![International Journal of Applied Power Engineering (IJAPE)
Vol. 3, No. 1, April 2014, pp. 1~8
ISSN: 2252-8792 1
Journal homepage: http://iaesjournal.com/online/index.php/IJAPE
Fuzzy Logic Controller Based Single Buck Boost Converter for
Solar PV Cell
K. Manickavasagam
Principal, Gopalan College of Engineering and Mangement, Bangalore, Karnataka
Article Info ABSTRACT
Article history:
Received Apr 16, 2013
Revised Aug 27, 2013
Accepted Sep 14, 2013
This paper deals with solar power production controlled by Fuzzy Logic
Controller (FLC) and Single Input Buck-Boost (SIBB) converter. Since the
solar energy is continuously varying, according to the irradiation the FLC
generates control pulses to switch on the MOSFET device. To analyze the
real time feasibility of this method, the system is simulated by using
MATLAB/Simulink 2010a. A simulation model of the system is developed
with solar Photovoltaic (PV) cell, FLC and SIBB in contradiction of the real
world conditions. The results are presented and discussed in this paper.Keyword:
Fuzzy Logic Controller (FLC)
Single Input Buck-Boost
(SIBB) converter
Copyright © 2014 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
K.Manickavasagam,
Principal, Gopalan College of Engineering and Mangement,
Bangalore, Karnataka, India,
Mobile no: +91 0776090182.
Email: manicavasagam2003@yahoo.com
1. INTRODUCTION
Solar energy is continuously varying according to the climatic condition. The variation in irradiation
affects the solar power produced by the panel. The solar power is considered as predominant resources when
compared with others. In this field many researches are carrying on to improve the efficiency of solar power
production.
The performance of PV system can be enhanced by power converter with intelligent control
techniques to develop the circuit model to improve the efficiency of solar power generation. MPPT algorithm
is one powerful suggestion for stand-alone Solar systems to improve the efficiency. The application of
Maximum Power Point Tracking in the PV module was developed [1]–[3] to achieve high performance in
actual field. Modeling of buck converter using MATLAB is explained in [4]. Researchers are carried out in
the solar cell for the understanding of the characteristic features and working scenarios [5]–[7]. The power
converter designs for solar PV cells are given in [8]–[11]. The conventional controllers used are insufficient
because of changes in operating points during a daily cycle and may no longer be suitable in all operating
conditions. The use of intelligence controllers are cited in the most of the papers recently [12]-[14] which has
faster transient responses and is more robust than several control method. The method of construction of
fuzzy rules and their usage of membership functions are given in [15]-[17].
In this paper, a Fuzzy logic controller along with a single input buck-boost converter is proposed,
which can deal with photovoltaic power individually based on their availability at that given instant. In
conventional controller, the system or process being controlled is modeled. But in a fuzzy controller, the
focus is on the human operator's judgment. Hence fuzzy logic provides a powerful representation and good
results for measurements of uncertainties present in this problem. Since, the availability of sun light is](https://image.slidesharecdn.com/1316-959-1-pb-201208061715/85/Fuzzy-Logic-Controller-Based-Single-Buck-Boost-Converter-for-Solar-PV-Cell-1-320.jpg)
![ ISSN: 2252-8792
IJAPE Vol. 3, No. 1, April 2014 : 1 – 8
2
continuously varying; the fuzzy logic controller is used to generate the pulses for MOSFET device which is
connected with single input buck-boost converter to generate required output.
2. MODELING OF THE SYSTEM
Fig. 1 illustrates block diagram of the solar power generation system. The major circuit elements are
PV cell, FLC, MOSFET, single input buck-boost converter, battery and load. The FLC generates the control
signal depends on the availability of sun light, to achieve required voltage. The resource available with
required power level is connected to the active system to supply the load and the mode of operation preferred
will decide by the controller.
Fig 1. Block diagram of the proposed system
3. PRINCIPLE OPERATION OF THE PROPOSED SYSTEM
A schematic diagram of a single input buck-boost converter is given in Fig. 2. As shown, a buck-
boost converter is nothing but cascade connection of the two basic converters: the step-down converter and
step-up converter. The main application of such a converter is in regulated dc power supplies, where a
negative polarity output may be desired with respect to the common terminal of the input voltage, and the
output voltage can be either higher or lower than the input voltage [18]. The control signal generation, for
the selection of available energy resource and the regulation of output voltage of the dc-dc converter is
controlled by FLC.
