMET/index.asp 364 editor@iaeme.com
Figure 3 Prototype of supporting frame &mirror, panel arrangement
3. TECHNICAL SPECIFICATIONS
Table 1 Technical Specifications
Dimensions of bottom 0support and roller
length=120 cm
Breadth=120 cm
Thickness=20 cm
Material= cast iron
Dimensions of parabolic platform
Length of parabola= 150 cm
Depth of parabola= 50 cm
Material = cast iron
Dimensions of solar panel and mirror
Smaller panel and mirror length=45 cm
Smaller panel and mirror width= 35 cm
Larger panel and mirror length= 55 cm
Larger panel and mirror width= 35 cm
Round plate diameter = 70 cm
Angle of panel from round plate= 15°
Angle between panel and mirror =120°
Dimensions of rollers
Diameter of roller= 40 mm
Weight of the equipment
Panel and mirror weight= 2 kg
Weight of tilting section(parabola)= 9 kg
Weight of support plate for panel and mirror =
6kg
Weight of bottom support for parabola =8 kg
Parabola specifications
Linear Diameter=186.69
Diameter=150.00

6. Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar
http://www.iaeme.com/IJMET/index.asp 365 editor@iaeme.com
Depth=50.00
Focal Length =28.13
Volume=441786.47
FLength/Diameter=00.19
Area=17671.46
Mechanical & Electrical Temperatures
10° to 70° C
4. CALCULATION OF SUN ROTATION
From the available geographical data Chennai Longitude and Latitude are given as 80.27o
E and
13.08o
N respectively.
The angle between shadow length and sun ray is calculated by taking one meter scale as
reference.
From the figure shown below
Ɵ =
Figure 4 Calculation Of Sun Rotation
Table 1 Shadow length w.r.to Sun rotation angle

7. Experimental Investigation On Efficiency Enhancement Of The Solar Panels With Mirrors And
Parabolic Platform Using Fuzzy Logic
http://www.iaeme.com/IJMET/index.asp 366 editor@iaeme.com
The above figure shows the variation of sun angle according to varying Time in a day from
10AM and onward. It helps to make sure for a particular time at what angle we should keep the
solar panel to achieve maximum radiation. According to Chennai longitude 80.27 degree east and
latitude 13.08 degree north the maximum solar radiation that falls on earth is 1000 watts per meter
square.
5. CALCULATION FOR PHOTOVOLTAIC ROTATION
The main aim of the paper is to focus the PV panel towards the sun at maximum time. For this
astronomical data is considered to estimate the sun path angle. Total rotation angle of panel is
180° where as Sun light duration in One-Day is 10 hrs. In case of panel, the angle of rotation in
1hr is estimated as 180°/10.The angle of rotation in 30minutes is calculated as 18°/2 and so the
angle of Rotation in 15minutes becomes 4.5°. Two servo motors are used for smooth and precise
movement of system. Selected rpm of the motor for depending on the overall system is
200rev/sec. There by the angular speed equals to 200 * 2 * ∏ / 60 which gives 20.94 rad /sec.
From this the angular acceleration is estimated at 2.09 rad /sec2.From the above values the
Moment of inertia & Torque are calculated as 0.49 kg-m2 & 1.02n-m respectively.
Figure 5 Experimental Setup
6. MICROCONTROLLER PROGRAMMING USING FUZZY LOGIC
CONTROL
The Proposed solar tracking system contains a controlling board, a controlling program, a
power supply, one motor interface panel and LDR sensors and two DC motors (M1, M2) and
microcontroller board. To make the system simple and cheap, the microcontroller PIC16F877A
is used. A closed loop circuit was implemented to control the unit. PIC16F877A is provided
with thirteen I/O ports, 1Kx14 EPROM memory, and four ADC channels with 8-bit and10MHz
clock frequency.

8. Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar
http://www.iaeme.com/IJMET/index.asp 367 editor@iaeme.com
Figure 6 Embedded circuit of microcontroller
Figure 7 Block diagram of fuzzy controller
To get the accurate motion of the PV panel, a crystal of 4MHz frequency acts as a clock signal
generator for MCU (Micro Controlling unit). The signal coming from the voltage divider consists
of resistor values and sensor (S1, S2, S3, and S4) readings used as inputs for the MCU (RA0,
RA1, RA2, and RA3). If the error between the sensors opposite to each other means the error
signal is greater than the default value, then the MCU generates moving signal for the motors. If
the error signal is similar with the default value, then the MCU gives no signal results in the PV
panel is perpendicular to the sun and absorbing maximum radiation. MATLAB with Fuzzy logic
toolbox is applied to the efficient motor control.
7. EXPERIMENTAL RESULTS
Type of
solar
system
Current
(ampere)
Voltage
(volt)
Input-
power
(watt)
Output-
power(watt)
Efficiency
Fixed
panel
without
mirror
1.21 15.6 190 19 10%
Fixed with
mirror
1.61 18.4 190 29.64 15.6%

