References
- Bracale, A., P. Caramia, G. Carpinelli, and A. R. Di Fazio. Dec, 2017. Modeling the three-phase short-circuit contribution of photovoltaic systems in balanced power systems. International Journal of Electrical Power & Energy Systems 93:204–15. [Online]. doi:10.1016/j.ijepes.2017.05.032.
- Chao, K. H., Y. W. Chao, and J. P. Chen. May, 2015. A circuit-based photovoltaic module simulator with shadow and fault settings. International Journal of Electronics. 103:424–38. [Online]. doi:10.1080/00207217.2015.1036376.
- Chen, L. C., P. J. Lin, J. Zhang, Z. C. Chen, Y. H. Lin, L. J. Wu, and S. Y. Cheng. 2018a. Fault diagnosis and classification for photovoltaic arrays based on principal component analysis and support vector machine. IOP Conference Series Earth and Environmental Science 188. [Online]. doi:10.1088/1755-1315/188/1/012089.
- Chen, S., X. Li, and J. Xiong. Jul, 2017. Series arc fault identification for photovoltaic system based on time-domain and time-frequency-domain analysis. Ieee Journal of Photovoltaics 7:1105–14. [Online]. doi:10.1109/JPHOTOV.2017.2694421.
- Chen, Z., Y. Chen, L. Wu, S. Cheng, and P. Lin. 2019. Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management 198:111793. doi:10.1016/j.enconman.2019.111793.
- Chen, Z., F. Han, L. Wu, J. Yu, S. Cheng, P. Lin, and H. Chen. 2018b. Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents. Energy Conversion and Management 178:250–64. doi:10.1016/j.enconman.2018.10.040.
- Chen, Z., L. Wu, S. Cheng, P. Lin, Y. Wu, and W. Lin. Oct, 2017. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics. Applied Energy 204:912–31. [Online]. doi:10.1016/j.apenergy.2017.05.034.
- Cherif, A., M. Jraidi, and A. Dhouib. Oct, 2002. A battery ageing model used in stand alone PV systems. Journal of Power Sources 112:49–53. [Online]. doi:10.1016/S0378-7753(02)00341-5.
- Chunlai, L., and Z. Xianshuang. 2017. A survey of online fault diagnosis for PV module based on BP neural network. 2016 International Conference on Smart City and Systems Engineering (ICSCSE) IEEE, Hunan, China, Jan. [Online]. doi:10.1109/ICSCSE.2016.0132.
- Drews, A., A. C. de Keizer, H. G. Beyer, E. Lorenz, J. Betcke, W. G. J. H. M. van Sark, W. Heydenreich, E. Wiemken, S. Stettler, P. Toggweiler, et al. Apr, 2007. Monitoring and remote failure detection of grid-connected PV systems based on satellite observations. Solar Energy 81:548–64. [Online]. doi:10.1016/j.solener.2006.06.019.
- Firth, S. K., K. J. Lomas, and S. J. Rees. 2010. A simple model of PV system performance and its use in fault detection. Solar Energy 84: 624–35. [Online]. doi:10.1016/j.solener.2009.08.004.
- Gokmen, N., E. Karatepe, B. Celik, and S. Silvestre. Sept, 2012. Engin Karatepe and Berk Celik, simple diagnostic approach for determining of faulted PV modules in string based PV arrays. Solar Energy 86:3364–77. [Online]. doi:10.1016/j.solener.2012.09.007.
- Hazra, A., S. Das, and M. Basu. Jun, 2017. An efficient fault diagnosis method for PV systems following string current. Journal of Cleaner Production 154:220–32. [Online]. doi:10.1016/j.jclepro.2017.03.214.
- Hejri, M., and H. Mokhtari. Nov, 2016. On the comprehensive parametrization of the photovoltaic (PV) cells and modules. IEEE Journal of Photovoltaics 99:1–9. [Online]. doi:10.1109/JPHOTOV.2016.2617038.
