Correntropy-based parameter estimation for photovoltaic array model considering partial shading condition

Correntropy-based parameter estimation for photovoltaic array model considering partial shading condition

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Analytical modelling of photovoltaic (PV) array is crucial for studying the current–voltage (I–V) characteristic of PV array and maximum power point tracking. A PV array model generally contains some undetermined parameters and the values of the parameters cannot be measured by sensors. It is difficult to correctly determine those model parameters. They should be estimated based on experimental data. Since the experimental data gathered from the solar panel equipment usually contain random and gross errors, a robust parameter estimation method, correntropy-based parameter estimation (C-PE) is proposed for PV array model considering partial shading condition here. First, the theoretical model of PV array considering partial shading condition is investigated. Second, compared with the most common estimator, weighted least squares (WLS), robustness of the proposed correntropy estimator is analysed by using influence function (IF), and then C-PE method is developed for the PV array model. The WLS-based parameter estimation (WLS-PE) and C-PE methods are used in the simulation example. The results show that the C-PE method is more robust than WLS-PE method. Finally, the experimental data of PV array under ideal condition and partial shading condition are also used to demonstrate the feasibility and effectiveness of C-PE method.


    1. 1)
      • 1. Ishaque, K., Salam, Z.: ‘A comprehensive MATLAB Simulink PV system simulator with partial shading capability based on two-diode model’, Sol. Energy, 2011, 85, (9), pp. 22172227.
    2. 2)
      • 2. Lattanzi, E., Dromedari, M., Freschi, V., et al: ‘Tuning the complexity of photovoltaic array models to meet real-time constraints of embedded energy emulators’, Energies, 2017, 10, (3), p. 278.
    3. 3)
      • 3. Villalva, M.G., Gazoli, J.R., Ruppert Filho, E.: ‘Comprehensive approach to modelling and simulation of photovoltaic arrays’, IEEE Trans. Power Electron., 2009, 24, (5), pp. 11981208.
    4. 4)
      • 4. Mohammadnejad, S., Khalafi, A., Ahmadi, S.M.: ‘Mathematical analysis of total-cross-tied photovoltaic array under partial shading condition and its comparison with other configurations’, Sol. Energy, 2016, 133, pp. 501511.
    5. 5)
      • 5. Yıldıran, N., Tacer, E.: ‘Identification of photovoltaic cell single diode discrete model parameters based on datasheet values’, Sol. Energy, 2016, 127, pp. 175183.
    6. 6)
      • 6. Cárdenas, A.A., Carrasco, M., Mancilla-David, F., et al: ‘Experimental parameter extraction in the single-diode photovoltaic model via a reduced-space search’, IEEE Trans. Ind. Electron., 2017, 64, (2), pp. 14681476.
    7. 7)
      • 7. Wang, Y.J., Hsu, P.C.: ‘Analytical modelling of partial shading and different orientation of photovoltaic modules’, IET Renew. Power Gener., 2010, 4, (3), pp. 272282.
    8. 8)
      • 8. Balasubramanian, I.R., Ganesan, S.I., Chilakapati, N.: ‘Impact of partial shading on the output power of PV systems under partial shading conditions’, IET Power Electron., 2013, 7, (3), pp. 657666.
    9. 9)
      • 9. Bastidas-Rodriguez, J.D., Franco, E., Petrone, G., et al: ‘Maximum power point tracking architectures for photovoltaic systems in mismatching conditions: a review’, IET Power Electron., 2014, 7, (6), pp. 13961413.
    10. 10)
      • 10. Aldaoudeyeh, A.M.I.: ‘Photovoltaic-battery scheme to enhance PV array characteristics in partial shading conditions’, IET Renew. Power Gener., 2016, 10, (1), pp. 108115.
    11. 11)
      • 11. Lappalainen, K., Valkealahti, S.: ‘Effects of irradiance transition characteristics on the mismatch losses of different electrical PV array configurations’, IET Renew. Power Gener., 2017, 11, (2), pp. 248254.
    12. 12)
      • 12. Pendem, S. R., Mikkili, S.: ‘Modelling and performance assessment of PV array topologies under partial shading conditions to mitigate the mismatching power losses’, Sol. Energy, 2018, 160, pp. 303321.
    13. 13)
      • 13. Alonso-Garcia, M.C., Ruiz, J.M., Chenlo, F.: ‘Experimental study of mismatch and shading effects in the IV characteristic of a photovoltaic module’, Sol. Energy Mater. Sol. Cells, 2006, 90, (3), pp. 329340.
    14. 14)
      • 14. Gasparin, F.P., Bühler, A.J., Rampinelli, G.A., et al: ‘Statistical analysis of IV curve parameters from photovoltaic modules’, Sol. Energy, 2016, 131, pp. 3038.
    15. 15)
      • 15. Mamun, M. A. A., Hasanuzzaman, M., Selvaraj, J.: ‘Experimental investigation of the effect of partial shading on photovoltaic performance’, IET Renew. Power Gener., 2017, 11, (7), pp. 912921.
