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Intelligent shading fault detection in a PV system with MPPT control using neural network technique

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Abstract

Photovoltaic (PV) power generation systems know widespread in the power generation world due to their production efficiency of clean energy. This system is exposed to several faults and errors during the production process, which reduces the quality and quantity of the produced energy, among the most common defects is partial shading. This paper proposes a simplified method for fault detection based on the generation of residual signals sensitive to these faults. For this detection, we have developed a model of the healthy photovoltaic system based on an artificial neural network (ANN). The output of this model is compared to the PV generator controlled by maximum power point tracking (MPPT) to form a residue used to feed a mechanism dedicated to fault detection. For the detection mechanism, an ANN was used as a fault classifier. The proposed method makes it possible to determine the percentage of partial shading, even in the presence of climate change. The results have been verified and validated using MATLAB/Simulink.

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References

  1. Harrou, F., Saidi, A., Sun, Y., Khadraoui, S.: Monitoring of photovoltaic systems using improved kernel-based learning schemes. IEEE J. Photovolt. 11(3), 806–818 (2021)

    Article  Google Scholar 

  2. Garoudja E, Harrou F, Sun Y, Kara K, Chouder A, Silvestre S: A statistical-based approach for fault detection and diagnosis in a photovoltaic system. 6th International Conference on Systems and Control (ICSC) 75–80 (2017)

  3. Harrou, F., Taghezouit, B., Sun, Y.: Improved k NN-based monitoring schemes for detecting faults in pv systems. IEEE J Photovolt. 9(3), 811–821 (2019)

    Article  Google Scholar 

  4. Ciampi, A., Appice, A., Malerba, D., Muolo, A.: An intelligent system for real time fault detection in PV plants. Sustainability in Energy and Buildings, pp. 235–244. Springer, Berlin (2012)

    Google Scholar 

  5. Li, Q., Zhao, S., Wang, M., Zou, Z., Wang, B., Chen, Q.: An improved perturbation and observation maximum power point tracking algorithm based on a PV module four-parameter model for higher efficiency. Appl. Energy 195, 523–537 (2017)

    Article  Google Scholar 

  6. Obukhov, S., Ibrahim, A., Zaki Diab, A.-A., Al-Sumaiti, A.S., Aboelsaud, R.: Optimal performance of dynamic particle swarm optimization based maximum power trackers for stand-alone pv system under partial shading conditions. IEEE Access 8, 20770–20785 (2020)

    Article  Google Scholar 

  7. Chao, K.-H., Rizal, M.-N.: A hybrid MPPT controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions. Energies 14(10), 2902 (2021)

    Article  Google Scholar 

  8. Rajavel, A., Rathina Prabha, N.: Fuzzy logic controller-based boost and buck-boost converter for maximum power point tracking in solar system. Trans Inst Meas Control 43(4), 945–957 (2021)

    Article  Google Scholar 

  9. Khan, U.-H., Khan, Q., Khan, L., Alam, W., Ali, N., Khan, I., Khan, R.-A.: MPPT control paradigms for pmsg-wecs: a synergistic control strategy with gain-scheduled sliding mode observer. IEEE Access 9, 139876–139887 (2021)

    Article  Google Scholar 

  10. Levron, Y., Shmilovitz, D.: Maximum power point tracking employing sliding mode control. IEEE Trans Circuits Syst. Regul. Pap. 60(3), 724–732 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Miqoi, S., El Ougli, A., Tidhaf, B.: Adaptive fuzzy sliding mode based MPPT controller for a photovoltaic water pumping system. Int. J. Power Electron Drive Syst. 10(1), 414–422 (2019)

    Google Scholar 

  12. Nguyen, X.-H.: Matlab/Simulink based modeling to study effect of partial shadow on solar photovoltaic array. Environ. Syst. Res. 4(1), 1–10 (2015)

    Article  Google Scholar 

  13. Basnet, B., Chun, H., Bang, J.: An intelligent fault detection model for fault detection in photovoltaic systems. J. Sens. 2020, 1–11 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Alam M-K, Khan F, Johnson J, Flicker J: PV ground-fault detection using spread spectrum time domain reflectometry (SSTDR). IEEE energy conversion congress and exposition 1015–1020 (2013)

  16. Jiang L-L, Maskell D-L: Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods. International Joint Conference on Neural Networks (IJCNN) 1–8 (2015)

  17. Pei, T., Hao, X.: A fault detection method for photovoltaic systems based on voltage and current observation and evaluation. Energies 12(9), 2–16 (2019)

