Cloud Computing and Machine Learning-based Electrical Fault Detection in the PV System

References

• X. Syafaruddin, and D. S. Zinger, “Review on methods of fault diagnosis in photovoltaic system applications,” J. Eng. Sci. Technol. Rev., Vol. 12, no. 5, pp. 53–66, 2019.
   Google Scholar
• B. Basnet, H. Chun, and J. Bang, “An intelligent fault detection model for fault detection in photovoltaic systems,” J. Sens., Vol. 2020, pp. 1–11, 2020.
   Web of Science ®Google Scholar
• R. J. Mustafa, M. R. Gomaa, M. Al-Dhaifallah, and H. Rezk, “Environmental impacts on the performance of solar photovoltaic systems,” Sustainability, Vol. 12, no. 2, pp. 608, 2019.
   Web of Science ®Google Scholar
• C.-C. Hua, and P.-K. Ku. “Implementation of a stand-alone photovoltaic lighting system with MPPT, battery charger and high brightness LEDs,” in Proceedings of the 2005 International Conference on Power Electronics and Drives Systems, Kuala Lumpur, Malaysia, 2005, pp. 1601–1605.
   Google Scholar
• A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, “Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques,” Int. J. Photoenergy, Vol. 2019, pp. Article 6953530, 2019. DOI: 10.1155/2019/6953530.
   Web of Science ®Google Scholar
• A. Bouraiou, M. Hamouda, A. Chaker, A. Neçaibia, and M. Mostefaoui, “Experimental investigation of observed defects in crystalline silicon PV modules under outdoor hot dry climatic conditions in Algeria,” Sol. Energy, Vol. 159, pp. 475–87, 2020.
   Web of Science ®Google Scholar
• M. Arani, and M. A. Hejazi, “The comprehensive study of electrical faults in PV arrays,” J. Electric. Comput. Eng., 1–10, 2016. DOI: 10.1155/2016/8712960.
   Google Scholar
• N. Venkatesh S, and V. Sugumaran, “Fault diagnosis of visual faults in photovoltaic modules: a review,” Int. J. Green Energy, Vol. 18, 2020. DOI: 10.1080/15435075.2020.1825443.
   Web of Science ®Google Scholar
• T. G. Amaral, V. F. Pires, and A. J. Pires, “Fault detection in PV tracking systems using an image processing algorithm based on PCA,” Energies, Vol. 14, no. 21, pp. 7278, 2021.
   Web of Science ®Google Scholar
• E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, “Statistical fault detection in photovoltaic systems,” Sol. Energy, Vol. 150, pp. 485–99, 2017. DOI: 10.1016/j.solener.2017.04.043.
   Web of Science ®Google Scholar
• S. Madeti, and S. N. Singh, “A comprehensive study on different types of faults and detection techniques for solar photovoltaic system,” Sol. Energy, Vol. 158, pp. 161–85, 2017. DOI: 10.1016/j.solener.2017.08.069.
   Web of Science ®Google Scholar
• J. Flicker, and J. Johnson, “Photovoltaic ground fault detection recommendations for array safety and operation,” Sol. Energy, Vol. 140, 2016. DOI: 10.1016/j.solener.2016.10.017.
   Web of Science ®Google Scholar
• M. Akram, “Modeling and fault detection in DC side of photovoltaic arrays,” Electron. Theses Dissertat., Vol. 4847, 2014.
   Google Scholar
• S. Fadhel, C. Delpha, D. Diallo, I. Bahri, A. Migan, M. Trabelsi, and M. F. Mimouni, “PV shading fault detection and classification based on I-V curve using principal component analysis application to isolated PV system,” Sol. Energy, Vol. 179, 2019. DOI: 10.1016/j.solener.2018.12.048.
   Web of Science ®Google Scholar
• D. S. Pillai, F. Blaabjerg, and N. Rajasekar, “A comparative evaluation of advanced fault detection approaches for PV systems,” IEEE J. Photovolt., Vol. 9, no. 2, pp. 513–27, 2019.
   Web of Science ®Google Scholar
• M. Z. Farahmand, M. E. Nazari, S. Shamlou, and M. Shafie-khah, “The simultaneous impacts of seasonal weather and solar conditions on PV panels electrical characteristics,” Energies, Vol. 14, no. 4, pp. 845, 2021. DOI: 10.3390/en14040845.
   Web of Science ®Google Scholar
• P. Kumari, N. Kumar, and B. K. Panigrahi, “A framework of reduced sensor rooftop SPV system using parabolic curve fitting MPPT technology for household consumers,” IEEE Trans. Consum. Electron., Vol. 69, no. 1, pp. 29–37, Feb. 2023. DOI: 10.1109/TCE.2022.3209974.
   Web of Science ®Google Scholar
• M. Nowlan, et al. Infrared detection of solar cell defects under forward bias Google Patents, 2005.
   Google Scholar
• M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, and P. Mather, “Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system,” Energy, Vol. 140, pp. 276–90, 2017.
   Web of Science ®Google Scholar
• E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, “Statistical fault detection in photovoltaic systems,” SolarEnergy, Vol. 150, pp. 485–99, 2017.
   Google Scholar
• T. Pei, and X. Hao, “A fault detection method for photovoltaic systems based on voltage and current observation and evaluation,” Energies, Vol. 12, pp. 1712, 2019. DOI: 10.3390/en12091712.