    16. 16)
      • 16. Liu, F., Li, R., Li, Y., et al: ‘Takagi–sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting’, IET Renew. Power Gener., 2017, 11, (10), pp. 12811287.
    17. 17)
      • 17. Mahmoud, Y.A., Xiao, W., Zeineldin, H.H.: ‘A parameterization approach for enhancing PV model accuracy’, IEEE Trans. Ind. Electron., 2013, 60, (12), pp. 57085716.
    18. 18)
      • 18. Orioli, A., Di Gangi, A.: ‘A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data’, Appl. Energy, 2013, 102, pp. 11601177.
    19. 19)
      • 19. Laudani, A., Fulginei, F.R., Salvini, A.: ‘Identification of the one-diode model for photovoltaic modules from datasheet values’, Sol. Energy, 2014, 108, pp. 432446.
    20. 20)
      • 20. AlHajri, M.F., El-Naggar, K.M., AlRashidi, M.R., et al: ‘Optimal extraction of solar cell parameters using pattern search’, Renew. Energy, 2012, 44, pp. 238245.
    21. 21)
      • 21. Ismail, M.S., Moghavvemi, M., Mahlia, T.M.I.: ‘Characterization of PV panel and global optimization of its model parameters using genetic algorithm’, Energy Convers. Manage., 2013, 73, pp. 1025.
    22. 22)
      • 22. Askarzadeh, A., Rezazadeh, A.: ‘Artificial bee swarm optimization algorithm for parameters identification of solar cell models’, Appl. Energy, 2013, 102, pp. 943949.
    23. 23)
      • 23. Lim, L.H.I., Ye, Z., Ye, J., et al: ‘A linear identification of diode models from single IV characteristics of PV panels’, IEEE Trans. Ind. Electron., 2015, 62, (7), pp. 41814193.
    24. 24)
      • 24. Ting, T.O., Ma, J., Kim, K.S., et al: ‘Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model’, Appl. Soft Comput., 2016, 40, pp. 5863.
    25. 25)
      • 25. Rahman, S. A., Varma, R. K., Vanderheide, T.: ‘Generalised model of a photovoltaic panel’, IET Renew. Power Gener., 2014, 8, (3), pp. 217229.
    26. 26)
      • 26. Moshksar, E., Ghanbari, T.: ‘Constrained optimisation approach for parameter estimation of PV modules with single-diode equivalent model’, IET Renew. Power Gener., 2018, 12, (12), pp. 13981404.
    27. 27)
      • 27. Alghuwainem, S.M.: ‘A close-form solution for the maximum-power operating point of a solar cell array’, Solar Energy Mater. Solar Cells, 1997, 46, (3), pp. 249257.
    28. 28)
      • 28. Walker, G.: ‘Evaluating MPPT converter topologies using a MATLAB PV model’, J. Electr. Electron. Eng., 2001, 21, (1), pp. 4956.
    29. 29)
      • 29. Arora, N., Biegler, L.T.: ‘Redescending estimators for data reconciliation and parameter estimation’, Comput. Chem. Eng., 2001, 25, pp. 15851599.
    30. 30)
      • 30. Liu, W., Pokharel, P.P., Príncipe, J.C.: ‘Correntropy: properties and applications in non-Gaussian signal processing’, IEEE Trans. Signal Process., 2007, 55, pp. 52865298.
    31. 31)
      • 31. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., et al: ‘Robust statistics – The approach based on influence functions’ (Wiley, New York, 1986).
    32. 32)
      • 32. Özyurt, D.B., Pike, R.W.: ‘Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes’, Comput. Chem. Eng., 2004, 28, (3), pp. 381402.
    33. 33)
      • 33. Zhang, Z., Shao, Z., Chen, X., et al: ‘Quasi-weighted least squares estimator for data reconciliation’, Comput. Chem. Eng., 2010, 34, (2), pp. 154162.
    34. 34)
      • 34. Xiao, W., Lind, M. G. J., Dunford, W. G., et al: ‘Real-time identification of optimal operating points in photovoltaic power systems’, IEEE Trans. Ind. Electron., 2006, 53, (4), pp. 10171026.
    35. 35)
      • 35. Hiyama, T., Kouzuma, S., Imakubo, T.: ‘Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control’, IEEE Trans. Energy Convers., 1995, 10, (2), pp. 360367.
    36. 36)
      • 36. Boztepe, M., Guinjoan, F., Velasco-Quesada, G., et al: ‘Global MPPT scheme for photovoltaic string inverters based on restricted voltage window search algorithm’, IEEE Trans. Ind. Electron., 2014, 61, (7), pp. 33023312.
    37. 37)
      • 37. Ishaque, K., Salam, F.: ‘A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition’, Renew. Sust. Energy Rev., 2013, 19, (1), pp. 475488.
    38. 38)
      • 38. Mellit, A., Tina, G. M., Kalogirou, S. A.: ‘Fault detection and diagnosis methods for photovoltaic systems: a review’, Renew. Sust. Energy Rev., 2018, 91, pp. 117.
    39. 39)
      • 39. Belaout, A., Krim, F., Mellit, A., et al: ‘Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification’, Renew. Energy, 2018, 127, pp. 548558.
    40. 40)
      • 40. Tapia, R., Fuerte-Esquivel, C. R., Espinosa-Juarez, E., et al: ‘Steady-state model of grid-connected photovoltaic generation for power flow analysis’, IEEE Trans. Power Syst., 2018, 33, (5), pp. 57275737.

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