Page 281 - Advances in Renewable Energies and Power Technologies

254 CHAPTER 7 Strategies for Fault Detection and Diagnosis

[55] J. Guerrero-Perez, E. De Jodar, E. Go ´mez-La ´zaro, A. Molina-Garcia, Behavioral
modeling of grid-connected photovoltaic inverters: development and assessment,
Renew. Energy 68 (2014) 686e696.
[56] M. Jantsch, H. Schimidt, J. Schmid, Results of the concerted action on power condition-
ing and control, in: Proceedings of the 11th European Photovoltaic Solar Energy Con-
¸
ference, Montreux, Suica, 1992, pp. 1589e1593.
[57] D.L. King, S. Gonzalez, G.M. Galbraith, W.E. Boyson, Performance Model for Grid-
connected Photovoltaic Inverters, Sandia National Laboratories, Tech. Rep, 2007.
[58] M. Zagrouba, A. Sellami, M. Bouaı ¨cha, M. Ksouri, Identification of PV solar cells and
modules parameters using the genetic algorithms: application to maximum power
extraction, Sol. Energy 84 (2010) 860e866.
[59] M. Ye, X. Wang, Y. Xu, Parameter extraction of solar cells using particle swarm
optimization, J. Appl. Phys. (2009) 105.
[60] M.H. Ali, A. Rabhi, A. El Hajjaji, G.M. Tina, Real time fault detection in photovoltaic
systems, Energy Procedia 111 (2017) 914e923.
[61] K.M. El-Naggar, M.R. AlRashidi, M.F. AlHajri, A.K. Al-Othman, Simulated Annealing
algorithm for photovoltaic parameters identification, Sol. Energy 86 (2012) 266e274.
[62] A. Askarzadeh, A. Rezazadeh, Parameter identification for solar cell models using har-
mony search-based algorithms, Sol. Energy 86 (2012) 3241e3249.
[63] M.F. AlHajri, K.M. El-Naggar, M.R. AlRashidi, Al-Othman a. K. Optimal extraction of
solar cell parameters using pattern search, Renew. Energy 44 (2012) 238e245, https://
doi.org/10.1016/j.renene.2012.01.082.
[64] K. Ishaque, Z. Salam, S. Mekhilef, A. Shamsudin, Parameter extraction of solar photo-
voltaic modules using penalty-based differential evolution, Appl. Energy 99 (2012)
297e308, https://doi.org/10.1016/j.apenergy.2012.05.017.
[65] D. Oliva, E. Cuevas, G. Pajares, Parameter identification of solar cells using artificial
bee colony optimization, Energy 72 (2014) 93e102.
[66] E. Garoudja, K. Kara, A. Chouder, S. Silvestre, Parameters extraction of photovoltaic
module for long-term prediction using artifical bee colony optimization, in: 3rd Int
Conf Control Eng Inf Technol, 2015, pp. 1e6.
[67] E.E. Ali, M.A. El-Hameed, A.A. El-Fergany, M.M. El-Arini, Parameter extraction of
photovoltaic generating units using multi-verse optimizer, Sustain. Energy Technol.
Assess. 17 (2016) 68e76.
[68] S. Kichou, S. Silvestre, L. Guglielminotti, L. Mora-Lo ´pez, E. Mun ˜oz-Cero ´n, Compar-
ison of two PVarray models for the simulation of PV systems using five different algo-
rithms for the parameters identification, Renew. Energy 99 (2016) 270e279.
[69] http://www.orcad.com/products/orcad-pspice-designer/overview.
[70] A. Saha, N.N. Nipu, M.F. Khan, PSpice based study of environmental effect on the per-
formance of the solar PV module, in: Development in the in Renewable Energy Tech-
nology (ICDRET), IEEE, January 2016, pp. 1e6, 2016 4th International Conference on
the.
[71] S. Silvestre, A. Boronat, A. Chouder, Study of bypass diodes configuration on PV
modules, Appl. Energy 86 (9) (2009) 1632e1640.
[72] A. Moreno, J. Julve, S. Silvestre, L. Castan ˜er, SPICE macromodeling of photovoltaic
systems, Prog. Photovoltaics Res. Appl. 8 (3) (2000) 293e306.
[73] http://sine.ni.com/nips/cds/view/p/lang/en/nid/212669.
[74] H. Rezk, I. Tyukhov, M. Al-Dhaifallah, A. Tikhonov, Performance of data acquisition
system for monitoring PV system parameters, Measurement 104 (2017) 204e211.