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252    CHAPTER 7 Strategies for Fault Detection and Diagnosis




                         [19] M.P. Almeida, O. Perpin ˜a ´n, L. Narvarte, PV power forecast using a nonparametric PV
                             model, Sol. Energy 115 (2015) 354e368.
                         [20] R. Marquez, C.F.M. Coimbra, Forecasting of global and direct solar irradiance using
                             stochastic learning methods, ground experiments and the NWS database, Sol. Energy
                             85 (2011) 746e756.
                         [21] R. Marquez, V.G. Gueorguiev, C.F. Coimbra, Forecasting of global horizontal irradi-
                             ance using sky cover indices, J. Sol. Energy Eng. 135 (1) (2013) 011017.
                         [22] A. Mellit, Artificial Intelligence technique for modelling and forecasting of solar radi-
                             ation data: a review, J. Artif. Intell. Soft Comput. 1 (2008) 52e76.
                         [23] S. Daliento, A. Chouder, P. Guerriero, A.M. Pavan, A. Mellit, R. Moeini, P. Tricoli,
                             Monitoring, diagnosis, and power forecasting for photovoltaic fields: a review, Int. J.
                             Photoenergy 2017 (2017) Article ID 1356851, 13 pp. https://doi.org/10.1155/2017/
                             1356851.
                         [24] E. Lorenz, A. Hammer, D. Heinemann, Short term forecasting of solar radiation based
                             on satellite data, in: Proc. ISES Europe Solar Congress EUROSUN 2004, Freiburg,
                             Germany, 2004.
                         [25] J.L. Bosch, J. Kleissl, Cloud motion vectors from a network of ground sensors in a solar
                             power plant, Sol. Energy 95 (2013) 13e20.
                         [26] R. Platon, J. Martel, N. Woodruff, T.Y. Chau, Online fault detection in PV systems,
                             IEEE Trans. Sustain. Energy 6 (4) (2015) 1200e1207.
                         [27] C. Ventura, G.M. Tina, Development of models for on-line diagnostic and energy
                             assessment analysis of PV power plants: the study case of 1 MW Sicilian PV plant, En-
                             ergy Procedia 83 (2015) 248e257.
                         [28] N. Gokmen, E. Karatepe, B. Celik, S. Silvestre, Simple diagnostic approach for deter-
                             mining of faulted PV modules in string based PV arrays, Sol. Energy 86 (2010)
                             3364e3377.
                         [29] A. Chouder, S. Silvestre, Analysis model of mismatch power losses in PV systems,
                             J. Sol. Energy Eng. 131 (2) (2009) 024504.
                         [30] Y. Yagi, H. Kishi, W. Chine, A. Mellit, A.M. Pavan, S.A. Kalogirou, Fault
                             detection method for grid-connected photovoltaic plants, Renew. Energy 66 (2014)
                             99e110.
                         [31] D. Trillo-Montero, I. Santiago, J.J. Luna-Rodriguez, R. Real-Calvo, Development of a
                             software application to evaluate the performance and energy losses of grid-connected
                             photovoltaic systems, Energy Convers. Manag. 81 (2014) 144e159.
                         [32] P. Xu, J.M. Hou, D.K. Yuan, fault diagnosis for building grid-connected photovoltaic
                             system based on analysis of energy loss, in: Advanced Materials Research, vol. 805,
                             Trans Tech Publications, 2013, pp. 93e98.
                         [33] M. Davarifar, A. Rabhi, A. El-Hajjaji, M. Dahmane, Real-time model base fault diag-
                             nosis of PV panels using statistical signal processing, in: 2013 International Conference
                             on Renewable Energy Research and Applications (ICRERA), IEEE, October 2013,
                             pp. 599e604.
                         [34] A. Chouder, S. Silvestre, Automatic supervision and fault detection of PV systems
                             based on power losses analysis, Energy Convers. Manag. 51 (10) (2010) 1929e1937.
                         [35] D. Riley, J. Johnson, Photovoltaic prognostics and heath management using learning al-
                             gorithms, in: Photovoltaic Specialists Conference (PVSC), 2012 38th IEEE, 2012,
                             pp. 001535e001539.
                         [36] L. Bonsignore, M. Davarifar, A. Rabhi, G.M. Tina, A. Elhajjaji, Neuro-Fuzzy fault
                             detection method for photovoltaic systems, Energy Procedia 62 (2014) 431e441.
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