PV module optimum operation modeling

• Autorzy: Shahat A. E.
• Języki publikacji: EN

Abstrakty

EN

This paper proposes the identification of maximum power point (MPP) function for photovoltaic (PV) module using the genetic algorithm (GA). Then deduction of the required function to generate the reference values to drive the tracking system in the PV system at MPP is done with the aid of Artificial Neural Network (ANN). This function deals with the more probable situations for variable values of temperature and irradiance to get the corresponding voltage and current at maximum power. The mathematical PV module modelling depends on Schott ASE-300-DGF PV panel with the aid of MATLAB environment. The aim of this paper is to pick peaks of the power curves (maximum points). The simulation results at MPP are well depicted in 3-D figures to be used as training or learning data for the ANN model.

Słowa kluczowe

EN

photovoltaics; neural network; maximum power; genetic algorithm; MATLAB

PL

fotowoltaika; sieci neuronowe; moc maksymalna; algorytm genetyczny; MATLAB

• Wydawca: Institute of Heat Engineering, Warsaw University of Technology
• Czasopismo: Journal of Power Technologies
• Rocznik: 2014
• Tom: Vol. 94, nr 1
• Strony: 50--66
• Opis fizyczny: Bibliogr. 56 poz., rys., wykr.

Twórcy

autor

Shahat A. E.

• Engineering Science Department, Faculty of Petroleum & Mining Engineering Suez University, Egypt