   Web of Science ®Google Scholar
• A. Okere, and T. Iqbal, “A review of conventional fault detection techniques in solar PV systems and a proposal of long range (LoRa) wireless sensor network for module level monitoring and fault diagnosis in large solar PV farms,” Eur. J. Electric. Eng. Comput. Sci., Vol. 4, 2020. DOI: 10.24018/ejece.2020.4.6.267.
   Google Scholar
• A. Abid, A. Obed, and F. Al-Naima, “Detection and control of power loss due to soiling and faults in photovoltaic solar farms via wireless sensor network,” Int. J. Eng. Technol., Vol. 7, pp. 718, 2018. DOI: 10.14419/ijet.v7i2.10987.
   Google Scholar
• J. Saha, N. Kumar, and S. K. Panda, “A futuristic silicon-carbide (SiC) based electric-vehicle fast charging/discharging (FC/dC) station,” IEEE. J. Emerg. Sel. Top. Power. Electron., 2022. DOI: 10.1109/JESTPE.2022.3223417.
   PubMed Web of Science ®Google Scholar
• Y. Zhao. Fault analysis in solar photovoltaic arrays. Northeastern University ProQuest Dissertations Publishing, 2011.
   Google Scholar
• N. Sapountzoglou, and B. Raison. “Fault detection through monitoring of the AC variables in Grid Connected PV systems.” 3ème édition du Symposium de GénieElectrique, ffhal-02072249f, 2018.
   Google Scholar
• A. Esksndari, J. Milimonfared, M. Aghaei, and A. Reinders, “Autonomous monitoring of line-to-line faults in photovoltaic systems by feature selection and parameter optimization of support vector machine using genetic algorithm,” Appl. Sci., Vol. 10, pp. 5527, 2020. DOI: 10.3390/app10165527.
   Google Scholar
• N. Kumar, B. Singh, and B. K. Panigrahi, “Voltage sensorless based model predictive control with battery management system: for solar PV powered On-board EV charging,” IEEE Trans. Transp. Electrification, 2022. DOI: 10.1109/TTE.2022.3213253.
   Web of Science ®Google Scholar
• J. A. Dhanraj, et al., “An effective evaluation on fault detection in solar panels,” Energies, Vol. 14, no. 22, pp. 7770, 2021. DOI: 10.3390/en14227770.
   Web of Science ®Google Scholar
• F. Aziz, et al., “A novel convolutional neural network-based approach for fault classification in photovoltaic arrays,” IEEE Access., Vol. 8, pp. 41889–904, 2020. DOI: 10.1109/ACCESS.2020.2977116.
   Web of Science ®Google Scholar
• S. Zaki, H. Zhu, M. Alfakih, A. R. Sayed, and J. Yao, “Deep-learning-based method for faults classification of PV system,” IET Renew. Power Gener., Vol. 15, 2021. DOI: 10.1049/rpg2.12016.
   Web of Science ®Google Scholar
• C. Kapucu, and M. Cubukcu, “A supervised ensemble learning method for fault diagnosis in photovoltaic strings,” Energy, Vol. 227, pp. 120463, 2021.
   Web of Science ®Google Scholar
• M. Hajji, M.-F. Harkat, A. Kouadri, K. Abodayeh, M. Mansouri, H. Nounou, and M. Nounou, “Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems,” Eur. J. Control, Vol. 59, pp. 313–21, 2021.
   Web of Science ®Google Scholar
• S. Ahmad. “Fault classification for single phase photovoltaic systems using machine learning techniques,” in IEEE India International Conference on Power Electronics (IICPE), 2018.
   Google Scholar
• M. Emamian, A. Esksndari, M. Aghaei, A. Nedaei, A. MoradiSizkouhi, and J. Milimonfared, “Cloud computing and IoT based intelligent monitoring system for photovoltaic plants using machine learning techniques,” Energies, Vol. 15, 2022. DOI: 10.3390/en15093014.
   Web of Science ®Google Scholar
• N. Kumar, and S. K. Panda, “A multipurpose and power quality improved electric vessels charging station for the seaports,” IEEE Trans. Ind. Inf., Vol. 19, no. 3, pp. 3254–61, Mar. 2023. DOI: 10.1109/TII.2022.3170424.
   Web of Science ®Google Scholar
• N. Kumar, and S. K. Panda, “Smart high power charging networks and optimal control mechanism for electric ships,” IEEE Trans. Ind. Inf., Vol. 19, no. 2, pp. 1476–83, Feb. 2023. DOI: 10.1109/TII.2022.3170484.
   Web of Science ®Google Scholar
• J. Y. Siu, N. Kumar, and S. K. Panda, “Command authentication using multiagent system for attacks on the economic dispatch problem,” IEEE Trans. Ind. Appl., Vol. 58, no. 4, pp. 4381–93, Jul.-Aug. 2022. DOI: 10.1109/TIA.2022.3172240.
   Web of Science ®Google Scholar
• T. Pan, and W. C. Hsu. “Long distance communication system for areas without network infrastructure,” in 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan, Taiwan, 2022, pp. 99–101. DOI: 10.1109/ECBIOS54627.2022.9944999.
   Google Scholar
• N. C. Gaitan, “A long-distance communication architecture for medical devices based on LoRaWAN protocol,” Electronics. (Basel), Vol. 10, no. 8, pp. 940, 2021. DOI: 10.3390/electronics10080940.
   Web of Science ®Google Scholar