Principal Component Analysis-Based Shading Defect Identification and Categorization in Standalone PV Systems Using I-V Curves

Hayder Dakhil Atiyah, Mohamed Boukattaya, Fatma Bensalem


Photovoltaic (PV) system health monitoring and fault diagnosis are essential for optimizing power generation, enhancing reliability, and prolonging the lifespan of PV power plants. Shading, especially in PV systems, leads to unique voltage-current (I-V) characteristics, serving as indicators of system health. This paper presents a cost-effective and highly accurate method for detecting, diagnosing, and classifying shading faults based on real I-V data obtained through electrical measurements under both healthy and shaded conditions. The method leverages Principal Component Analysis (PCA) to separate classes, and a confusion matrix assesses classification accuracy. The results demonstrate a success rate exceeding 98% in various configurations, using experimental data from a 250 W PV module. Importantly, this method relies solely on existing electrical measurements, eliminating the need for additional sensors, making it both efficient and cost-effective.


Pv model, Principal component analysis, Health system, temperature, irradiation

Full Text:



K. Ohdaira, M. Akitomi, Y. Chiba, A. “Potential-induced degradation of n-type front-emitter crystalline silicon photovoltaic modules — Comparison between indoor and outdoor test results”, Solar Energy Materials and Solar Cells, Vol. 249, 2023,112038 ISSN 0927-0248,

B. -K. Kang, S. -T. Kim, S. -H. Bae and J. -W. Park, "Diagnosis of Output Power Lowering in a PV Array by Using the Kalman-Filter Algorithm," in IEEE Transactions on Energy Conversion, Vol. 27, no. 4, pp. 885-894, Dec. 2012, doi: 10.1109/TEC.2012.2217144. 8

S. Fadhel, C. Delpha, D. Diallo, I. Bahri, A. Migan, M. Trabelsi, M.F. Mimouni, “PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system”, Solar Energy, Vol. 179, 2019, pp. 1-10, ISSN0038-092X

S. Bordihn, A. Fladung, J. Schlipf and M. Köntges, "Machine Learning Based Identification and Classification of Field-Operation Caused Solar Panel Failures Observed in Electroluminescence Images," in IEEE Journal of Photovoltaics, Vol. 12, no. 3, pp. 827-832, May 2022, doi: 10.1109/JPHOTOV.2022.3150725.

A. Migan, C. Delpha, D. DialloI. I. Bahri, M. Trabelsi; M. Faouzi-Mimouni, "Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis," 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 2018, pp. 927-932, doi: 10.1109/ICIT.2018.8352302.

Koray Şener Parlak, Obtaining electrical characteristics of a PV module by FPGA based experimental system International Journal of hydrogen Energy, no. 58, 2020, pp. 3312833135, SSN0363199 , https :// .113.

Y. Zhu, W. Xiao, A comprehensive review of topologies for photovoltaic I–V curve tracer, Solar Energy, Vol. 196, 2020, pp. 346357, ISSN0038092X, Jesus dos Santos Rodrigues, P. F. Torres, M. André Barros Galhardo, O. Andre Chase, W. Leão Monteiro, J. de Arimatéia Alves Vieira Filho, F. Menezes Mares, W. Negrão Macêdo, “A new methodology for the assessing of power losses in partially shaded SPV arrays,” Energy, Vol. 232, 2012, pp. 120938. ISSN 0360-5442 , .

Q. Jia, Fault detection and recognition by hybrid nonnegative matrix factorizations, Chemometrics andIntelligentLaboratorySystems, Vol. 225, 2022, pp. 104553,ISSN01697439,

J J. Khanam , S. Y. Foo, "Neural Networks Technique for Maximum Power Point Tracking of Photovoltaic Array," SoutheastCon 2018, St. Petersburg, FL, USA, 2018, pp. 1-4, doi: 10.1109/SECON.2018.8479054.

Fahmida, M. S. Alam, M. A. Hoque, "MATLAB Simscape Simulation of Solar Photovoltaic array fed BLDC Motor using Maximum Power Point Tracker," 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 2019, pp. 662-667, doi: 10.1109/ICREST.2019.8644339.

