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Solar panel fault diagnosis based on the intelligent recursive method

Document Type : Reseach Article

Authors

Faculty of Technology, University of Chlef 02000 DZ, Algeria

10.57647/j.mjee.2025.1902.28

Abstract

The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis method that can be implemented in a real structure. These faults are diagnosed and located by implementing an algorithm based on the measured values of the solar panel using an intelligent recursive least squares approach. Our objective is to contribute to the diagnosis of faults in photovoltaic systems based on fuzzy logic in a recurrent manner. The integration of recursive least squares (RLS) with fuzzy logic are essential to improve system efficiency and reliability. This approach enables rapid identification and resolution of faults, helping to avoid energy losses, reduce downtime, and support proactive maintenance. It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. Quantitatively, this method achieves high diagnostic accuracy (over 90%), reduces error rates by up to 30% under dynamic conditions, and
provides real-time fault detection with minimal latency. The combination of RLS and fuzzy logic improves fault diagnosis by effectively handling uncertainties and handling ambiguous situations better than traditional methods.

Keywords

References
  1. Power FS, 'Global Market Outlook' 2016. http://www.solarpowereurope.org
  2. Triki-lahiani, A.B.  Abdelghani and I.  Slama-belkhodja, “Fault Detection and Monitoring Systems for Photovoltaic Installations: A Review”, Renewable Sustainable Energy Review, pp. 0-1, 2017. DOI: https://doi.org/10.1016/j.rser.2017.09.101.
  3. Bun,  “Détection  et  localisation  de  défauts  dans  un  système  photovoltaïque”,  L. Bun  To  Cite  this  Version:  Hal-Id,  Détection  et  Localisation  de  Défauts  pour  un Système PV 2012.DOI : https://theses.hal.science/tel-00647189v1
  4. Ball, B. Brooks, J. Johnson, A. Rosenthal, M. Albers and T. Zgonena,  “Inverter Ground- Fault Detection  'Blind  Spot”  and Mitigation Methods', Prepared  by Solar America Board for Codes and Standards, Non disponible.DOI: https://doi.org/10.13140/RG.2.1.3836.8720
  5. L. King,  M.A.  Quintana,  J.A.  Kratochvil,  D.E.  Ellibee  and  B.R.  Hansen, “Photovoltaic  Module  Performance  and  Durability  Following  Long-term”  Field Exposure 2000:241–56.DOI: https://doi.org/10.1002/(SICI)1099-159X(200003/04)8:2
  6. Han, J.D.  Jeong,  I.  Lee  and  S.H. Kim,  “Low-Cost Monitoring  of Photovoltaic Systems  at  Panel  Level  in  Residential  Homes  Based  on  Power  Line Communication”,  IEEE  Transactions  on  Consumer  Electronics,  pp.  425  -  441, 2017.  https://doi.org/10.3390/fr12050881
  7. Cristaldi, G. Leone and S. Vergura, “Performance Index of Photovoltaic Fields for Diagnostic Purposes”, pp. 1 - 6, non disponible.DOI: https://doi.org/10.1049/cp.2016.0556
  8. R. Madeti and S.N. Singh, “A comprehensive study on different types of faults and detection techniques for solar photovoltaic system”, Solar Energy, Vol. 158, pp. 161 - 185, 2017.DOI: https://doi.org/10.1016/j.solener.2017.08.069.
  9. Mellit, G.M. Tina and S.A. Kalogirou, “Fault detection and diagnosis methods for photovoltaic systems: a review”, Renewable Sustainable Energy Review, Vol. 91, pp. 1 - 17, 2018. DOI: https://doi.org/10.1016/j.rser.2018.03.062.
  10. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder and S. Silvestre, 'Statistical fault detection in photovoltaic systems’, Solar Energy, Vol.  150, pp. 485 - 499, 2017.DOI: https://doi.org/10.1016/j.solener.2017.04.043.