    Article  Google Scholar 

  18. Fadhel, S., Delpha, C., Diallo, D., Bahri, I., Migan, A., Trabelsi, M., Mimounid, M.-F.: PV shading fault detection and classification based on IV curve using principal component analysis: application to isolated PV system. Sol. Energy 179, 1–10 (2019)

    Article  Google Scholar 

  19. Kofinas, P., Dounis, A.-I., Papadakis, G., Assimakopoulos, M.-N.: An Intelligent MPPT controller based on direct neural control for partially shaded PV system. Energy Build 1(90), 51–64 (2015)

    Article  Google Scholar 

  20. Punitha, K., Devaraj, D., Sakthivel, S.: Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 1(62), 330–340 (2013)

    Article  Google Scholar 

  21. Alhabib, A., Itako, K., Kudoh, T.: Development of real-time hotspot detection system utilizing artificial intelligence in pv generation system. J. Adv. Sci. 32, 1–7 (2020)

    Article  Google Scholar 

  22. Winston, D.-P., Murugan, M.-S., Elavarasan, R.-M., Pugazhendhi, R., Singh, O.-J., Murugesan, P., Hossain, E.: Solar PV’s micro crack and hotspots detection technique using NN and SVM. IEEE Access 9, 127259–127269 (2021)

    Article  Google Scholar 

  23. Salehi, M., Farhadi, S., Moieni, A., Safaie, N., Hesami, M.: A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in corylus avellana cell culture. Plant Methods 17(1), 1–13 (2021)

    Article  Google Scholar 

  24. Talhaoui, H., Ameid, T., Kessal, A.: Energy eigenvalues and neural network analysis for broken bars fault diagnosis in induction machine under variable load: experimental study. J. Ambient Intell Hum. Comput. (2021). https://doi.org/10.1007/s12652-021-03172-2

    Article  Google Scholar 

  25. Pillai, D.-S., Blaabjerg, F., Rajasekar, N.: A comparative evaluation of advanced fault detection approaches for PV systems. IEEE J. Photovolt. 9(2), 513–527 (2019)

    Article  Google Scholar 

  26. Raiker, G.-A., Loganathan, U., Reddy, S.-B.: Current control of boost converter for PV interface with momentum-based perturb and observe MPPT. IEEE Trans. Ind. Appl. 57(4), 4071–4079 (2021)

    Article  Google Scholar 

  27. Khan, S., Zaid, M., Mahmood, A., Nooruddin, A.-S., Ahmad, J., Alghaythi, M.-L., Lin, C.-H.: A New transformerless ultra high gain DC–DC converter for dc microgrid application. IEEE Access 9, 124560–124582 (2021)

    Article  Google Scholar 

  28. Baharudin, N.H., Mansur, M.N.T.T., Hamid, A.F., Ali, R., Misrun, M.I.: Topologies of DC-DC converter in solar PV applications. Indones. J. Electr. Eng. Comput. Sci. 8(2), 368–374 (2017)

    Google Scholar 

  29. Paz, F., Ordonez, M.: Zero oscillation and irradiance slope tracking for photovoltaic MPPT. IEEE Trans. Ind. Electron 61(11), 6138–6147 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Grimaccia, F., Leva, S., Mussetta, M., Ogliari, E.: Analysis and validation of ANN PV power output forecasting at 24 hours ahead. Math. Comput. Simul. 131, 88–100 (2017)

    Article  MATH  Google Scholar 

  32. Yuan, Z., Song, N., Pan, X., Song, J., Ma, F.: fault detection, isolation, and reconstruction for satellite attitude sensors using an adaptive hybrid method. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)

    Google Scholar 

  33. Bouchriha I, Ghanem A B, Nouri K: MPPT control of a photovoltaic system based on sliding mode control. International Conference on Advanced Systems and Emergent Technologies (IC_ASET) 12–16 (2019)

  34. Robles Algarín, C., Taborda Giraldo, J., Rodriguez Alvarez, O.: Fuzzy logic based MPPT controller for a PV system. Energies 10(12), 1–18 (2017)

    Google Scholar 

  35. Kinattingal, S., Simon, S.P., Nayak, P.S.R.: MPPT in PV systems using ant colony optimisation with dwindling population. IET Renew Power Gener. 14(7), 1105–1112 (2020)

    Article  Google Scholar 

  36. Merchaoui M, Sakly A, Mimouni M F: Improved fast particle swarm optimization based PV MPPT. 9th international renewable energy congress (IREC) 1–7 (2018)

  37. Nazer, M.N.R., Noorwali, A., Tajuddin, M.F.N., Khan, M.Z., Tazally, M.A.I.A., Ahmed, J., Kumar, N.M.: Scenario-based investigation on the effect of partial shading condition patterns for different static solar photovoltaic array configurations. IEEE Access 9, 116050–116072 (2021)

