Fault Detection and Diagnosis Technic Using Electrical Characteristics of a PV Module and Machine Learning Classifier

The growth of photovoltaic power plants is continuously rising, this growth would not be possible without safety, monitoring, and fault detection systems. In this paper, the common faults of a typical photovoltaic power plant that may occur in a photovoltaic module are discussed. Also, the paper studies the electrical characteristics of a photovoltaic module operating under several faults’ conditions applied on a specially designed module that measures the output current of each substring by utilizing sensitive Hall Effect sensors. After obtaining the electrical characteristics under faults, using machine learning, two decision trees classifier models are trained, the first classifier is trained to detect and recognize faults. However, this classifier may confuse the partial shading case with several other faults. Hence, the second decision tree classifier is trained to distinguish the exact fault type when the module is operating under partial shading condition by applying a short-circuit test on the photovoltaic module. This design can be achieved by connecting current sensors in the junction box of a typical photovoltaic module.


Ali, M. H., Rabhi, A., El Hajjaji, A., & Tina, G. M. (2017). Real time fault detection in photovoltaic systems. Energy Procedia, 111, 914-923.

Alam, M. K., Khan, F., Johnson, J., & Flicker, J. (2015). A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE Journal of Photovoltaics, 5(3), 982-997.

Andò, B., Baglio, S., Pistorio, A., Tina, G. M., & Ventura, C. (2015). Sentinella: Smart monitoring of photovoltaic systems at panel level. IEEE Transactions on Instrumentation and Measurement, 64(8), 2188-2199.

BP Energy, BP Energy Outlook 2019 edition, 2019. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2019.pdf (accessed December 8, 2020).

Chen, Z., Wu, L., Cheng, S., Lin, P., Wu, Y., & Lin, W. (2017). Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and IV characteristics. Applied

Chine, W., Mellit, A., Lughi, V., Malek, A., Sulligoi, G., & Pavan, A. M. (2016). A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy, 90, 501-512.

Chouder, A., & Silvestre, S. (2010). Automatic supervision and fault detection of PV systems based on power losses analysis. Energy conversion and Management, 51(10), 1929-1937.

Drews, A., De Keizer, A. C., Beyer, H. G., Lorenz, E., Betcke, J., Van Sark, W. G. J. H. M., ... & Bofinger, S. (2007). Monitoring and remote failure detection of grid-connected PV systems based on satellite observations. Solar energy, 81(4), 548-564.

Du, C. J., & Sun, D. W. (2008). Object Classification. Computer vision technology for food quality evaluation, 81.

Duke Energy, NC PV DG program SEPA presentation. (2011) 1–14.

EPDK, Elektrik Piyasası Yıllık Sektör Raporu, (2018). https://www.epdk.org.tr/Detay/Icerik/3-0-24-3/elektrikyillik-sektor-raporu.

Gokmen, N., Karatepe, E., Silvestre, S., Celik, B., & Ortega, P. (2013). An efficient fault diagnosis method for PV systems based on operating voltage-window. Energy conversion and management, 73, 350-360.

Guerriero, P., d'Alessandro, V., Petrazzuoli, L., Vallone, G., & Daliento, S. (2013, June). Effective real-time performance monitoring and diagnostics of individual panels in PV plants. In 2013 International Conference on Clean Electrical Power (ICCEP) (pp. 14-19). IEEE.

Hernandez, J. C., & Vidal, P. G. (2009). Guidelines for protection against electric shock in PV generators. IEEE Transactions on Energy Conversion, 24(1), 274-282.

Jung, T. H., Ko, J. W., Kang, G. H., & Ahn, H. K. (2013). Output characteristics of PV module considering partially reverse biased conditions. Solar Energy, 92, 214-220.

Key world energy statistics, 2019. https://www.connaissancedesenergies.org/sites/default/files/pdf-actualites/Key_World_Energy_Statistics_2019.pdf.

Köntges, M., Kurtz, S., Packard, C. E., Jahn, U., Berger, K. A., Kato, K., ... & Miller, D. (2014). Review of failures of photovoltaic modules.

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.

Mellit, A., Tina, G. M., & Kalogirou, S. A. (2018). Fault detection and diagnosis methods for photovoltaic systems: A review.

Renewable and Sustainable Energy Reviews, 91, 1-17.

Rezk, H., Tyukhov, I., Al-Dhaifallah, M., & Tikhonov, A. (2017). Performance of data acquisition system for monitoring PV system parameters. Measurement, 104, 204-211.

SEPA, SEPA, (2018). http://www.yegm.gov.tr/MyCalculator/Default.aspx (accessed June 24, 2020).

Spagnuolo, G., Xiao, W., & Cecati, C. (2015). Monitoring, diagnosis, prognosis, and techniques for increasing the lifetime/reliability of photovoltaic systems. IEEE Transactions on Industrial Electronics, 62(11), 7226-7227.

TEİAŞ, Electricity Statistics, 2019. https://www.teias.gov.tr/tr-TR/kurulu-guc-raporlari.

Triki-Lahiani, A., Abdelghani, A. B. B., & Slama-Belkhodja, I. (2018). Fault detection and monitoring systems for photovoltaic installations: A review. Renewable and Sustainable Energy Reviews, 82, 2680-2692.

Zhao, Y., De Palma, J. F., Mosesian, J., Lyons, R., & Lehman, B. (2012). Line–line fault analysis and protection challenges in solar photovoltaic arrays. IEEE transactions on Industrial Electronics, 60(9), 3784-3795.

Zhao, Y., Ball, R., Mosesian, J., de Palma, J. F., & Lehman, B. (2014). Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. IEEE Transactions on Power Electronics, 30(5), 2848-2858.

Kaynak Göster

Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi
  • ISSN: 1308-5514
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2009