Abstract
Parameter estimation (PE) is one of the most challenging problems in photovoltaic (PV) system modeling. Owing to the ability to handle nonlinear functions regardless of the derivatives information, meta-heuristics have attracted many researchers. Recently, many implementations of particle swarm optimization (PSO) based PE method have been proposed in the literature. However, these algorithms utilize multiple agents or particles in the search process, and are normally compute intensive. In this paper, we describe our implementation of PSO on graphic processing units (GPUs) using open computing language (OpenCL). The proposed method has been specifically designed and entirely executed on the GPUs to provide a reduction of computational costs. Results show that the GPU-based PE is faster in comparison with its sequential implementation of PSO, and this proves the efficacy of the GPU framework.
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References
DOE, U.S.: Annual Energy Review 2011. Energy Information Administration (EIA) (2012)
Villalva, M.G., Gazoli, J.R., Filho, E.R.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24, 1198–1208 (2009)
Jung, J.-H., Ahmed, S.: Real-time simulation model development of single crystalline photovoltaic panels using fast computation methods. Sol. Energy 86, 1826–1837 (2012)
Siddiqui, M.U., Abido, M.: Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms. Appl. Soft Comput. 13, 4608–4621 (2013)
Beck, J.V., Arnold, K.J.: Parameter estimation in engineering and science parameter. Wiley Series in Probability and Mathematical Statistics, New York (1977)
Ishaque, K., Salam, Z., Taheri, H., Shamsudin, A.: A critical evaluation of EA computational methods for photovoltaic cell parameter extraction based on two diode model. Sol. Energy 85, 1768–1779 (2011)
Ishaque, K., Salam, Z., Mekhilef, S., Shamsudin, A.: Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl. Energy 99, 297–308 (2012)
Nishioka, K., Sakitani, N., Uraoka, Y., Fuyuki, T.: Analysis of multicrystalline silicon solar cells by modified 3-diode equivalent circuit model taking leakage current through periphery into consideration. Solar Energy Mater. Solar Cells 91, 1222–1227 (2007)
Gottschalg, R., Rommel, M., Infield, D.G., Kearney, M.J.: The influence of the measurement environment on the accuracy of the extraction of the physical parameters of solar cells. Meas. Sci. Technol. 10, 796 (1999)
Mullejans, H., Hyvarinen, J., Karila, J., Dunlop, E.D.: Reliability of the routine 2-diode model fitting of PV modules. In: 19th European Photovoltaic Solar Energy Conference, pp. 2459 (2004)
Yordanov, G., Midtgrd, O., Saetre, T.: Two-diode model revisited: parameters extraction from semi-log plots of IV data. In: 25th European Photovoltaic Solar Energy Conference (2010)
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithm. Luniver Press, Bristol (2010)
Joseph, A.J., Hadj, B., Ali, A.-L.: Solar cell parameter extraction using genetic algorithms. Meas. Sci. Technol. 12, 1922 (2001)
Huang, W., Jiang, C., Xue, L., Song, D.: Extracting solar cell model parameters based on chaos particle swarm algorithm. In: 2011 International Conference on Electric Information and Control Engineering, pp. 398–402 (2011)
Ye, M., Wang, X., Xu, Y.: Parameter extraction of solar cells using particle swarm optimization. J. Appl. Phys. 105, 094502–094508 (2009)
Rajasekar, N., Krishna Kumar, N., Venugopalan, R.: Bacterial foraging algorithm based solar PV parameter estimation. Sol. Energy 97, 255–265 (2013)
El-Naggar, K.M., AlRashidi, M.R., AlHajri, M.F., Al-Othman, A.K.: Simulated annealing algorithm for photovoltaic parameters identification. Sol. Energy 86, 266–274 (2012)
AlHajri, M.F., El-Naggar, K.M., AlRashidi, M.R., Al-Othman, A.K.: Optimal extraction of solar cell parameters using pattern search. Renew. Energy 44, 238–245 (2012)
da Costa, W.T., Fardin, J.F., Simonetti, D.S.L., Neto, L.d.V.B.M.: Identification of photovoltaic model parameters by differential evolution. In: 2010 IEEE International Conference on Industrial Technology (ICIT), pp. 931–936 (2010)
Gaster, B., Howes, L., Kaeli, D.R., Mistry, P., Schaa, D.: Heterogeneous Computing with OpenCL. Elsevier, Waltham (2012)
Ma, J., Ting, T.O., Man, K.L., Zhang, N., Guan, S.-U., Wong, P.W.H.: Parameter estimation of photovoltaic models via cuckoo search. J. Appl. Math. 2013, 8 (2013)
Eberhart R.C., Yuhui S.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 81, pp. 81–86. IEEE Press (2001)
Ting, T.O., Man, K.L., Guan, S.-U., Nayel, M., Wan, K.: Weightless swarm algorithm (WSA) for dynamic optimization problems. In: Park, J.J., Zomaya, A., Yeo, S.-S., Sahni, S. (eds.) NPC 2012. LNCS, vol. 7513, pp. 508–515. Springer, Heidelberg (2012)
Easwarakhanthan, T., Bottin, J., Bouhouch, I., Boutrit, C.: Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Solar Energy 4, 1–12 (1986)
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Ma, J., Ting, T.O., Wen, H., Fu, B., Ban, J. (2015). GPU-Based Parameter Estimation Method for Photovoltaic Electrical Models. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_29
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DOI: https://doi.org/10.1007/978-3-319-23862-3_29
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