access icon free Simultaneous fault detection algorithm for grid-connected photovoltaic plants

© The Institution of Engineering and Technology
Received 06/03/2017, Accepted 27/07/2017, Revised 24/06/2017, Published 27/07/2017

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

Inspec keywords: virtual instrumentation; photovoltaic power systems; power grids; maximum power point trackers; power engineering computing; fault diagnosis; statistical analysis

Other keywords: PV fault detection algorithm; Bypass diode; LabVIEW software; grid-connected photovoltaic plants; University of Huddersfield; t-test statistical analysis method; simultaneous fault detection algorithm; faulty maximum power point tracking unit; GCPV system; UK; virtual instrumentation; power 1.98 kW

Subjects: Computerised instrumentation; Solar power stations and photovoltaic power systems; Other topics in statistics; Other topics in statistics; Power engineering computing; DC-DC power convertors; Computerised instrumentation

References

    1. 1)
      • 14. Chine, W., Mellit, A., Pavan, A.M., et al: ‘Fault detection method for grid-connected photovoltaic plants’, Renew. Energy, 2014, 66, pp. 99–110.
    2. 2)
      • 8. Dhimish, M., Holmes, V., Mehrdadi, B., et al: ‘The impact of cracks on photovoltaic power performance’, J. Sci.: Adv. Mater. Devices, 2017, 2, (2), pp. 199–209.
    3. 3)
      • 1. Ondraczek, J.: ‘Are we there yet? Improving solar PV economics and power planning in developing countries: The case of Kenya’, Renew. Sust. Energy Rev., 2014, 30, pp. 604–615.
    4. 4)
      • 7. Shah, N., Rajagopalan, C.: ‘Experimental investigation of a multifunctional grid interactive photovoltaic system operating in partial shading conditions’, IET Renew. Power Gener., 2016, 10, (9), pp. 1382–1392.
    5. 5)
      • 2. Dhimish, M., Holmes, V., Mehrdadi, B., et al: ‘Diagnostic method for photovoltaic systems based on six layer detection algorithm’, Electr. Power Syst. Res., 2017, 151, (C), pp. 26–39, doi: 10.1016/j.epsr.2017.05.024.
    6. 6)
      • 9. Dhimish, M., Holmes, V., Dales, M., et al: ‘The effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements’, Micro Nano Lett., 2017, DOI: 10.1049/mnl.2017.0205.
    7. 7)
      • 11. Karabiber, A., Keles, C., Kaygusuz, A., et al: ‘An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids’, Renew. Energy, 2013, 52, pp. 251–259.
    8. 8)
      • 18. Kim, K.A., Seo, G.S., Cho, B.H., et al: ‘Photovoltaic hot-spot detection for solar panel substrings using ac parameter characterization’, IEEE Trans. Power Electron., 2016, 31, (2), pp. 1121–1130.
    9. 9)
      • 5. Mojallizadeh, M.R., Badamchizadeh, M., Khanmohammadi, S., et al: ‘Chattering free full-order terminal sliding-mode control for maximum power point tracking of photovoltaic cells’, IET Renew. Power Gener., 2016, 11, (1), pp. 85–91.
    10. 10)
      • 23. Dhimish, M., Holmes, V., Dales, M.: ‘Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure’. 2016 Fourth Int. Symp. on Environmental Friendly Energies and Applications (EFEA), Belgrade, 2016, pp. 1–6, doi: 10.1109/EFEA.2016.7748777.
    11. 11)
      • 15. Chine, W., Mellit, A., Lughi, V., et al: ‘A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks’, Renew. Energy, 2016, 90, pp. 501–512.
    12. 12)
      • 4. Karbakhsh, F., Amiri, M., Zarchi, H.A.: ‘Two-switch flyback inverter employing a current sensorless MPPT and scalar control for low cost solar powered pumps’, IET Renew. Power Gener., 2016, 11, (5), pp. 669–677.
    13. 13)
      • 16. Dhimish, M., Holmes, V.: ‘Fault detection algorithm for grid-connected photovoltaic plants’, Sol. Energy, 2016, 137, pp. 236–245.
    14. 14)
      • 3. Lasheen, M., Rahman, A.K.A., Abdel-Salam, M., et al: ‘Adaptive reference voltage-based MPPT technique for PV applications’, IET Renew. Power Gener., 2017, 11, (5), pp. 715–722.
    15. 15)
      • 17. Platon, R., Martel, J., Woodruff, N., et al: ‘Online fault detection in PV systems’, IEEE Trans. Sust. Energy, 2015, 6, (4), pp. 1200–1207.
    16. 16)
      • 26. Dhimish, M., Holmes, V., Dales, M.: ‘Parallel fault detection algorithm for grid-connected photovoltaic plants’, Renew. Energy, 2017, 113, pp. 94–111.
    17. 17)
      • 25. Wail, R., Mouss, N.K., Mouss, L.H., et al: ‘A regression algorithm for the smart prognosis of a reversed polarity fault in a photovoltaic generator’. 2014 Int. Conf. on Green Energy, March 2014, pp. 134–138.
    18. 18)
      • 10. Jelle, B.P.: ‘The challenge of removing snow downfall on photovoltaic solar cell roofs in order to maximize solar energy efficiency—Research opportunities for the future’, Energy Build., 2013, 67, pp. 334–351.
    19. 19)
      • 22. Miller, J.N., Miller, J.C.: ‘Statistics and chemometrics for analytical chemistry’ (Pearson Education, 2005).
    20. 20)
      • 20. Dhimish, M., Holmes, V., Mehrdadi, B., et al: ‘Multi-layer photovoltaic fault detection algorithm’, High Volt., 2017, DOI: 10.1049/hve.2017.0044.
    21. 21)
      • 12. Tadj, M., Benmouiza, K., Cheknane, A., et al: ‘Improving the performance of PV systems by faults detection using GISTEL approach’, Energy Convers. Manage., 2014, 80, pp. 298–304.
    22. 22)
      • 21. Sera, D., Teodorescu, R., Rodriguez, P.: ‘PV panel model based on datasheet values’. Int. Symp. on Industrial Electronics, 2007 (ISIE 2007), June 2007, pp. 2392–2396.
    23. 23)
      • 24. Silvestre, S., da Silva, M.A., Chouder, A., et al: ‘New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators’, Energy Convers. Manage., 2014, 86, pp. 241–249.
    24. 24)
      • 6. Dhimish, M., Holmes, V., Mehrdadi, B., et al: ‘Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions’, Renew. Energy, 2017, (113), pp. 438–460.
    25. 25)
      • 13. Takashima, T., Yamaguchi, J., Otani, K., et al: ‘Experimental studies of fault location in PV module strings’, Sol. Energy Mater. Sol. Cells, 2009, 93, (6), pp. 1079–1082.
    26. 26)
      • 19. Silvestre, S., Kichou, S., Chouder, A., et al: ‘Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions’, Energy, 2015, 86, pp. 42–50.
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