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

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.

Inspec keywords: parameter estimation; photovoltaic power systems; least squares approximations; maximum power point trackers; solar cell arrays

Other keywords: robust parameter estimation method; PV array model; influence function; WLS-based parameter estimation; weighted least squares; photovoltaic array model; C-PE method; undetermined parameters; partial shading condition; correntropy-based parameter estimation; current–voltage characteristic; solar panel equipment; maximum power point tracking

Subjects: Numerical approximation and analysis; DC-DC power convertors; Power electronics, supply and supervisory circuits; Photoelectric conversion; solar cells and arrays; Interpolation and function approximation (numerical analysis); Solar cells and arrays

References

    1. 1)
      • 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.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 29. Arora, N., Biegler, L.T.: ‘Redescending estimators for data reconciliation and parameter estimation’, Comput. Chem. Eng., 2001, 25, pp. 15851599.
    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)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • 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.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 25. Rahman, S. A., Varma, R. K., Vanderheide, T.: ‘Generalised model of a photovoltaic panel’, IET Renew. Power Gener., 2014, 8, (3), pp. 217229.
    14. 14)
      • 5. Yıldıran, N., Tacer, E.: ‘Identification of photovoltaic cell single diode discrete model parameters based on datasheet values’, Sol. Energy, 2016, 127, pp. 175183.
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 28. Walker, G.: ‘Evaluating MPPT converter topologies using a MATLAB PV model’, J. Electr. Electron. Eng., 2001, 21, (1), pp. 4956.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 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.
    27. 27)
      • 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.
    28. 28)
      • 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.
    29. 29)
      • 22. Askarzadeh, A., Rezazadeh, A.: ‘Artificial bee swarm optimization algorithm for parameters identification of solar cell models’, Appl. Energy, 2013, 102, pp. 943949.
    30. 30)
      • 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.
    31. 31)
      • 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.
    32. 32)
      • 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.
    33. 33)
      • 31. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., et al: ‘Robust statistics – The approach based on influence functions’ (Wiley, New York, 1986).
    34. 34)
      • 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.
    35. 35)
      • 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.
    36. 36)
      • 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.
    37. 37)
      • 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.
    38. 38)
      • 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.
    39. 39)
      • 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.
    40. 40)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5094
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5094
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading