Abstract
This chapter presents the design and optimization of a fuzzy logic controller (FLC) with a minimum rule base for maximum power point tracking in photovoltaic (PV) systems. A strategy for automated design and optimization of the FLC using genetic algorithms is proposed. An optimal Takagi-Sugeno FLC with a rule base of only 9-rules is realized and compared to the conventional design of 49 or 25 rules. Two FLCs, one using Gaussian input membership functions (MFs) and the other using trapezoidal MFs are designed and their performance compared. Expert knowledge for tuning the FLC is extracted from a PV module model under varying solar radiation, temperature, and load conditions. The proposed method is implemented using C language as a dynamic linked library (.dll format) and simulated using LabVIEW. Simulation results are used to compare the performance of the optimized FLCs in terms of speed, accuracy, and robustness. It is shown that the optimization algorithm produces an optimal FLC for both Gaussian and trapezoidal MFs.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baghat, A.B.G., et al.: Maximum power point tracking controller for PV systems using neural networks. Renewable Energy 30(2005), 1257–1268 (2004)
Bose, B.K.: Modern Power Electronics and AC Drives. Prentice-Hall, Englewood Cliffs (2001)
BP Solar Global Marketing, BP Solar SX 75TU PV module data sheet (2002), http://www.solardepot.com/
Hua, C., Shen, C.: Comparative study of peak power tracking techniques for solar storage systems, in Proc. IEEE Appl. In: Power Electron. Conf. and Expo., vol. 2, pp. 676–683 (February 1998)
Sullivan, C.R., Powers, M.J.: A high-efficiency maximum power point trackers for photovoltaic array in a solar-powered race vehicle. In: Proc. IEEE PESC, pp. 574–580 (1993)
Otieno, C.A., Nyakoe, G.N., Wekesa, C.W.: A Neural Fuzzy Based Maximum Power PointTracker for a Photovoltaic System. In: IEEE Africon, pp. 1–6 (September 2009)
Hohm, D.P., Ropp, M.E.: Comparative Study of Maximum Power Point Tracking Algorithms. Progress in Photovoltaics: Research and Applications 11, 47–62 (2003), doi:10.1002/pip.459
Koutroulis, E., Kalaitzakis, K., Voulgaris, N.C.: Development of a Microcontroller Based Photovoltaic Maximum Power Point Tracking Control System. IEEE Transactions on Power Electronics 16(1) (2001)
Manwell, J.F., et al.: Hybrid2 - A hybrid system simulation model theory manual (2006), http://ceere.org/rerl/projects/software/hybrid2/Hy2theorymanual.pdf
Enslin, J.H.R., Snyman, D.B.: Simplified feed-forward control of the maximum power point tracker for photovoltaic applications. In: Proc. Int. Conf. IEEE Power Electron. Motion Control, vol. 1, pp. 548–553 (1992)
Enslin, J.R., Wolf, M.S., Snyman, D.B., Sweigers, W.: Integrated photovoltaic maximum power point tracking converter. IEEE Trans. Ind. Electron 44(6), 769–773 (1997)
Bodur, M., Ermis, M.: Maximum power point tracking for low power photovoltaic solar panels. In: Proc. IEEE Electro Tech. Conf., vol. 2, pp. 758–761 (1992)
Veerachary, M., Senjyu, T., Uezato, K.: Feed-forwardmaximum power point tracking of PV systems using fuzzy controller. IEEE Trans. Aerosp. Electron. Syst. 38(3), 969–981 (2002)
Khaehintung, N., Kunakorn, A., Sirisuk, P.: A Novel Fuzzy Logic Control Technique tuned by Particle Swarm Optimization for Maximum Power Point Tracking for a Photovoltaic System using a Current-mode Boost Converter with Bifurcation Control. International Journal of Control, Automation, and Systems 8(2), 289–300 (2010), http://www.springer.com/12555 , doi:10.1007/s12555-010-0215-7.
Erickson, R.W., Maksimovic, D.: Fundamentals of Power Electronics, 2nd edn. Kluwer Academic Publishers, Dordrecht (2001)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, Inc., Hoboken (2004)
Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)
Noguchi, T., Togashi, S., Nakamoto, R.: Short-current pulse-based maximum power point tracking method for multiple photovoltaic-and converter module system. IEEE Trans. Ind. Electron. 49(1), 217–223 (2002)
Ocran, T.A., et al.: Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle. Tsinghua Science & Technology 10(2), 204–208 (2005)
Kohata, Y., Yamauchi, K., Kurihara, M.: Quick Maximum Power Point Tracking of Photovoltaic Using Online Learning Neural Network. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5863, pp. 606–613. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Letting, L.K., Munda, J.L., Hamam, Y. (2011). Optimization of Fuzzy Logic Controller Design for Maximum Power Point Tracking in Photovoltaic Systems. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-22176-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22175-0
Online ISBN: 978-3-642-22176-7
eBook Packages: EngineeringEngineering (R0)