Fig 2. Single input Buck-Boost converter of the proposed system
4. DESIGN OF BUCK BOOST DC-DC CONVERTER
The buck-boost converter circuit parameters of this proposed method is designed as follows. Some
of the important circuit parameters are listed as follows:
Input Power Pin : 50 Watts
PV panel voltage : 12V
Efficiency η : 0.9
Output Power Po : 45 Watts
Output Current Io : 3.75 A
Battery : 12V
Switching frequency : 20 kHz
Switching period T=1/f=Ton+Toff
Regulated output obtained from the dc-dc converter is used to charge the battery and the connected
load. The duty cycle D is assumed as 0.5. The minimum value of inductance (L) and capacitance (C)
PV
Cell
MOSFET
Single buck-boost
converter
Load
Battery
Gate Pulses
Produced by
FLC](https://image.slidesharecdn.com/1316-959-1-pb-201208061715/85/Fuzzy-Logic-Controller-Based-Single-Buck-Boost-Converter-for-Solar-PV-Cell-2-320.jpg)
![IJAPE ISSN: 2252-8792
Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell (K. Manickavasagam)
3
L = R [1- D]2
/ 2f (1)
ΔVo/Vo = DTs / RC (2)
Using equations (1) and (2) the minimum values of inductor and capacitor are calculated for the dc-
dc converter and expressed below
inductance (L) : 5mH
capacitance (C) : 15µF
5. DESIGN OF FUZZY LOGIC CONTROLLER
In this paper, the FLC is used to generate the pulses to drive the MOSFET. The PV cell’s
output voltage (V) and change in output voltage (∆V) are chosen as an input to the fuzzy logic
controller. The control output (u) signal is compared with triangular waveform and generated pulses
are used to switch on the MOSFET. The input of FLC PV cell’s output voltage (V) and change in
output voltage (∆V) are converted into fuzzy values by fuzzification. The ranges of input and output
variables are assigned with linguistic variables. The Gauss membership function is used in this work.
The input and output variables are assigned with 5 linguistic variables as follows:
1. The PV cell output voltage (V) is classified into:
Negative maximum (V -vemax); Negative medium (V -vemed); Zero (V zero); Positive medium (V
+vemed); Positive maximum (V +vemax)
2. The change in output voltage (∆V ) is classified into:
Negative maximum (∆V -vemax); Negative medium (∆V -vemed); Zero (∆V zero); Positive
medium (∆V +vemed); Positive maximum (∆V +vemax)
3. The output of fuzzy logic controller u is classified into:
Negative maximum (u -vemax); Negative medium (u -vemed); Zero (u zero); positive medium (u
+vemed); Positive maximum (u +vemax).
The input variable V and ∆V lies within the range of [-0.0188 0.001] and [-0.009.0.0188].
The control output ‘u’ lies in the range of [0 0.0123]. These input and output ranges are used for
designing the FLC, in which each of input and output set is assigned with five linguistic variables and
25 rules are framed in fuzzy inference engine. The rules are given in Fig .3.
Fig .3 Picture of FLC rule base
In this design, “Center of gravity Method” is used for defuzzification. It should be noted that for
various rules (r =1…R) would be in operation for a set of (V, V), each recommending possibly different
fuzzy controller actions. The defuzzified output is obtained by the following expression](https://image.slidesharecdn.com/1316-959-1-pb-201208061715/85/Fuzzy-Logic-Controller-Based-Single-Buck-Boost-Converter-for-Solar-PV-Cell-3-320.jpg)
![IJAPE ISSN: 2252-8792
Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell (K. Manickavasagam)
7
Fig .9.b
To analyze the performance of single buck boost converter, the step pulse is given as an input
through the summation block to the FLC as shown in simulink model in Fig .4. From Fig .8 and Fig .9, it is
observed that up to 0.2 sec the converter is in boost mode and after 0.2 sec, it is buck mode of operation.