    Article  Google Scholar 

  38. Vadivel, S., Boopthi, C.-S., Ramasamy, S., Ahsan, M.: Haider J, Rodrigues E-M: performance enhancement of a partially shaded photovoltaic array by optimal reconfiguration and current injection schemes. Energies 14(19), 6332 (2021)

    Article  Google Scholar 

  39. Boggarapu, P.-K., Pillai, D.-S., Rajasekar, N., Manickam, C., Ganesan, S.-I.: Identification and localization of array faults with optimized placement of voltage sensors in a PV system. IEEE Trans. Ind. Electron 68(7), 5921–5931 (2020)

    Google Scholar 

  40. Garcìa, M.-A., Herrmann, W., Böhmer, W., Proisy, B.: Thermal and electrical effects caused by outdoor hot-spot testing in associations of photovoltaic cells. Prog. Photovolt. Res. Appl. 11(5), 293–307 (2003)

    Article  Google Scholar 

  41. García, E., Ponluisa, N., Quiles, E., Zotovic-Stanisic, R., Gutiérrez, S.C.: Solar panels string predictive and parametric fault diagnosis using low-cost sensors. Sensors 22(1), 332 (2022)

    Article  Google Scholar 

  42. Mellit, A., Kalogirou, S.: Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems. Renew Energy 184, 1074–1090 (2022)

    Article  Google Scholar 

  43. Yurtseven, K., Karatepe, E., Deniz, E.: Sensorless fault detection method for photovoltaic systems through mapping the inherent characteristics of PV plant site: simple and practical. Sol. Energy 216, 96–110 (2021)

    Article  Google Scholar 

  44. Colmenares-Quintero, R.F., Rojas-Martinez, E.R., Macho-Hernantes, F., Stansfield, K.E., Colmenares-Quintero, J.C.: Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks. Cogent Eng. 8(1), 1981520 (2021)

    Article  Google Scholar 

  45. Raeisi H A, Sadeghzadeh S M: A novel experimental and approach of diagnosis, partial shading, and fault detection for domestic purposes photovoltaic system using data exchange of adjacent panels. Int J Photoenergy (2021)

  46. Rouani, L., Harkat, M.F., Kouadri, A., Mekhilef, S.: Shading fault detection in a grid-connected PV system using vertices principal component analysis. Renew Energy 164, 1527–1539 (2021)

    Article  Google Scholar 

  47. Rakesh, N., Banerjee, S., Subramaniam, S., Babu, N.: A simplified method for fault detection and identification of mismatch modules and strings in a grid-tied solar photovoltaic system. Int. J. Emerg. Electr. Power Syst. 21(4), 20200001 (2020)

    Google Scholar 

  48. Sathyanarayana, P., Ballal, R., Sagar, L., Kumar, G.: Effect of shading on the performance of solar PV panel. Energy Power 5(1A), 1–4 (2015)

    Google Scholar 

  49. Kaushika, N.-D., Rai, A.-K.: An investigation of mismatch losses in solar photovoltaic cell networks. Energy 32(5), 755–759 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. LPMRN Laboratory, University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria

    Fethallah Tati, Hicham Talhaoui & Oualid Aissa

  2. Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement Des Energies Renouvelables (CDER), 01000, Adrar, Algeria

    Abdeldjalil Dahbi

  3. Laboratory of Sustainable Development and Computing (L.D.D.I), University of Adrar Department of Electrical Engineering, Adrar University, 1000, Adrar, Algeria

    Abdeldjalil Dahbi

Authors
  1. Fethallah Tati
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  2. Hicham Talhaoui
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Correspondence to Fethallah Tati.

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Appendices

Appendices

Appendix A: Specifications of PV array

Maximum power (Pmp)

200.143 (W)

Maximum voltage (Vmp)

26.3 (V)

Maximum current (Imp)

7.61 (A)

Open circuit voltage (Voc)

32.9 (V)

Short circuit current (Isc)

8.21 (A)

Temperature coefficient short circuit (Ki)

0.00318 (A/°C)

Temperature coefficient open circuit (Kv)

−0.123 (V/°C)

Number of cells per module (Ns)

54

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Tati, F., Talhaoui, H., Aissa, O. et al. Intelligent shading fault detection in a PV system with MPPT control using neural network technique. Int J Energy Environ Eng 13, 1147–1161 (2022). https://doi.org/10.1007/s40095-022-00486-5

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  • DOI: https://doi.org/10.1007/s40095-022-00486-5

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