Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

References

• Ahmed, B. M., & Farman Alhialy, N. F. (2019). Optimum efficiency of PV panel using genetic algorithms to Touch Proximate Zero Energy House (NZEH). Civil Engineering Journal, 5(8), 1832–21. https://doi.org/10.28991/cej-2019-03091375
   Google Scholar
• Bonsignore, L., Davarifar, M., Rabhi, A., Tina, G. M., & Elhajjaji, A. (2014). Neuro-Fuzzy fault detection method for photovoltaic systems. Energy Procedia, 62, 431–441. https://doi.org/10.1016/j.egypro.2014.12.405
   Google Scholar
• Chen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (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. https://doi.org/10.1016/j.enconman.2019.111793
   Web of Science ®Google Scholar
• Chine, W., Mellit, A., Lughi, V., Malek, A., Sulligoi, G., & Massi Pavan, A. (2016). A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy, 90, 501–512. https://doi.org/10.1016/j.renene.2016.01.036
   Web of Science ®Google Scholar
• Chine, W., Mellit, A., Pavan, A. M., & Kalogirou, S. A. (2014). Fault detection method for grid-connected photovoltaic plants. Renewable Energy, 66, 99–110. https://doi.org/10.1016/j.renene.2013.11.073
   Web of Science ®Google Scholar
• De Benedetti, M., Leonardi, F., Messina, F., Santoro, C., & Vasilakos, A. (2018). Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing, 310, 59–68. https://doi.org/10.1016/j.neucom.2018.05.017
   Web of Science ®Google Scholar
• Dhimish, M., Holmes, V., Mehrdadi, B., & Dales, M. (2018). Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection. Renewable Energy, 117, 257–274. https://doi.org/10.1016/j.renene.2017.10.066
   Web of Science ®Google Scholar
• Firth, S. K., Lomas, K. J., & Rees, S. J. (2010). A simple model of PV system performance and its use in fault detection. Solar Energy, 84(4), 624–635. https://doi.org/10.1016/j.solener.2009.08.004
   Web of Science ®Google Scholar
• Harrou, F., Sun, Y., Taghezouit, B., Saidi, A., & Hamlati, M.-E. (2018). Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy, 116, 22–37. https://doi.org/10.1016/j.renene.2017.09.048
   Web of Science ®Google Scholar
• Hosenuzzaman, M., Rahim, N. A., Selvaraj, J., Hasanuzzaman, M., Malek, A. B. M. A., & Nahar, A. (2015). Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renewable and Sustainable Energy Reviews, 41, 284–297. https://doi.org/10.1016/j.rser.2014.08.046
   Web of Science ®Google Scholar
• Jiang, L. L., & Maskell, D. L. (2015). Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods. 2015 International Joint Conference on Neural Networks (IJCNN), 1–8, Killarney, Ireland. https://doi.org/10.1109/IJCNN.2015.7280498
   Google Scholar
• Kibaara, S. K., Murage, D. K., Musau, P., & Saulo, M. J. (2020). Comparative analysis of implementation of solar PV systems using the advanced SPECA modelling tool and HOMER software: Kenyan scenario. HighTech and Innovation Journal, 1(1), 8–20. https://doi.org/10.28991/HIJ-2020-01-01-02
   Google Scholar
• Lu, X., Lin, P., Cheng, S., Lin, Y., Chen, Z., Wu, L., & Zheng, Q. (2019). Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph. Energy Conversion and Management, 196, 950–965. https://doi.org/10.1016/j.enconman.2019.06.062
   Web of Science ®Google Scholar
• Madeti, S. R., & Singh, S. N. (2018). Modeling of PV system based on experimental data for fault detection using kNN method. Solar Energy, 173, 139–151. https://doi.org/10.1016/j.solener.2018.07.038
   Web of Science ®Google Scholar
• Mekki, H., Mellit, A., & Salhi, H. (2016). Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. Simulation Modelling Practice and Theory, 67, 1–13. https://doi.org/10.1016/j.simpat.2016.05.005
   Web of Science ®Google Scholar
• Pahwa, K., Sharma, M., Saggu, M. S., & Mandpura, A. K. (2020, February). Performance evaluation of machine learning techniques for fault detection and classification in PV array systems. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 791–796). IEEE, Noida, India.
   Google Scholar
• Pierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., & Zingaretti, P. (2018). DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–2, XLII-2, 893–900. https://doi.org/10.5194/isprs-archives-XLII-2-893-2018
   Google Scholar
• Rao, S., Spanias, A., & Tepedelenlioglu, C. (2019). Solar array fault detection using neural networks. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 196–200, Taipei, Taiwan. https://doi.org/10.1109/ICPHYS.2019.8780208
   Google Scholar
• Solórzano, J., & Egido, M. A. (2014). Hot-spot mitigation in PV arrays with distributed MPPT (DMPPT). Solar Energy, 101, 131–137. https://doi.org/10.1016/j.solener.2013.12.020
   Web of Science ®Google Scholar