The conventional energy resources are exhausting rapidly to meet the ever increasing demand of exponentially rising global population. In this present scenario researchers are finding out the way to slow down the quick depletion of earth energy and this effort has opened up the era of green energy or non-conventional energy or renewable energy. There are various types of renewable source of energy like wind energy, solar energy, tidal energy etc. Among these non-conventional energy resources the Sun is the foremost resource of the renewable energies. Solar energy has become popular across the world from rural area to country side because it is abundant in nature, requires little maintenance, and causes no noise and pollution. There are various ways to convert solar energy into electric energy such as solar thermal, solar pond, photovoltaic effect. Solar cell is a device which converts solar energy into electric energy using photovoltaic effect and it is represented by a current source in parallel with a diode. Assemble of photovoltaic cell construct the solar module. Although solar cell was expensive at the beginning, it is considered that solar power systems can compete with the fossil fuel systems due to the development of the semiconductor technology and manufacturing process. The installation cost of solar system is high. Moreover the power-voltage characteristic is non-linear in nature and drifts with temperature, irradiation, building material of solar cell and delivers maximum power output at a specific operating point called maximum power point. So under this fast varying operating condition a fast converging Maximum Power Point Tracking Technique (MPPT) is required to ensure minimum power losses. Till date a number of MPPT has been proposed and implemented to optimize the cost of generation of solar electricity. These MPPT techniques vary in terms of implementation complexity, speed of operation, convergence under abrupt atmospheric condition, efficiency or accuracy. In this paper Biogeography Based Optimization algorithm has been simulated to obtain Maximum Power Point on PV characteristic.
(size: 417.62 kB, 202-207
, Download times:
F. Kreith and D. Y. Goswami, 2007. Handbook of Energy Efficiency and Renewable Energy. CRC Press, Taylor & Francis Group.
M. Ameli, S. Moslehpour, and M. Shamlo, 2008. Economical load distribution in power networks that include hybrid solar power plants, Elect. Power Syst. Res., vol. 78, no. 7, pp. 1147–1152.
C. Jaen, C. Moyano, X. Santacruz, J. Pou, A. Arias, Techniques Used In Photovoltaic Systems, Electronic Engineering Department, Technical University of Catalonia, UPC Terrassa, Spain.
 V. Salas, E. Olias, A. Barrado, A. Lazaro, 2006. Review of the maximum power point tracking algorithm for stand-alone photovoltaic system, Solar Energy Mater. Solar Cells, vol. 90, no. 11, pp. 1555–1578.
T. Esram and P. L. Chapman, 2007. Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. Energy Conv., vol. 22, no. 2, pp. 439–449, Jun.
J. A. Jiang, T. L. Huang, Y. T. Hsiao, and C.-H. Chen, 2005. Maximum power tracking for photovoltaic power systems, Tamkang J. Sci. Eng., vol. 8, no. 2, pp. 147–153.
Md. A. S. Masoum, H. Dehbonei and Ewald, 2002. Theoretical and Experimental Analyses of Photovoltaic Systems With Voltage- Current Based Maximum Power-Point Tracking, IEEE Transaction on Energy Conversion, vol. 17, no. 4, December.
W. Xiao and W. G. Dunford, 2004. A modified adaptive hill climbing MPPT method for photovoltaic power systems, in Proc. 35th Annu. IEEE Power Electron. Spec. Conf., pp. 1957–1963.
N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, 2005. Optimization of perturb and observe maximum power point tracking method, IEEE Trans. Power Electron., vol. 20, no. 4, pp. 963–973, Jul.
C. Hua and J. R. Lin, 1996. DSP-Based Controller Application in Battery Storage of Photovoltaic System, Industrial Electronics Control and Instrumentation, IEEE.
A. Tariq, J. Asghar, 2006. Development of microcontroller-based maximum power point tracker for a photovoltaic panel, Power India Conference, IEEE.
J. Zhang, F. Guo 2009. The Study in Photovoltaic Control System Based on FPGA, International Conference on Research Challenges in Computer Science.
D. Das. 2012. FPGA based implementation of MPPT of Solar Cell, NCCCS, IEEE.
S. Jain and V. Agarwal, 2007. A single-stage grid connected inverter topology for solar PV systems with maximum power point tracking, IEEE Trans. Power Electron., vol. 22, no. 5, pp. 1928–1940, Sep.
Q. Mei, M. Shan, L. Liu, and J. M. Guerrero, 2011. A Novel Improved Variable Step Size Incremental Resistance MPPT Method for PV Systems, IEEE Industrial Electronics, vol. 58, no. 6, pp. 2427-2434, JUN.
J. Young-Hyok, J. Doo-Yong, K. Jun-Gu, K. Jae-Hyung, L. Tae-Won, and W. Chung-Yuen, A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions.
IEEE Trans. 2011. Power Electron., vol. 26, no. 4, pp. 1001–1009, Apr.
A. Safari and S. Mekhilef, 2011. Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter, IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1154–1161, Apr.
K. Ishaque and Z. Salam, 2011. An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE), Solar Energy, vol. 85, pp. 2349–2359.
A. E. Eiben and C.A. Schippers, 1998. On evolutionary exploration and exploitation, Fundamenta Informaticate, vol. 35, no. 1-4, pp. 35-50.
M. Miyatake, F. Toriumi, T. Endo, and N. Fujii, 2007. A Novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique,” in Proc. Eur. Conf. Power Electron. Appl., pp. 1–10.
V. Phimmasone,Y.Kondo, T. Kamejima, and M.Miyatake, 2010. Evaluation of extracted energy from PV with PSO-based MPPT against various types of solar irradiation changes, presented at the Int. Conf. Electrical Machines and Systems, Incheon, Korea.
E. Rashedi, H. 2009. Nezamabadi-pour, S. Saryazdi, “GSA: A gravitational search algorithm,” Information Sciences, vol. 179, 2009, pp. 2232-2248.
S. Mirjalili and S. Z. M. Hashim, 2010. A new hybrid PSOGSA algorithm for function optimization, in proc. of ICCIA, IEEE, pp. 347-377.
B. Shaw, V. Mukherjee, S. P. Ghoshal, 2012. A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power system, International Journal of Electircal & Energy System, vol. 35, Feb, pp. 21-33.
C. Wang, K. Z. Gao, J. Guo, 2013. An improved gravitational algorithm based on neighbour search, in proc. of ICNC, IEEE, pp. 681-685.
A. Wallace, 2005. The Geographical Distribution of Animals (Two Volumes). Boston, MA: Adamant Media Corporation,
C. Darwin, 1995. The Origin of Species. New York: Gramercy.
R. MacArthur and E. Wilson, 1967. The Theory of Biogeography. Princeton, NJ: Princeton Univ. Press.