H. S. Agha, Z. -u. Koreshi and M. B. Khan, "Artificial neural network based maximum power point tracking for solar photovoltaics," 2017 International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan, 2017, pp. 150-155, doi: 10.1109/ICICT.2017.8320180.

V. K. Viswambaran, A. Ghani and E. Zhou, "Modelling and simulation of maximum power point tracking algorithms & review of MPPT techniques for PV applications," 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), Ras Al Khaimah, United Arab Emirates, 2016, pp. 1-4, doi: 10.1109/ICEDSA.2016.7818506. 20).

S. Zouirech, M. Zerouali, H. Elaissaoui, A. E. Ougli and B. Tidhaf, "Application of Various Classical and Intelligent MPPT Tracking Techniques for the Production of Energy through a Photovoltaic System," 2019 7th International Renewable and Sustainable Energy Conference (IRSEC), Agadir, Morocco, 2019, pp. 1-6, doi: 10.1109/IRSEC48032.2019.9078154.

G. Abdel-rahman, N. H. Saad and A. A. -s. Abdel-fatah, "Performance Analysis of Fourth Order Buck Converter Based on Current Sensorless Maximum Power Point Tracking Technique for Photovoltaic Systems," 2018 Twentieth International Middle East Power Systems Conference (MEPCON), 2018.

K. -Y. Chou, C. -S. Yang and Y. -P. Chen, "Deep Q-Network Based Global Maximum Power Point Tracking for Partially Shaded PV System," 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020.

P. Sahu, D. Verma, S. Nema, "Physical design and modelling of boost converter for maximum power point tracking in solar PV systems," 2016 International Conference on Electrical Power and Energy Systems (ICEPES), 2016.

A. H. EL-Din, S. F. Mekhamer, H. M. EL-Helw, "Maximum power point tracking under partial shading condition using particle swarm optimization with DC-DC boost converter," 2018 53rd International Universities Power Engineering Conference (UPEC), 2018.

J. Raju, B. T. Kumar, "Optimized maximum power point tracking technique for the simulation of photovoltaic system sourced for higher output power," 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), 2017.

K. GB, Shivashankar, K. N. S. Kumar, "Optimum Power Point Tracking Technique for Standalone Solar Photovoltaic System," 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2021.

Sergiu Spataru, Dezso Sera, Tamas Kerekes, Remus Teodorescu, Diagnostic method for photovoltaic systems based on light I–V measurements, Solar Energy, Volume119,2015,Pages2944,ISSN0038092X, .

M. Mao; Y. Zhao, S. Sun; L. Chang, Yu Cao, J.i Su, M.Sun, G. Zhang, "Quantitative analysis on economic impacts of installation at different sites on microgrids with multi-energy," 2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Aalborg, Denmark, 2012, pp. 668-673, doi: 10.1109/PEDG.2012.6254074.

M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, “Diagnostic method for photovoltaic systems based on six layer detection algorithm,” Electric Power Systems Research, Vol. 151, 2017, pp. 2639, ISSN03787796, https:// 10.1016/j.epsr.2017.05.024.

Fadhel, S., Migan, A., Delpha, C., Diallo, D., Bahri, I., Trablesi, M., Mimouni, M.F., 2018. Data-Driven Approach for Isolated PV Shading Fault Diagnosis Based of Experimental I-V Curves Analysis. In: IEEE International Conference on Industrial Technology (ICIT), pp. 927–932.

M.-Faouzi Harkat, G. Mourot, J. Ragot, “An improved PCA scheme for sensor FDI: Application to an air quality monitoring network,” Journal of Process Control, Vol. 16, no 6, 2006, pp. 625-634, ISSN 0959-1524,https: // 0.1016/j. jprocont .2005. 09.007.

S. Fadhel, A. Migan, C. Delpha, D. Diallo, I. Bahri, M. Trablesi, M.F Mimouni. “Data-Driven Approach for Isolated PV Shading Fault Diagnosis Based on Experimental I-V Curves Analysis”. In: IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 927–932.



  • There are currently no refbacks.

Copyright (c) 2023 International Journal of Energetica

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
The content of this journal is licenced under a Creative Commons Attribution-NonCommercial 4.0 International License