Bibliografia

• [1] [s.n.], nTrends in photovoltaic applications. survey report of selected iea countries between 1992 and 2006.
• [2] T. Markvart, L. Castaner, Practical Handbook of Photovoltaics, Fundamentals and Applications, Elsevier, 2003.
• [3] A. El Shahat, Dc-dc converter duty cycle ann estimation for dg applications, Journal of Electrical Systems 9 (1) (2013) 13–38.
• [4] A. El Shahat, Photovoltaic power system simulation for micro – grid distribution generation, in: 8th International Conference on Electrical Engineering (ICEENG 2012), Military Technical College, Egypt, 2012.
• [5] A. Chatterjee, A. Keyhani, D. Kapoor, Identification of photovoltaic source models, IEEE Transactions on Energy Conversion 26 (3) (2011) 883–889.
• [6] A. Keyhani, Modeling of photovoltaic microgrids for bulk power grid studies, in: IEEE Power & Energy Society General Meeting, Detroit, Michigan, 2011.
• [7] K. Hussein, I. Muta, T. Hoshino, M. Osakada, Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions, Generation, Transmission and Distribution, IEE Proceedings- 142 (1995) 59–64.
• [8] D. Hohm, M. Ropp, Comparative study of maximum power point tracking algorithms using an experimental, programmable, maximum power point tracking test bed, in: Photovoltaic Specialists Conference, 2000. Conference Record of the Twenty-Eighth IEEE, 2000, pp. 1699–1702.
• [9] D. Sera, R. Teodorescu, P. Rodriguez, Pv panel model based on datasheet values, in: Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on, 2007, pp. 2392–2396.
• [10] D. P. Hohm, M. E. Ropp, Comparative study of maximum power point tracking algorithms, Progress in Photovoltaics: Research and Applications 11 (2003) 47–62.
• [11] S. Premrudeepreechacharn, N. Patanapirom, Solar-array modelling and maximum power point tracking using neural networks, in: IEEE Bologna Power Tech Conference, Bologna, Italy, 2003.
• [12] L. Zhang, Y. F. Bai, Genetic algorithm-trained radial basis function neural networks for modelling photovoltaic panels, Engineering Applications of Artificial Intelligence Journal 18 (7) (2005) 833–844.
• [13] H. Mekki, A. Mellit, H. Salhi, K. Belhout, Modeling and simulation of photovoltaic panel based on artificial neural networks and vhdl-language, in: 4th International Conference on Computer Integrated Manufacturing CIP’2007, 2007.
• [14] M. G. Villalva, J. R. Gazoli, E. R. Filho, Comprehensive approach to modeling and simulation of photovoltaic arrays, Power Electronics, IEEE Transactions on 24 (5) (2009) 1198–1208.
• [15] S. I. Sulaiman, T. K. Abdul Rahman, I. Musirin, S. Shaari, Optimizing three-layer neural network model for gridconnected photovoltaic system output prediction, in: Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA 2009), 2009, pp. 338–343.
• [16] F.-M. Petcut, T. Leonida-Dragomir, Solar cell parameter identification using genetic algorithms, Control Engineering and Applied Informatics 12 (1) (2010) 30–37.
• [17] D. M. Riley, G. K. Venayagamoorthy, Comparison of a recurrent neural network pv system model with a traditional component – based pv system model, in: 37th IEEE Photovoltaic Specialists Conference, Seattle, WA, USA, 2011.
• [18] S. Hadji, J.-P. Gaubert, F. Krim, Genetic algorithms for maximum power point tracking in photovoltaic systems, in: Proceedings of the 2011-14th European Conference Power Electronics and Applications (EPE 2011), Birmingham, 2011.
• [19] Y. P. Chang, Optimal tilt angle for pv modules using the neural-genetic algorithm considering mathematical model of the solar orbit and position, in: Advanced Materials Research, 2012, pp. 250–253.
• [20] M. Hadjab, S. Berrah, H. Abid, Neural network for modeling solar panel, International Journal of Energy 6 (1) (2012) 9–16.
• [21] J. L. Gao, Modeling of photovoltaic cell based on bp neural networks improved by mea, Applied Mechanics and Materials 217–219 (2012) 809–814.
• [22] F. Bonanno, G. Capizzi, C. Napoli, G. Graditi, G. M. Tina, A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module, Applied Energy Journal 97 (2012) 956–961.
• [23] H. Ravaee, S. Farahat, F. Sarhaddi, Artificial neural network based model of photovoltaic thermal (pv/t) collector, The Journal of Mathematics and Computer Science 4 (3) (2012) 411–417.
• [24] J. K. Maherchandani, C. Agarwal, M. Sahi, Estimation of solar cell model parameter by hybrid genetic algorithm using matlab, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 1 (6) (2012) 78–81.
• [25] D. Niu, Y. Wei, Y. Chen, Photovoltaic power prediction based on scene simulation knowledge mining and a neural network, Hindawi Publishing Corporation, Mathematical Problems in Engineering Journal, Article ID 260351 2013 (2013) 6 pages.
• [26] C. Gallo, M. De Bonis, A neural network model for forecasting photovoltaic deployment in italy, International Journal of Sustainable Energy and Environment 1 (1) (2013) 1–13.
• [27] G. Venkateswarlu, P. S. Raju, Modeling and parameter extraction of pv modules using genetic algorithms and differential evaluation, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) 5 (6) (2013) 37–44.