7. CONCLUSION
This paper presented an idea of solar PV system controlled by FLC with single input buck boost
converter. The simulation model of the proposed system is developed and the results obtained under different
conditions are discussed and presented in this paper. The variation in solar irradiation is compensated by FLC
and single buck boost converter and the output voltage remains same irrespective of solar irradiation. The
results proves that the validation and real time feasibility of the proposed new model. For the research
purpose only single PV cell is considered in this study. There is a possibility to control the entire solar power
plant using solar PV cells with the same method.
REFERENCES
[1] Rong-Jong Wai and Wen-Hung Wang. “High-Performance Stand- Alone Photovoltaic Generation System”, IEEE
Transactions on Industrial Electronics, Vol/Issue: 55(1). Pp. 240-250, 2008.
[2] R. Gules, J. De Pellegrin Pacheco, H. Leaes Hey and J. Imhoff. “A Maximum Power Point Tracking System with
Parallel Connection for PV Stand-Alone Applications”, IEEE Transactions on Industrial Electronics, Vol/Issue:
55(7). Pp. 2674-2683, 2008.
[3] M.J.V. Vazquez, J.M.A. Marquez, and F.S. Manzano. “A Methodology for Optimizing Stand-Alone PV-System
Size Using Parallel-Connected DC/DC Converters”, IEEE Transactions on Industrial Electronics, Vol. 55. Pp.
2664–2673, 2008.
[4] Matlab/Simulink. Modeling A Buck Converter, Help Files Mathwork, 2008.
[5] Y M Chen, S C Hung. “Multi-Input Inverter for Grid Connected Hybrid PV/Wind Power System”, IEEE
Transactions on Industrial Electronics, Vol/Issue: 55(1). Pp. 850-856, 2007.
[6] J A Gow, C D Manning. “Development of a PV Array model for use in Power Electronics Simulation Studies”,
proceedings of the IEEE International Conference on Power Electronics Applications, Vol. 146. Pp. 193-200, 1999.
[7] Wenhua Zhu, Li Wang, LingluLuo. “A Modeling and Analysis of Output Features of the Solar Cells Based on
MATLAB/Simulink”, IEEE Conférence on Renewable Energy and Environment, Vol. 1, 2011.
[8] D.C. Riawan and C.V. Nayar. “Analysis and Design of a Solar Charge Controller Using Cuk Converter”, IEEE
Transactions on Industrial Electronics, Vol/Issue: 55(41), 2008.
[9] Bo-Yuan Chen, Yen-Shin Lai. “New Digital-Controlled Technique for Battery Charger with Constant Current and
Voltage Control without Current Feedback”, IEEE Transactions On Industrial Electronics, Vol/Issue: 59(3), 2012.
[10] Yaow-Ming, Yuan-Chuan Liu. “Multi-Input DC/DC Converter Based on the Multi winding Transformer for
Renewable Energy Applications”, IEEE Transactions on Industry Applications, Vol/Issue: 38(4), 2002.
[11] Chih-Lung Shen, Cheng-Tao, Tsai Yu-En, Wu Chun-Chuan Chen. “A Modified-Forward Multi-input Power
Converter for Solar Energy and Wind Power Generation”, IEEE Conference on Renewable Energy and
Environment. Pp. 631-636, 2009.
[12] M. E. Sahin, H. I Okumus. “Fuzzy Logic Controlled Synchronous Buck DC-DC Converter for Solar Energy-
Hydrogen Systems”, INISTA 2009 Conference, 2009.
[13] I. H. Altas and A. M. Sharaf. “A Generalized Direct Approach for Designing Fuzzy Logic Controllers in
Matlab/Simulink GUI Environment”, International Journal of Information Technology and Intelligent Computing ,
Vol/Issue: 1(4), 2007.
[14] M. E. Sahin. “Designing An Electrolyses System With Dc/Dc Buck Converter”, M.Sc. Thesis, Gazi University
Institute of Science and Technology, 2006.
[15] D.Dubios and H.Prade. “What are fuzzy rules and how to use them”, Fuzzy sets and system, Vol/Issue: 84(2). Pp.
