10.1007/s40095-021-00416-x

Faults detection and identification in PV array using kernel principal components analysis

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  • Salomé Ndjakomo Essiane1,
  • Patrick Juvet Gnetchejo*1,
  • Pierre Ele2,
  • Zhicong Chen3,
  1. Laboratory of Technologies and Applied Sciences, University of Douala, Douala, CM Signal, Image and Systems Laboratory, Higher Technical Teacher Training College of Ebolowa, University of Yaounde 1, Yaoundé, CM
  2. Laboratory of Technologies and Applied Sciences, University of Douala, Douala, CM Laboratory of Electrical Engineering, Mechatronic and Signal Treatment, National Advanced School of Engineering, University of Yaounde 1, Yaoundé, CM
  3. College of Physics and Information Engineering, Fuzhou University, Fuzhou, CN

Published in Issue 2021-08-18

How to Cite

Ndjakomo Essiane, S., Gnetchejo, P. J., Ele, P., & Chen, Z. (2021). Faults detection and identification in PV array using kernel principal components analysis. International Journal of Energy and Environmental Engineering, 13(1 (March 2022). https://doi.org/10.1007/s40095-021-00416-x
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Abstract

Abstract The exponential growth of the photovoltaic system installations also requires an adequate maintenance and supervision system to ensure the continuity of service of the system. Conventional protection systems for electrical systems have shown their shortcomings for protecting photovoltaic systems. In this article, a statistical approach based on principal component analysis and its variants is used to detect and identify faults in a photovoltaic array. This involves analysing the variations of the data of the current–voltage and voltage–power characteristics. Subsequently, the calculation of the contributions is applied to the SPE index for the identification of faults. By employing the intermediate value theorem, six different operating states have been identified. The various results obtained first from the simulation model from the Simulink environment and then from a real system of 18 PV show that the kernel principal component analysis allows defect detection with a better precision.

Keywords

  • Photovoltaic system,
  • Kernel principal component analysis,
  • Fault detection,
  • Multivariate statistical analysis,
  • Fault identification,
  • I–V characteristics

