experimental investigation on efficiency … · requirements. aymen jemaa et al.[15] discussed...

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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.

Suneetha Racharla, K Rajan, M.Rajaram Narayanan and K R Senthil Kumar


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

Experimental Investigation On Efficiency Enhancement Of The Solar Panels With Mirrors And Parabolic Platform Using Fuzzy Logic


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.



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



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



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



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.



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%



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] 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

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