169-185, 1996.](https://image.slidesharecdn.com/1316-959-1-pb-201208061715/85/Fuzzy-Logic-Controller-Based-Single-Buck-Boost-Converter-for-Solar-PV-Cell-7-320.jpg)
![ ISSN: 2252-8792
IJAPE Vol. 3, No. 1, April 2014 : 1 – 8
8
[16] T.P. Hong and Lee. “Induction of fuzzy rules and membership functions from training examples”, Fuzzy sets and
system, Vol/Issue: 84(1). Pp. 33-47, 1996.
[17] Tzu-Ping Wu and Shyi-Ming Chen. “A new method for constructing Membership functions and fuzzy rules from
training examples”, IEEE Transactions on systems and cybernetics-part B cybernetics, Vol/Issue: 29(1). Pp. 25-40,
1999.
[18] A. Bakhti and L. Benbaouche. “Simulink-DSP Co- Design of Fuzzy Logic Controller”, IEEE Explore, Pp. 4587-
4592, 2006.
BIOGRAPHY OF AUTHOR
K.Manickavasagam received the PhD from Madurai Kamaraj University, M.E.Degree
from Thiagarajar College of engineering, Madurai, Tamilnadu,
India.Currently he is working in Gopalan College of Engineering and
Management, Bangalore, Karnataka, India. His reseach interests includes
Power generation and control and artificial intelligence applications in power
Systems. He is a member of Institute of engineers (India) and life member of Indian
Society of Technical Education.](https://image.slidesharecdn.com/1316-959-1-pb-201208061715/85/Fuzzy-Logic-Controller-Based-Single-Buck-Boost-Converter-for-Solar-PV-Cell-8-320.jpg)
Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell
- 1. International Journal of Applied Power Engineering (IJAPE) Vol. 3, No. 1, April 2014, pp. 1~8 ISSN: 2252-8792 1 Journal homepage: http://iaesjournal.com/online/index.php/IJAPE Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell K. Manickavasagam Principal, Gopalan College of Engineering and Mangement, Bangalore, Karnataka Article Info ABSTRACT Article history: Received Apr 16, 2013 Revised Aug 27, 2013 Accepted Sep 14, 2013 This paper deals with solar power production controlled by Fuzzy Logic Controller (FLC) and Single Input Buck-Boost (SIBB) converter. Since the solar energy is continuously varying, according to the irradiation the FLC generates control pulses to switch on the MOSFET device. To analyze the real time feasibility of this method, the system is simulated by using MATLAB/Simulink 2010a. A simulation model of the system is developed with solar Photovoltaic (PV) cell, FLC and SIBB in contradiction of the real world conditions. The results are presented and discussed in this paper.Keyword: Fuzzy Logic Controller (FLC) Single Input Buck-Boost (SIBB) converter Copyright © 2014 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: K.Manickavasagam, Principal, Gopalan College of Engineering and Mangement, Bangalore, Karnataka, India, Mobile no: +91 0776090182. Email: manicavasagam2003@yahoo.com 1. INTRODUCTION Solar energy is continuously varying according to the climatic condition. The variation in irradiation affects the solar power produced by the panel. The solar power is considered as predominant resources when compared with others. In this field many researches are carrying on to improve the efficiency of solar power production. The performance of PV system can be enhanced by power converter with intelligent control techniques to develop the circuit model to improve the efficiency of solar power generation. MPPT algorithm is one powerful suggestion for stand-alone Solar systems to improve the efficiency. The application of Maximum Power Point Tracking in the PV module was developed [1]–[3] to achieve high performance in actual field. Modeling of buck converter using MATLAB is explained in [4]. Researchers are carried out in the solar cell for the understanding of the characteristic features and working scenarios [5]–[7]. The power converter designs for solar PV cells are given in [8]–[11]. The conventional controllers used are insufficient because of changes in operating points during a daily cycle and may no longer be suitable in all operating conditions. The use of intelligence controllers are cited in the most of the papers recently [12]-[14] which has faster transient responses and is more robust than several control method. The method of construction of fuzzy rules and their usage of membership functions are given in [15]-[17]. In this paper, a Fuzzy logic controller along with a single input buck-boost converter is proposed, which can deal with photovoltaic power individually based on their availability at that given instant. In conventional controller, the system or process being controlled is modeled. But in a fuzzy controller, the focus is on the human operator's judgment. Hence fuzzy logic provides a powerful representation and good results for measurements of uncertainties present in this problem. Since, the availability of sun light is