- Hunter, G., J. Riedemann, I. Andrade, R. Blasco-Gimenez, and R. Peña. Apr, 2019. Power control of a grid-connected PV system during asymmetrical voltage faults. Electrical Engineering 101:239–50. [Online]. doi:10.1007/s00202-019-00769-x.
- Jaffery, Z. A., A. K. Dubey, and A. Haque. Jun, 2017. Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging. Infrared Physics & Technology 83:182–87. [Online]. doi:10.1016/j.infrared.2017.04.015.
- Kumar, B. P. Dec, 2017. Online fault detection and diagnosis in photovoltaic systems using wavelet packets. IEEE Journal of Photovoltaics 99:1–9. [Online]. doi:10.1109/JPHOTOV.2017.2770159.
- Lewis, N. S., and D. G. Nocera. Oct, 2006. Powering the planet: Chemical challenges in solar energy utilization. Proceedings of the National Academy of Sciences of the United States of America 103:15729–35. [Online]. doi:10.1073/pnas.0603395103.
- Li, Q., and F. H. Khan. 2014. Identifying natural degradation/aging in power MOSFETs in a live grid-tied PV inverter using spread spectrum time domain reflectometry. 2014 International Power s Conference (IPEC-Hiroshima 2014 ECCE-ASIA) IEEE, Japan, May. [Online]. doi:10.1109/IPEC.2014.6869888.
- Lin, H., Z. Chen, L. Wu, P. Lin, and S. Cheng. Nov, 2015. On-line monitoring and fault diagnosis of PV array based on BP neural network optimized by genetic algorithm. International Workshop on Multi-disciplinary Trends in Artificial Intelligence 29:102–12. [Online]. doi:10.1007/978-3-319-26181-2_10.
- Lu, S. B., T. Phung, and D. Zhang. Jun, 2018. A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems. Renewable and Sustainable Energy Reviews 89:88–98. [Online]. doi:10.1016/j.rser.2018.03.010.
- Ma, Y. 2018. DC fault arc identification and detection analysis of photovoltaic power generation system. 2018 International Conference on Robots & Intelligent System (ICRIS) IEEE Computer Society, Amsterdam, Netherland 1, 63–65, Nov. [Online]. doi:10.1109/ICRIS.2018.00024.
- Madeti, S. R., and S. N. Singh. Sept, 2017a. Online fault detection and the economic analysis of grid-connected photovoltaic systems. Energy 134:121–35. [Online]. doi:10.1016/j.energy.2017.06.005.
- Madeti, S. R., and S. N. Singh. Nov, 2017b. Online modular level fault detection algorithm for grid-tied and off-grid PV systems. Solar Energy 157:349–64. [Online]. doi:10.1016/j.solener.2017.08.047.
- Mellit, A., G. M. Tina, and S. A. Kalogirou. Aug, 2018. Fault detection and diagnosis methods for photovoltaic systems: A review. Renewable and Sustainable Energy Reviews 91:1–17. [Online]. doi:10.1016/j.rser.2018.03.062.
- Muselli, M., G. Notton, J. L. Canaletti, and A. Louche. 1998. Utilization of meteosat satellite-derived radiation data for integration of autonomous photovoltaic solar energy systems in remote areas. Energy Conversion and Management 39: 1–19. [Online]. doi:10.1016/S0196-8904(96)00183-5.
- Nogami, S., and A. Yokoyama. Jan, 2017. Power factor control of photovoltaic generation output for improving transient stability. Electrical Engineering in Japan. 202:45–51. [Online]. doi:10.1002/eej.23040.
- Pillai, D. S., F. Blaabjerg, and N. Rajasekar. 2019. A Comparative Evaluation of Advanced Fault Detection Approaches for PV Systems. IEEE Journal of Photovoltaics 1–15. [Online]. doi:10.1109/JPHOTOV.2019.2892189.
- Pillai, D. S., and N. Rajasekar. Aug, 2018. A comprehensive review on protection challenges and fault diagnosis in PV systems. Renewable and Sustainable Energy Reviews 91:18–40. [Online]. doi:10.1016/j.rser.2018.03.082.
