[1]
Jiang, L.L. et al.,Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods,. International Joint Conference on Neural Networks, IEEE International Joint Conference, 2015, vol. 12, p.1–8.
DOI: 10.1109/ijcnn.2015.7280498
Google Scholar
[2]
Wolgemuth J., Failure modes of crystalline Si modules,, PV Module Reliability Workshop, (2010).
Google Scholar
[3]
Zhao Y et al., Fault analysis in solar PV arrays under: Low irradiance conditions and reverse connections". In: Proceedings of the 37th photovoltaic specialists, conference (PVSC). IEEE; 2011. p.2–5.
DOI: 10.1109/pvsc.2011.6186346
Google Scholar
[4]
Davarifar M et al., Comprehensive modulation and classification of faults and analysis their effect in DC side of photovoltaic system,. Energy Power Engineering, 2013, issue 5, vol.4, p.230.
DOI: 10.4236/epe.2013.54b045
Google Scholar
[5]
Chine W. et al., Fault diagnosis in photovoltaic arraysIn: Clean Electrical Power (ICCEP), International Conference on 2015, p.67–72.
DOI: 10.1109/iccep.2015.7177602
Google Scholar
[6]
Alam MK et al., A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques,. IEEE J Photovoltaic.
Google Scholar
[7]
Hund, T.D. et al., Analysis techniques used on field degraded photovoltaic modules,. NASA STI/Recon Technical Report N, 1995, p.96.
Google Scholar
[8]
Breitenstein O. et al, On the detection of shunts in silicon solar cells by photo-and electroluminescence imaging,. Progress in Photovoltaics Research and Applications, 2008, issue 16, vol.4, p.325–330.
DOI: 10.1002/pip.803
Google Scholar
[9]
Kirchartz, T. et al., Reciprocity between electroluminescence and quantum efficiency used for the characterization of silicon solar cells,. Progress in Photovoltaics Research and Applications, 2009, 17 (6), p.394–402.
DOI: 10.1002/pip.895
Google Scholar
[10]
Kasemann, M. et al., Luminescence imaging for the detection of shunts on silicon solar cells,. Progress in Photovoltaics Research and Applications, 2008, issue 16, vol. 4, p.297–305.
DOI: 10.1002/pip.812
Google Scholar
[11]
Kaplani E., Detection of degradation effects in field-aged c-Si solar cells through IR thermography and digital image processing,. Int. J. Photoenergy, (2012).
DOI: 10.1155/2012/396792
Google Scholar
[12]
Ancuta F. et al., Fault analysis possibilities for PV panels,. In: Proceedings of the 3rd international youth conference on energetics (IYCE). IEEE; 2011, p.1–5.
Google Scholar
[13]
Kase R. et al., Fault detection of bypass circuit of PV module—Detection technology of open circuit fault location,. In: Proceedings of the 19th international conference on electrical machines and systems (ICEMS), 2016. IEEE; 2016. p.1–4.
Google Scholar
[14]
Yihua Hu et al., Photovoltaic fault detection using a parameter based model,, Research article, Solar Energy, vol. 96, 2013, pp.96-102.
DOI: 10.1016/j.solener.2013.07.004
Google Scholar
[15]
Zhicong Chen et al., Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics,, Research article, Applied Energy, 2017, vol. 204, pp.912-931.
DOI: 10.1016/j.apenergy.2017.05.034
Google Scholar
[16]
Belaout A. et al., A Neuro-fuzzy classifier for fault detection and classification in photovoltaic module,. In: Proceedings of the 8th international conference on modelling, identification and control (ICMIC). IEEE; 2016, p.144–9.
DOI: 10.1109/icmic.2016.7804289
Google Scholar
[17]
Hu, Y. et al., 2013. Photovoltaic fault detection using a parameter based model,. Solar Energy, 1996, p.96–102.
Google Scholar