9. Experimental Investigation On Efficiency Enhancement Of The Solar Panels With Mirrors And
Parabolic Platform Using Fuzzy Logic
http://www.iaeme.com/IJMET/index.asp 368 editor@iaeme.com
Tracking
without
mirror
2.203 19.06 190 43.89 22.1%
Tracking
with
mirror
2.14 22.4 190 47.95 25.24%
8. CONCLUSION
An automatic solar tracking system is designed with a parabolic platform for the solar Panel
control to place the solar panel direct to the sun light continuously by scanning in which direction
the solar power is high. After getting the position the panel will follow the sun light to get
maximum radiation by for the next position with fuzzy logic controller. The comparison has
studied for fixed PV panel and this new PV panel with mirrors and results are tabulated. The
results shows increase in efficiency by using mirror and tracking system.
REFERENCES
[1] Barnam Jyoti Saharia, Munish Manas and Bani Kanta Talukdar. (2016) ‘Comparative
evaluation of photovoltaic MPP trackers: A simulated approach’, Cogent Engineering, Vol.
3, pp. 1-17
[2] Chakravorty, Jaydeep, and Ruchika Sharma. (2013) ‘Fuzzy logic based method of speed
control of DC motor’, International journal of emerging technology and advanced
engineering, Vol. 3 No.4, pp.64-66
[3] Cong-Hui Huang, Heng-Yau Pan and Kuan-Chen Lin. (2016) ‘Development of Intelligent
Fuzzy Controller for a Two-Axis Solar Tracking System’ , Applied Sciences, Vol. 6 No.130,
pp.1-11
[4] Dietmar J. Baumgartner, Werner Pötzi, Heinrich Freislich, Heinz Strutzmann, Astrid M.
Veronig and Harald E. Rieder .(2017) ‘An automated method for the evaluation of the
pointing accuracy of Sun-tracking devices’, Atmospheric Measurement Techniques, Vol.10,
pp. 1181–1190
[4] Hamzah Hijawi and Labib Arafeh. (2016) ‘Design of dual axis solar tracker system based on
fuzzy inference systems’, International Journal on Soft Computing, Artificial Intelligence and
Applications (IJSCAI),Vol. 5 No.2/3, pp.23-36
[5] Iulia Stamatescua, Ioana Făgărășana , Grigore Stamatescua ,Nicoleta Arghiraa snd Sergiu
Stelian Iliescua. (2014) ‘Design and Implementation of a Solar-Tracking Algorithm’ 24th
DAAAM International Symposium on Intelligent Manufacturing and Automation 2013,
Procedia Engineering, Vol. 69, pp. 500 – 507
[6] Liu, Chun-Liang, Jing-Hsiao Chen, Yi-Hua Liu, and Zong-Zhen Yang. (2014) ‘An
asymmetrical fuzzy logic- control-based MPPT algorithm for photovoltaic systems’,
Energies, Vol. 7 No.4, pp. 2177-2193
[7] Makhloufi, M. T, Khireddine , M. S, Abdessemed,Yand Boutarfa, A. (2014) ‘Maximum
Power Point Tracking of a Photovoltaic System using a Fuzzy Logic Controller on DC/DC
Boost Converter’, IJCSI International Journal of Computer Science Issues, Vol. 11No.3, pp.1-
11
[8] Mukesh Kumar, Dr.Kapoor, S.R, Rajkumar Nagar and Amit Verma .(2015) ‘Comparison
between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost
Converter’, International Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering,Vol. 4 no.6,pp. 4927-4939
[9] Roshan, Y.M, Moallem, M. (2013) ‘Maximum power point estimation and tracking using
power converter input resistance control’, Sol.Energy, Vol. 96, pp.177–186

10. Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar
http://www.iaeme.com/IJMET/index.asp 369 editor@iaeme.com
[10] Sabah Miqoi, Abdelghani El Ougli,Mohamed Boutouba and BelkassemTidhaf. (2017) ‘Fuzzy
sliding mode control for maximum power point tracking of a photovoltaic pumping system’,J.
Electrical Systems,Vol. 13 No.1,pp. 95-114
[11] Seera, M, Lim, C.P, Loo, C.K and Singh, H. (2015) ‘A modified fuzzy min-max neural
network for data clustering and its application to power quality monitoring’, Applied Soft
Computing, Vol. 28 No. 2015,pp. 19–29
[12] Usta, M. A, Akyazı, Ö and İ. Altaş, H. (2011) ‘Design and Performance of Solar Tracking
System with Fuzzy Logic Controller’ : 6th International Advanced Technologies Symposium
(IATS’11), 16-18 May 2011, Elazığ, Turkey, pp.331-336
[13] Keke Zhang , Chaoming Si ,Zhencai Zhu,Chongbin Guo and Qi Shi. (2018) ‘A Two-
Dimensional Solar Tracking Stationary Guidance Method Based on Feature-Based Time
Series’, Hindawi Mathematical Problems in Engineering, Vol. 2018, pp.1-12
[14] Aymen Jemaa ,Ons Zarrad, Mohamed Ali Hajjaji and Mohamed Nejib Mansouri. (2018)
‘Hardware Implementation of a Fuzzy Logic Controller for a Hybrid Wind-Solar System in
an Isolated Site’, Hindawi International Journal of Photo energy, Vol. 2018, pp.1-16.
[15] E N Yaqin, A G Abdullah , D L Hakim and Nandiyanto. (2017) ‘MPPT based on Fuzzy
Logic Controller for Photovoltaic System using PSIM and Simulink’, The 2nd Annual
Applied Science and Engineering Conference (AASEC 2017) Materials Science and
Engineering ,Vol.288 (2018) 012066 doi:10.1088/1757-899X/288/1/012066