  11. Hirata Y, Noro S, Aoki T, Miyazawa S. “Diagnosis Photovoltaic Failure by Simple Function Method to Acquire I - V Curve of Photovoltaic Modules String” 2011:10–3.DOI: https://doi.org/10.1109/PVSC.2008.4922833
  12. Çak B. “A novel voltage-current characteristic based global maximum power point tracking algorithm in photovoltaic systems” 2016; 112. DOI:  https://doi.org/10.1016/j.energy.2016.05.121.
  13. Aouchiche N, Becherif M, HadjArab A, Aitcheikh MS, Ramadan HS, Cheknane A. “Dynamic  Performance  Comparison  for  MPPT-PV  Systems  using  Hybrid Pspice/Matlab  Simulation”.  Int  J  Emerg  Electr  Power  Syst  2016;17:529–39. DOI: https://doi.org/10.1515/ijeeps-2016-0074.
  14. Bizon,  'Global  Extremum  Seeking  Control  of  the  power  generated  by  a Photovoltaic  Array  under  Partially  Shaded  Conditions',  Energy  Conversion Management, Vol. 109, pp. 71 - 85, 2016. DOI: https://doi.org/10.1016/j.enconman.2015.11.046.
  15. Bernadette, Bouchon-Meunier “Logique floue, principes, aide à la décision“. Lavoisier, 2003. https://hal.science/hal-01533303
  16. Kamingu. Théorie des ensembles flous, Lareq One Pager, Vol. 11, n° 1, p. 37-45. DOI: http://www.lamsade.dauphine.fr/mcda/biblio/Category/thesis.html.
  17. Angelov, P. et X. Zhou (2008). “Evolving fuzzy-rule-based classifiers from data streams“. Fuzzy Systems, IEEE Transactions on 16(6), 1462 –1475.DOI:  https://doi.org/10.1109/TFUZZ.2008.925904
  18. Lughofer, E. et P. Angelov (2011). “Handling drifts and shifts in on-line data streams withe volving fuzzy systems“. Applied Soft Computing 11(2), 2057–2068.DOI: https://doi.org/10.1016/j.asoc.2010.07.003
  19. M. Karmacharya and R. Gokaraju, "Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN," IEEE Trans. Power Del., vol. 33, no. 2, pp. 549-559,2018. DOI: https://doi.org/10.1109/TPWRD.2017.2721903
  20. Chen and X. Wang, "Adaptive fault localization in photovoltaic systems," IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6752- 6763, 2018.DOI: https://doi.org/10.1109/TSG.2017.2722821
  21. A. Zadeh, « Fuzzy sets, fuzzy loqic and fuzzy systems». DOI: https://doi.org/10.1142/2895 
  22. Liu, M. Li, X. Ji, X. Luo, M. Wang and Y. Zhang, 'A comparative study of the maximum power point tracking methods for PV systems', Energy and Conversion Management, Vol. 85, pp. 809 - 816, 2014. DOI: https://doi.org/10.1016/j.enconman.2014.01.049.
  23. Zhou and W. Sun, 'Study on maximum power point tracking of photovoltaic array in irregular shadow', International Journal of Electric Power Energy System, Vol. 66, pp. 227 - 234, 2015.DOI: https://doi.org/10.1016/j.ijepes.2014.10.030.
  24. Savita Nema, R.K. Nema, Gayatri Agnihotri, “MATLAB/Simulink based study of photovoltaic cells / modules / array and their experimental verification”, International journal of Energy and Environment, vol.1, No.3, pp.487-500, 2010. https://www.researchgate.net/publication/44024846_Matlab_simulink_based_study_of_photovoltaic_cells_modules_array_and_their_experimental_verification 
  25. Johnson et al., "Photovoltaic DC arc fault detector testing at Sandia National Laboratories," Rec. IEEE Photovolt. Spec. Conf., pp. 003614-003619, 2011.DOI:  https://doi.org/10.1109/PVSC.2011.6185930
  26. Kamingu. “Théorie des ensembles flous“, Lareq One Pager, Vol. 11, n° 1, p. 37-45.DOI:  https://doi.org 10.13140/RG.2.2.31700.81286
  27. Lughofer, E. et P. Angelov (2011). Handling drifts and shifts in on-line data streams withe volving fuzzy systems. Applied Soft Computing 11(2), 2057–2068.DOI: https://doi.org/10.1016/j.asoc.2010.07.003
  28. Sun, A. Chouder, S. Silvestre, E. Garoudja, K. Kara, and F. Harrou, "Statistical fault detection in photovoItaic systems," Sol. Energy, vol. 150, pp. 485-499, 2017.DOI: https://doi.org/10.1016/j.solener.2017.04.043