References

  1. Ali et al. (2017) Real time fault detection in photovoltaic systems (pp. 914-923) https://doi.org/10.1016/j.egypro.2017.03.254
  2. Chamandoust et al. (2019) Tri-objective optimal scheduling of smart energy hub system with schedulable loads https://doi.org/10.1016/j.jclepro.2019.07.059
  3. Chamandoust et al. (2020) Multi-objectives optimal scheduling in smart energy hub system with electrical and thermal responsive loads 24(1) (pp. 209-232) https://doi.org/10.2478/rtuect-2020-0013
  4. Chamandoust et al. (2020) Multi-objective performance of smart hybrid energy system with multi-optimal participation of customers in day-ahead energy market https://doi.org/10.1016/j.enbuild.2020.109964
  5. Chamandoust et al. (2020) Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources https://doi.org/10.1016/j.est.2019.101112
  6. Chamandoust et al. (2020) Day-ahead scheduling problem of smart micro-grid with high penetration of wind energy and demand side management strategies https://doi.org/10.1016/j.seta.2020.100747
  7. Kannan and Vakeesan (2016) Solar energy for future world: a review (pp. 1092-1105) https://doi.org/10.1016/j.rser.2016.05.022
  8. Jäger-Waldau, A.: Snapshot of photovoltaics—February 2020. Energies
  9. 13
  10. (4), 4 (2020).
  11. https://doi.org/10.3390/en13040930
  12. Gnetchejo et al. (2021) Faults diagnosis in a photovoltaic system based on multivariate statistical analysis https://doi.org/10.1080/15567036.2021.19197923
  13. Alam et al. (2015) A comprehensive review of catastrophic faults in pv arrays: types, detection, and mitigation techniques 5(3) (pp. 982-997) https://doi.org/10.1109/JPHOTOV.2015.2397599
  14. NC PV DG Program SEPA Presentation. Duke Energy (2011)
  15. Coonick (2018) Fire and solar PV systems—investigations and evidence 2(9)
  16. Harrou et al. (2018) Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches (pp. 22-37) https://doi.org/10.1016/j.renene.2017.09.048
  17. Hare et al. (2016) Fault diagnostics in smart micro-grids: a survey (pp. 1114-1124) https://doi.org/10.1016/j.rser.2016.01.122
  18. Livera et al. (2019) Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems (pp. 126-143) https://doi.org/10.1016/j.renene.2018.09.101
  19. Madeti and Singh (2017) A comprehensive study on different types of faults and detection techniques for solar photovoltaic system (pp. 161-185) https://doi.org/10.1016/j.solener.2017.08.069
  20. Dolara et al. (2013) Experimental investigation of partial shading scenarios on PV (photovoltaic) modules (pp. 466-475) https://doi.org/10.1016/j.energy.2013.04.009
  21. Faranda, R., Leva, S., Lazaroiu, G.C.: Productivity comparison and performance improvement of a concentrated photovoltaic. In: 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, pp. 1073–1077 (2015).
  22. https://doi.org/10.1109/EEEIC.2015.7165314
  23. Syafaruddin, Karatepe, E., Hiyama, T.: Controlling of artificial neural network for fault diagnosis of photovoltaic array. In: 2011 16th International Conference on Intelligent System Applications to Power Systems, pp. 1–6 (2011).
  24. https://doi.org/10.1109/ISAP.2011.6082219
  25. Georgijevic et al. (2016) The detection of series arc fault in photovoltaic systems based on the arc current entropy 31(8) (pp. 5917-5930) https://doi.org/10.1109/TPEL.2015.2489759
  26. Silvestre et al. (2013) Automatic fault detection in grid connected PV systems (pp. 119-127) https://doi.org/10.1016/j.solener.2013.05.001
  27. Yi and Etemadi (2017) Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine 64(11) (pp. 8546-8556) https://doi.org/10.1109/TIE.2017.2703681
  28. Yao et al. (2014) Characteristic study and time-domain discrete-wavelet-transform based hybrid detection of series DC arc faults 29(6) (pp. 3103-3115) https://doi.org/10.1109/TPEL.2013.2273292
  29. Mekki et al. (2016) Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules (pp. 1-13) https://doi.org/10.1016/j.simpat.2016.05.005
  30. Zhao, Y., Yang, L., Lehman, B., de Palma, J.-F., Mosesian, J., Lyons, R.: Decision tree-based fault detection and classification in solar photovoltaic arrays. In: 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 93–99 (2012).
  31. https://doi.org/10.1109/APEC.2012.6165803
  32. Kumar et al. (2018) Online fault detection and diagnosis in photovoltaic systems using wavelet packets 8(1) (pp. 257-265) https://doi.org/10.1109/JPHOTOV.2017.2770159
  33. Kuo et al. (2017) Photovoltaic energy conversion system fault detection using fractional-order color relation classifier in microdistribution systems 8(3) (pp. 1163-1172) https://doi.org/10.1109/TSG.2015.2478855
  34. Fadhel (2019) PV shading fault detection and classification based on I–V curve using principal component analysis: application to isolated PV system (pp. 1-10) https://doi.org/10.1016/j.solener.2018.12.048
  35. Jiang, L.L., Maskell, D.L.: Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015).