• [28] A. Shahsavar, P. Talebizadeh, H. Tabaei, Optimization with genetic algorithm of a pv/t air collector with natural air flow and a case study, Journal of Renewable and Sustainable Energy 5 (2013) 023118. doi:10.1063/1.4798312. [29] N. Ngu, H. Wang, L. Tam, Comparison of radial basis function neural network and back propagation neural network in controller for photovoltaic-array modeling and maximum power point tracking, Advanced Science Letters 19 (10) (2013) 3047–3050.
• [30] M. S. Ismail, M. Moghavvemi, T. M. I. Mahlia, Characterization of pv panel and global optimization of its model parameters using genetic algorithm, Energy Conversion and Management 73 (9) (2013) 10–25. doi:10.1016/j.enconman.2013.03.033.
• [31] A. Tofighi, Performance evaluation of pv module by dynamic thermal model, Journal of Power Technologies 93 (2) (2013) 111–121.
• [32] J. M. Lopez-Guede, J. A. Ramos-Hernanz, M. Grana, Artificial neural network modeling of a photovoltaic module, in: International Joint Conference SOCO’13-CISIS’13- ICEUTE’13, Vol. 239 of Advances in Intelligent Systems and Computing, 2014, pp. 389–397.
• [33] Schott ase-300-dgf pv panel data sheet, Affordable Solar website. URL http://www.affordable-solar.com/admin/product_doc/Doc_pd-00-009-c_ase_300_20080328114646.pdf
• [34] A. Keyhani, M. N. Marwali, M. Dai, Integration of Green and Renewable Energy in Electric Power Systems, Wiley,2010.
• [35] G. M. Masters, Renewable and E_cient Electric Power Systems, John Wiley & Sons Ltd, 2004.
• [36] A. Keyhani, Design of Smart Power Grid Renewable Energy Systems, John Wiley &Son, Inc. and IEEE Publication, 2011.
• [37] R. A. Messenger, J. Ventre, Photovoltaic Systems Engineering, 2nd Edition, CRC Press, 2003.
• [38] L. Castaner, S. Silvestre, Modelling Photovoltaic Systems Using PSpice, John Wiley & Sons Ltd, 2002.
• [39] M. A. Green, Solar Cells: Operating Principles, Technology, and System Applications, Prentice Hall Inc., 1982.
• [40] G. R.Walker, Evaluating mppt converter topologies using a matlab pv model, in: Australasian Universities Power Engineering Conference, AUPEC’00, Brisbane, 2000.
• [41] A. El Shahat, H. El Shewy, Pm synchronous motor control strategies with their neural network regression functions, Journal of Electrical Systems 5 (4).
• [42] S. A. Kalogirou, Optimization of solar systems using neural-networks and genetic algorithms, Appl Energy 77 (4) (2004) 383–405.
• [43] S. A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passivesolar building, Energy 25 (2000) 479–91.
• [44] A. El Shahat, H. El Shewy, High speed pm synchronous motor basic sizing neural regression function for renewable energy applications, in: Paper ID: X304, 2nd International Conference on Computer and Electrical Engineering (ICCEE 2009), Dubai, UAE, 2009.
• [45] A. El Shahat, Generating basic sizing design regression neural function for hspmsm in aircraft, in: EP-127, 13th International Conference on Aerospace Science & Aviation Technology (ASAT 2009), Military Technical College, Cairo, Egypt, 2009.
• [46] A. El Shahat, H. El Shewy, Neural unit for pm synchronous machine performance improvement used for renewable energy, in: The Third Ain Shams University International Conference on Environmental Engineering (ASCEE-3), Cairo, Egypt, 2009.
• [47] A. El Shahat, H. El Shewy, Neural unit for pm synchronous machine performance improvement used for renewable energy, in: Global Conference on Renewable and Energy Efficiency for Desert Regions (GCREEDER2009), Amman, Jordan, 2009.
• [48] A. R. Conn, N. I. M. Gould, P. L. Toint, A globally convergent augmented lagrangian algorithm for optimization with general constraints and simple bounds, SIAM Journal on Numerical Analysis 28 (2) (1991) 545–572.
• [49] A. R. Conn, N. I. M. Gould, P. L. Toint, A globally convergent augmented lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds, Mathematics of Computation 66 (217) (1997) 261– 288.
• [50] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, 1989.
• [51] G. Rudolph, Convergence analysis of canonical genetic algorithms, Neural Networks, IEEE Transactions on 5 (1) (1994) 96–101.
• [52] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, Univ. of Michigan Press, 1995.
• [53] G. Cvetkovski, L. Petkovska, M. Cundev, S. Gair, Mathematical model of a permanent magnet axial field synchronous motor for ga optimisation, in: Proc. ICEM’98, Vol. 2/3, 1998, pp. 1172–1177.
• [54] A. El Shahat, A. Keyhani, H. El Shewy, Spacecraft flywheel high speed pm synchronous motor design (classical & genetic), Journal of Theoretical and Applied Information Technology 13 (1) (2010) 83–100.
• [55] A. El Shahat, H. El Shewy, Pm synchronous motor genetic algorithm performance improvement for renewable energy applications, in: MDGEN11, Accepted in the International Conference on Millennium Development Goals (MDG): Role of ICT and other technologies, Chennai, India, 2009.
• [56] A. El Shahat, H. El Shewy, Pm synchronous motor genetic algorithm performance improvement for green energy applications, in: Paper ID: X792, Accepted in 2nd International Conference on Computer and Electrical Engineering (ICCEE 2009), Dubai, UAE, 2009.