- Roy, S., M. K. Alam, F. Khan, J. Johnson, and J. Flicker. Aug, 2018. An irradiance independent, robust ground fault detection scheme for PV arrays based on spread spectrum time domain reflectometry. IEEE Transactions on Power Electronics 33:7046–57. [Online]. doi:10.1109/TPEL.2017.2755592.
- Serge, T. B., K. Xiaoning, and H. Xinghua. Sept, 2018. Fault analysis of solar photovoltaic penetrated distribution systems including overcurrent relays in presence of fluctuations. International Journal of Electrical Power and Energy Systems 100:517–30. [Online]. doi:10.1016/j.ijepes.2018.03.003.
- Silvestre, S., M. A. D. Silva, A. Chouder, D. Guasch, and E. Karatepe. May, 2014. New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators. Energy Conversion and Management 86:241–49. [Online]. doi:10.1016/j.enconman.2014.05.008.
- Takashima, T., J. Yamaguchi, and M. Ishida. Sep, 2008. Disconnection detection using earth capacitance measurement in photovoltaic module string. Progress in Photovoltaic: Research and Applications 16:669–77. [Online]. doi:10.1002/pip.860.
- Tsanakas, J. A., and P. N. Botsaris. March, 2012. An infrared thermographic approach as a hot-spot detection tool for photovoltaic modules using image histogram and line profile analysis. International Journal of Condition Monitoring 2:22–30. [Online]. doi:10.1784/204764212800028842.
- Vergura, S., F. Marino, and M. Carpentieri. 2015. Processing infrared image of PV modules for defects classification. 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Palermo, Italy, Nov. [Online]. doi:10.1109/ICRERA.2015.7418626.
- Vergura, S., M. F. D. Ruvo, and F. Manno. 2015. A GUI based analysis of infrared images of PV modules. 2015 International Conference on Clean Electrical Power (ICCEP), Taormina, Jun. [Online]. doi:10.1109/ICCEP.2015.7177574.
- Wenying, Z. Jul, 2017. Weather prediction with multiclass support vector machines in the fault detection of photovoltaic system. Journal of Automatic SINICA. 4. [Online]. doi:10.1109/JAS.2017.7510562.
- Wilson, T., A. Wu, X. Geng, Z. Wang, and X. Xiong. Oct, 2016. Analysis of the electronic crosstalk effect in Terra MODIS long-wave infrared photovoltaic bands using lunar images. International Society for Optics and Photonics. [Online]. doi:10.1117/12.2240574.
- Wu, L., Z. Chen, C. Long, S. Cheng, P. Lin, Y. Chen, and H. Chen. 2018. Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm. Applied Energy 232C:36–53. doi:10.1016/j.apenergy.2018.09.161.
- Xiong, Q., X. Liu, X. Feng, A. L. Gattozzi, Y. Shi, L. Zhu, S. Ji, and R. E. Hebner. Jun, 2018. Arc fault detection and localization in photovoltaic systems using feature distribution maps of parallel capacitor currents. IEEE Journal of Photovoltaics 8 (4):1090–97. [Online]. doi:10.1109/JPHOTOV.2018.2836986.
- Youssef, A., M. El-Telbany, and A. Zekry. Oct, 2017. The role of artificial intelligence in photo-voltaic systems design and control: A review. Renewable and Sustainable Energy Reviews 78:72–79. [Online]. doi:10.1016/j.rser.2017.04.046.
- Zhao, Y., R. Ball, J. Mosesian, J. F. de Palma, B. Lehman. May, 2015. Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. IEEE Transactions on Power Electronics 30: 2848–58.
- Zou, Z., Y. Hu, B. Gao, W. L. Woo, and X. Zhao. Mar, 2014. Temperature recovery from degenerated infrared image based on the principle for temperature measurement using infrared sensor. Journal of Applied Physics. 115. [Online]. doi:10.1063/1.4863783.