  36. https://doi.org/10.1109/IJCNN.2015.7280498
  37. Platon et al. (2015) Online fault detection in PV systems 6(4) (pp. 1200-1207) https://doi.org/10.1109/TSTE.2015.2421447
  38. Garoudja, E., Kara, K., Chouder, A., Silvestre, S., Kichou, S.: Efficient fault detection and diagnosis procedure for photovoltaic systems. In: 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, Algeria, pp. 851–856 (2016).
  39. https://doi.org/10.1109/ICMIC.2016.7804233
  40. Prasad and Dash (2020) Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis https://doi.org/10.1002/2050-7038.12564
  41. Garoudja et al. (2017) An enhanced machine learning based approach for failures detection and diagnosis of PV systems (pp. 496-513) https://doi.org/10.1016/j.enconman.2017.09.019
  42. Karimi et al. (2020) A current based approach for hotspot detection in photovoltaic strings https://doi.org/10.1002/2050-7038.12517
  43. Georgijevic et al. (2020) Series arc fault detection in photovoltaic system by small-signal impedance and noise monitoring https://doi.org/10.1002/2050-7038.12234
  44. Chen et al. (2019) Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions https://doi.org/10.1016/j.enconman.2019.111793
  45. Chen et al. (2017) Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I–V characteristics (pp. 912-931) https://doi.org/10.1016/j.apenergy.2017.05.034
  46. Jolliffe, I.T.: Principal Component Analysis (2002). Accessed 06 Sept. 2020 [Online].
  47. https://doi.org/10.1007/b98835
  48. Fadhel, S. et al.: Data-driven approach for isolated PV shading fault diagnosis based on experimental I–V curves analysis. In: 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 927–932 (2018).
  49. https://doi.org/10.1109/ICIT.2018.8352302
  50. Gnetchejo et al. (2019) Important notes on parameter estimation of solar photovoltaic cell https://doi.org/10.1016/j.enconman.2019.111870
  51. Gnetchejo et al. (2019) Enhanced vibrating particles system algorithm for parameters estimation of photovoltaic system 07(08) (pp. 1-26) https://doi.org/10.4236/jpee.2019.78001
  52. Gnetchejo PJ, Ndjakomo Essiane S, Ele P, Wamkeue R, Mbadjoun Wapet D, Perabi Ngoffe S (2019) Reply to comment on ‘Important notes on parameter estimation of solar photovoltaic cell’, by Gnetchejo et al. [Energy Convers Manag.
  53. https://doi.org/10.1016/j.enconman.2019.111870
  54. ]. Energy Convers Manag 201:112132
  55. https://doi.org/10.1016/j.enconman.2019.112132
  56. Silvestre et al. (2015) Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions (pp. 42-50) https://doi.org/10.1016/j.energy.2015.03.123
  57. Harkat, M.-F.: Détection et localisation de défauts par analyse en composantes principales. Phd thesis, Institut National Polytechnique de Lorraine (2003). Accessed: 06 Sept 2020 [Online].
  58. https://hal.univ-lorraine.fr/tel-01749705
  59. Chakour, C.: Diagnostic et surveillance des procédés industriels et de leur environnement sur la base de l’analyse de données. thèse, Université Badji Mokhtar - Annaba (2016)
  60. Mansouri, M., Harkat, M.-F., Nounou, H.N., Nounou, M.N.: Chapter 2—PCA and PLS-based generalized likelihood ratio for fault detection. In: Mansouri, M., Harkat, M.-F., Nounou, H.N., Nounou, M.N. (eds.) Data-Driven and Model-Based Methods for Fault Detection and Diagnosis. Elsevier, pp. 11–48 (2020).
  61. https://doi.org/10.1016/B978-0-12-819164-4.00011-X
  62. Mansouri, M., Harkat, M.-F., Nounou, H.N., Nounou, M.N.: Chapter 3—Kernel PCA- and Kernel PLS-based generalized likelihood ratio tests for fault detection. In: Mansouri, M., Harkat, M.-F., Nounou, H.N., Nounou, M.N. (eds.) Data-Driven and Model-Based Methods for Fault Detection and Diagnosis. Elsevier, pp. 49–77 (2020).
  63. https://doi.org/10.1016/B978-0-12-819164-4.00012-1
  64. Dong and McAvoy (1996) Nonlinear principal component analysis—based on principal curves and neural networks 20(1) (pp. 65-78) https://doi.org/10.1016/0098-1354(95)00003-K
  65. Kwami, A.: Diagnostic de systèmes non linéaires par analyse en composantes principales à noyau. Université de Lorraine (2019) [Online].
  66. https://hal.univ-lorraine.fr/tel-02132788
  67. Alcala, C.F., Qin, S.J.: Reconstruction-based contribution for process monitoring with kernel principal component analysis. In: Proceedings of the 2010 American Control Conference, pp. 7022–7027 (2010).
  68. https://doi.org/10.1109/ACC.2010.5531315
  69. Choi et al. (2005) Fault detection and identification of nonlinear processes based on kernel PCA 75(1) (pp. 55-67) https://doi.org/10.1016/j.chemolab.2004.05.001
  70. Alcala and Qin (2009) Reconstruction-based contribution for process monitoring 45(7) (pp. 1593-1600) https://doi.org/10.1016/j.automatica.2009.02.027
  71. Elshenawy and Mahmoud (2018) Fault diagnosis of time-varying processes using modified reconstruction-based contributions (pp. 12-23) https://doi.org/10.1016/j.jprocont.2018.07.017