- 2. ISSN: 2252-8792 IJAPE Vol. 3, No. 1, April 2014 : 1 – 8 2 continuously varying; the fuzzy logic controller is used to generate the pulses for MOSFET device which is connected with single input buck-boost converter to generate required output. 2. MODELING OF THE SYSTEM Fig. 1 illustrates block diagram of the solar power generation system. The major circuit elements are PV cell, FLC, MOSFET, single input buck-boost converter, battery and load. The FLC generates the control signal depends on the availability of sun light, to achieve required voltage. The resource available with required power level is connected to the active system to supply the load and the mode of operation preferred will decide by the controller. Fig 1. Block diagram of the proposed system 3. PRINCIPLE OPERATION OF THE PROPOSED SYSTEM A schematic diagram of a single input buck-boost converter is given in Fig. 2. As shown, a buck- boost converter is nothing but cascade connection of the two basic converters: the step-down converter and step-up converter. The main application of such a converter is in regulated dc power supplies, where a negative polarity output may be desired with respect to the common terminal of the input voltage, and the output voltage can be either higher or lower than the input voltage [18]. The control signal generation, for the selection of available energy resource and the regulation of output voltage of the dc-dc converter is controlled by FLC. Fig 2. Single input Buck-Boost converter of the proposed system 4. DESIGN OF BUCK BOOST DC-DC CONVERTER The buck-boost converter circuit parameters of this proposed method is designed as follows. Some of the important circuit parameters are listed as follows: Input Power Pin : 50 Watts PV panel voltage : 12V Efficiency η : 0.9 Output Power Po : 45 Watts Output Current Io : 3.75 A Battery : 12V Switching frequency : 20 kHz Switching period T=1/f=Ton+Toff Regulated output obtained from the dc-dc converter is used to charge the battery and the connected load. The duty cycle D is assumed as 0.5. The minimum value of inductance (L) and capacitance (C) PV Cell MOSFET Single buck-boost converter Load Battery Gate Pulses Produced by FLC
- 3. IJAPE ISSN: 2252-8792 Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell (K. Manickavasagam) 3 L = R [1- D]2 / 2f (1) ΔVo/Vo = DTs / RC (2) Using equations (1) and (2) the minimum values of inductor and capacitor are calculated for the dc- dc converter and expressed below inductance (L) : 5mH capacitance (C) : 15µF 5. DESIGN OF FUZZY LOGIC CONTROLLER In this paper, the FLC is used to generate the pulses to drive the MOSFET. The PV cell’s output voltage (V) and change in output voltage (∆V) are chosen as an input to the fuzzy logic controller. The control output (u) signal is compared with triangular waveform and generated pulses are used to switch on the MOSFET. The input of FLC PV cell’s output voltage (V) and change in output voltage (∆V) are converted into fuzzy values by fuzzification. The ranges of input and output variables are assigned with linguistic variables. The Gauss membership function is used in this work. The input and output variables are assigned with 5 linguistic variables as follows: 1. The PV cell output voltage (V) is classified into: Negative maximum (V -vemax); Negative medium (V -vemed); Zero (V zero); Positive medium (V +vemed); Positive maximum (V +vemax) 2. The change in output voltage (∆V ) is classified into: Negative maximum (∆V -vemax); Negative medium (∆V -vemed); Zero (∆V zero); Positive medium (∆V +vemed); Positive maximum (∆V +vemax) 3. The output of fuzzy logic