  29. Rodolfo Araneo, Salvatore Celozzi "Transient behavior of wind towers grounding systems under lightning strikes’’ Int J Energy Environ Eng09 December 2015.DOI : https://doi.org/10.1007/s40095-015-0196-7 .
  30. Smith JA et al. ‘’ Enhancing solar power forecasting accuracy using machine learning’’. Renewable Energy. 2022.DOI: https://doi.org/10.1016/j.csite.2024.104924
  31.  
  32. Kim, H. and Lee, D., 2021’’ Probabilistic Solar Power Forecasting Based on Bivariate Conditional Solar Irradiation Distributions’’. IEEE Transactions on Sustainable Energy, 12(4), pp.2031-2041.DOI:   https://doi.org/1109/TSTE.2021.3077001
  33. Bhanu Pratap, Parmanand Sharma, Lavkush K Patel, Ajit T Singh, Sunil N Oulkar, Meloth Thamban.’’ surface melting of a debris-covered glacier and its geomorphological control—A case study from Batal Glacier, western Himalaya’’. Geomorphology. Elsevier. 2023/6/15.DOI:  https://doi.org/10.1016/j.geomorph.2023.108686
  34. Polo López, Cristina S.; Lucchi, Elena ; Franco, Giovanna.’’ ACCEPTANCE OF BUILDING INTEGRATED PHOTOVOLTAIC (BIPV) IN HERITAGE BUILDINGS AND LANDSCAPES: POTENTIALS, BARRIER AND ASSESSMENT CRITERIA’’. Construction Pathology, Rehabilitation Technology and Heritage Management. REHABEND 2020 REHABEND 2020 Congress. http://repository.supsi.ch/id/eprint/12136
  35. Wang Y et al. ‘’Long short-term memory networks for solar irradiance prediction’’. Solar Energy. 2021;222:41 49.DOI: https://doi.org/10.1109/LGRS.2021.3107139
  36. Neeraj Priyadarshi , Pandav Kiran Maroti, Farooque Azam, Mohamed G. Hussien. ‘’An improved Z-source inverter-based sensorless induction motor-driven photovoltaic water pumping with Takagi–Sugeno fuzzy MPPT’’. IET Renewable Power Generation. IET Renew. Power Gener. 2022;1–14.DOI: https://doi.org/ 10.1049/rpg2.12654.
  37. Neeraj Priyadarshi, Sanjeevikumar Padmanaban , Jens Bo Holm-Nielsen, Frede Blaabjerg , and Mahajan Sagar Bhaskar. ‘’An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration under Fluctuating Sun Irradiance’’. IEEE SYSTEMS JOURNAL. DOI:. https://doi.org/10.1109/JSYST.2019.2949083.
  38. Neeraj Priyadarshi, Sanjeevikumar Padmanaban, Mahajan Sagar Bhaskar, Farooque Azam, Baseem Khan, Mohamed G. Hussien.’’ A novel hybrid grey wolf optimized fuzzy logic control based photovoltaic water pumping system’’. IET Renewable Power Generation. IET Renew. Power Gener. 2022; 1–12. DOI:. https://doi.org/10.1049/rpg2.12638.
  39. Neeraj Priyadarshi, P. Sanjeevikumar, MS Bhaskar, Farooque Azam, Ibrahim B. M. Taha, Mohamed G. Hussien.’’An adaptive TS-fuzzy model based RBF neural network learning for grid integrated photovoltaic applications’’. IET Renewable Power Generation. DOI:. https://doi.org/10.1049/rpg2.12505.
  40. Neeraj Priyadarshi, Sanjeevikumar Padmanaban, Mahajan Sagar Bhaskar, Baseem Khan.’’An experimental performance verification of continuous mixed P-norm based adaptive asymmetrical fuzzy logic controller for single stage photovoltaic grid integration’’. IET Renewable Power Generation. DOI:. https://doi.org/ 10.1049/rpg2.12410.
  41. Neeraj Priyadarshi, Pandav Kiran Maroti, Baseem Khan.’’ An adaptive grid integrated photovoltaic system with perturb T–S fuzzy based sliding mode controller MPPT tracker: An experimental realization’’. IET Renewable Power Generation DOI:. https://doi.org/10.1049/rpg2.12738.
  42. Ashish Singh Chauhan, Rajesh Singh, Neeraj Priyadarshi, Bhekisipho Twala, · Surindra Suthar, Siddharth Swami. ‘’Unleashing the power of advanced technologies for revolutionary medical imaging: pioneering the healthcare frontier with artificial intelligence’’.Discover Artificial Intelligence. (2024). https://doi.org/10.1007/s44163-024-00161-0
Majlesi Journal of Electrical Engineering
Volume 19, Issue 2
June 2025
Pages 1-14
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