controller u is classified into: Negative maximum (u -vemax); Negative medium (u -vemed); Zero (u zero); positive medium (u +vemed); Positive maximum (u +vemax). The input variable V and ∆V lies within the range of [-0.0188 0.001] and [-0.009.0.0188]. The control output ‘u’ lies in the range of [0 0.0123]. These input and output ranges are used for designing the FLC, in which each of input and output set is assigned with five linguistic variables and 25 rules are framed in fuzzy inference engine. The rules are given in Fig .3. Fig .3 Picture of FLC rule base In this design, “Center of gravity Method” is used for defuzzification. It should be noted that for various rules (r =1…R) would be in operation for a set of (V, V), each recommending possibly different fuzzy controller actions. The defuzzified output is obtained by the following expression
- 4. ISSN: 2252-8792 IJAPE Vol. 3, No. 1, April 2014 : 1 – 8 4 R r r r R r r H H u 1 1 ' (5) Where µr’ is the membership value of the linguistic variable recommending the fuzzy controller action and Hr is the precise numerical value corresponding to that fuzzy controller action. 6. SIMULATION RESULTS The adaptability of this proposed method is studied by simulating the circuit model against the different possible real world situations using MATLAB/Simulink 2010a. The simulation diagram is shown in Fig .4. The solar irradiation is given to photo voltaic cell. The solar photovoltaic cell generates voltage based on the solar irradiation. Fig .4 Simulink model The solar irradiation is continuously changing because of weather and cloud. In this paper, the variation is chosen as shown in Fig .5. The voltage (V) from solar cell is given as one of the input to FLC and the change in voltage (∆V) is given as another input to FLC. The FLC output is compared with ramp signal to produce gate pulses. The width of the gate pulses is used to decide the duty cycle (d) of the MOSFET. The FLC output, ramp signal and trigger pulse generated are given in Fig 6.a, 6.b and 6.c. Fig .5 Solar irradiation –input
- 5. IJAPE ISSN: 2252-8792 Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell (K. Manickavasagam) 5 Fig .6.a Fig . 6.b Fig .6.c The input waveform of buck boost converter is shown in Fig. 7.a. The focused waveform is obtained in between 0.18 sec to 0.22 sec is shown in Fig .7.b for clear observation. From the Fig .7.b and Fig 5, it is observed that the input variation in solar irradiation is reflected in the output of solar PV cell. Fig .7.a
- 6. ISSN: 2252-8792 IJAPE Vol. 3, No. 1, April 2014 : 1 – 8 6 Fig .7.b The trigger pulses are driving the MOSFET. When power MOSFET is switched ON, the current will flow through the inductance. Hence inductor L stores energy during the Ton period. When the power MOSFET is switched OFF, the inductor current tends to decrease and as a result, the polarity of the emf induced in L is reversed. Thus the inductance energy discharges in the load. The output voltage waveform across load is shown in Fig .8.a and 8.b. The focused waveform is obtained in between 0.18 sec to 0.22 sec is shown in Fig .8.b for clear observation. The output current waveform across load is shown in Fig .9.a and 9.b. The focused waveform is obtained in between 0.18 sec to 0.22 sec is shown in Fig .9.b for clear observation. Fig .8.a Fig .8.b Fig .9.a
- 7. IJAPE ISSN: 2252-8792 Fuzzy Logic Controller Based Single Buck Boost Converter for Solar PV Cell (K. Manickavasagam) 7 Fig .9.b To analyze the performance of single buck boost converter, the step pulse is given as an input through the summation block to the FLC as shown in simulink model in Fig .4. From Fig .8 and Fig .9, it is observed that up to 0.2 sec the converter is in boost mode and after 0.2 sec, it is buck mode of operation. 7. CONCLUSION This paper presented an idea of solar PV system controlled by FLC with single input buck boost converter. The simulation model of the proposed system is developed and the results obtained under different conditions are discussed and presented in this paper. The variation in solar irradiation is compensated by FLC and single buck boost converter and the output voltage remains same irrespective of solar irradiation. The results proves that the validation and real time feasibility of the proposed new model. For the research purpose only single PV cell is considered in this study. There is a possibility to control the entire solar power plant using solar PV cells with the same method. REFERENCES [1] Rong-Jong Wai and Wen-Hung Wang. “High-Performance Stand- Alone Photovoltaic Generation System”, IEEE Transactions on Industrial Electronics, Vol/Issue: 55(1). Pp. 240-250, 2008. [2] R. Gules, J. De Pellegrin Pacheco, H. Leaes Hey and J. Imhoff. “A Maximum Power Point Tracking System with Parallel Connection for PV Stand-Alone Applications”, IEEE Transactions on Industrial Electronics, Vol/Issue: 55(7). Pp. 2674-2683, 2008. [3] M.J.V. Vazquez, J.M.A. Marquez, and F.S. Manzano. “A Methodology for Optimizing Stand-Alone PV-System Size Using Parallel-Connected DC/DC Converters”, IEEE Transactions on Industrial Electronics, Vol. 55. Pp. 2664–2673, 2008. [4] Matlab/Simulink. Modeling A Buck Converter, Help Files Mathwork, 2008. [5] Y M Chen, S C Hung. “Multi-Input Inverter for Grid Connected Hybrid PV/Wind Power System”, IEEE Transactions on Industrial Electronics, Vol/Issue: 55(1). Pp. 850-856, 2007. [6] J A Gow, C D Manning. “Development of a PV Array model for use in Power Electronics Simulation Studies”, proceedings of the IEEE International Conference on Power Electronics Applications, Vol. 146. Pp. 193-200, 1999. [7] Wenhua Zhu, Li Wang, LingluLuo. “A Modeling and Analysis of Output Features of the Solar Cells Based on MATLAB/Simulink”, IEEE Conférence on Renewable Energy and Environment, Vol. 1, 2011. [8] D.C. Riawan and C.V. Nayar. “Analysis and Design of a Solar Charge Controller Using Cuk Converter”, IEEE Transactions on Industrial Electronics, Vol/Issue: 55(41), 2008. [9] Bo-Yuan Chen, Yen-Shin Lai. “New Digital-Controlled Technique for Battery Charger with Constant Current and Voltage Control without Current Feedback”, IEEE Transactions On Industrial Electronics, Vol/Issue: 59(3), 2012. [10] Yaow-Ming, Yuan-Chuan Liu. “Multi-Input DC/DC Converter Based on the Multi winding Transformer for Renewable Energy Applications”, IEEE Transactions on Industry Applications, Vol/Issue: 38(4), 2002. [11] Chih-Lung Shen, Cheng-Tao, Tsai Yu-En, Wu Chun-Chuan Chen. “A Modified-Forward Multi-input Power Converter for Solar Energy and Wind Power Generation”, IEEE Conference on Renewable Energy and Environment. Pp. 631-636, 2009. [12] M. E. Sahin, H. I Okumus. “Fuzzy Logic Controlled Synchronous Buck DC-DC Converter for Solar Energy- Hydrogen Systems”, INISTA 2009 Conference, 2009. [13] I. H. Altas and A. M. Sharaf. “A Generalized Direct Approach for Designing Fuzzy Logic Controllers in Matlab/Simulink GUI Environment”, International Journal of Information Technology and Intelligent Computing , Vol/Issue: 1(4), 2007. [14] M. E. Sahin. “Designing An Electrolyses System With Dc/Dc Buck Converter”, M.Sc. Thesis, Gazi University Institute of Science and Technology, 2006. [15] D.Dubios and H.Prade. “What are fuzzy rules and how to use them”, Fuzzy sets and system, Vol/Issue: 84(2). Pp. 169-185, 1996.
- 8. ISSN: 2252-8792 IJAPE Vol. 3, No. 1, April 2014 : 1 – 8 8 [16] T.P. Hong and Lee. “Induction of fuzzy rules and membership functions from training examples”, Fuzzy sets and system, Vol/Issue: 84(1). Pp. 33-47, 1996. [17] Tzu-Ping Wu and Shyi-Ming Chen. “A new method for constructing Membership functions and fuzzy rules from training examples”, IEEE Transactions on systems and cybernetics-part B cybernetics, Vol/Issue: 29(1). Pp. 25-40, 1999. [18] A. Bakhti and L. Benbaouche. “Simulink-DSP Co- Design of Fuzzy Logic Controller”, IEEE Explore, Pp. 4587- 4592, 2006. BIOGRAPHY OF AUTHOR K.Manickavasagam received the PhD from Madurai Kamaraj University, M.E.Degree from Thiagarajar College of engineering, Madurai, Tamilnadu, India.Currently he is working in Gopalan College of Engineering and Management, Bangalore, Karnataka, India. His reseach interests includes Power generation and control and artificial intelligence applications in power Systems. He is a member of Institute of engineers (India) and life member of Indian Society of Technical Education.