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Sensitivity analysis of a new approach to photovoltaic parameters extraction based on the total least squares method

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Języki publikacji
EN
Abstrakty
EN
The degradation of photovoltaic modules and their subsequent loss of performance has a serious impact on the total energy generation potential. The lack of real-time information on the output power leads to additional losses since the panels may not be operating at their optimal point. To understand the behaviour, numerically simulate the characteristics and identify the optimal operating point of a photovoltaic cell, the parameters of an equivalent electrical circuit must first be identified. The aim of this work is to develop a total least-squares based algorithm which can identify those parameters from the output voltage and current measurements, taking into consideration the uncertainties on both measured quantities. This work presents a comparative study of the Ordinary Least Squares (OLS) and Total Least Squares (TLS) approaches to the estimation of the parameters of a photovoltaic cell.
Rocznik
Strony
751--765
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr., wzory
Twórcy
  • University of Évora, Department of Mechatronics, R. Romão Ramalho 59, 7000-671 Évora, Portugal
  • Instrumentation and Control Laboratory, Institute of Earth Sciences, Évora, Portugal
  • University of Évora, Department of Mechatronics, R. Romão Ramalho 59, 7000-671 Évora, Portugal
  • Instrumentation and Control Laboratory, Institute of Earth Sciences, Évora, Portugal
  • University of Évora, Department of Mechatronics, R. Romão Ramalho 59, 7000-671 Évora, Portugal
  • Instrumentation and Control Laboratory, Institute of Earth Sciences, Évora, Portugal
  • Instituto de Telecomunicações, Lisbon, Portugal
  • University of Chouaib Doukkali, Energy Engineering Laboratory, National School of Applied Sciences, El Jadida, Morocco
  • University of Chouaib Doukkali, Energy Engineering Laboratory, National School of Applied Sciences, El Jadida, Morocco
Bibliografia
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  • [4] Mao, M., Cui, L., Zhang, Q., Guo, K., Zhou, L., & Huang, H. (2020). Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies. Energy Reports, 6, 1312-1327.
  • [5] Ahmed, M. T., Rashel, M. R., Faisal, F., & Tlemçani, M. (2020). Non-iterative MPPT Method: A Comparative Study. International Journal of Renewable Energy Research (IJRER), 10(2), 549-557.
  • [6] Azmi, F. F. A., Sahraoui, B., & Muzakir, S. K. (2019). Study of ZnO nanospheres fabricated via thermal evaporation for solar cell application. Makara Journal of Technology, 23(1), 11-15.
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  • [8] Bader, S., Ma, X., & Oelmann, B. (2020). A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels. IEEE Access, 8, 172057-172064. https://doi.org/10.1016/10.1109/ACCESS.2020.3025146
  • [9] Ciani, L., Catelani, M., Carnevale, E. A., Donati, L., & Bruzzi, M. (2015). Evaluation of the Aging Process of Dye-Sensitized Solar Cells Under Different Stress Conditions. IEEE Transactions on Instrumentation and Measurement, 64(5), 1179-1187. https://doi.org/10.1109/TIM.2014.2381352
  • [10] Ndiaye, A., Charki, A., Kobi, A., Kébé, C. M. F., Ndiaye, P. A., & Sambou, V. (2013). Degradations of silicon photovoltaic modules: A literature review. Solar Energy, 96, 140-151. https://doi.org/10.1016/10.1016/j.solener.2013.07.005
  • [11] Lay-Ekuakille, A., Ciaccioli, A., Griffo, G., Visconti, P., & Andria, G. (2018). Effects of dust on photovoltaic measurements: A comparative study. Measurement, 113, 181-188. http://dx.doi.org/10.1016/10.1016/j.measurement.2017.06.025
  • [12] Cristaldi, L., Faifer, M., Rossi, M., Toscani, S., Catelani, M., Ciani, L., & Lazzaroni, M. (2014). Simplified method for evaluating the effects of dust and aging on photovoltaic panels. Measurement, 54, 207-214. https://doi.org/10.1016/j.measurement.2014.03.001
  • [13] Carullo, A., Ferraris, F., Vallan, A., Spertino, F., & Attivissimo, F. (2014). Uncertainty analysis of degradation parameters estimated in long-term monitoring of photovoltaic plants. Measurement, 55, 641-649. https://doi.org/10.1016/j.measurement.2014.06.003
  • [14] Cubas, J., Pindado, S., & Victoria, M. (2014). On the analytical approach for modeling photovoltaic systems behavior. Journal of Power Sources, 247, 467-474. https://doi.org/10.1016/j.jpowsour.2013.09.008
  • [15] Batzelis, E. I., & Papathanassiou, S. A. (2016). A Method for the Analytical Extraction of the Single-Diode PV Model Parameters. IEEE Transactions on Sustainable Energy, 7(2), 504-512. https://doi.org/10.1109/TSTE.2015.2503435
  • [16] Hassan Ali, M., Rabhi, A., Haddad, S., & El Hajjaji, A. (2017). Real-Time Determination of Solar Cell Parameters. Journal of Electronic Materials, 46(11), 6535-6543. https://doi.org/10.1016/10.1007/s11664-017-5697-0
  • [17] Subudhi, B., & Pradhan, R. (2018). Bacterial Foraging Optimization Approach to Parameter Extraction of a Photovoltaic Module. IEEE Transactions on Sustainable Energy, 9(1), 381-389. https://doi.org/10.1109/TSTE.2017.2736060
  • [18] Long, W., Cai, S., Jiao, J., Xu, M., & Wu, T. (2020). A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 203, 112243. https://doi.org/10.1016/j.enconman.2019.112243
  • [19] Liao, Z., Chen, Z., & Li, S. (2020). Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization. IEEE Access, 8, 69937-69952. https://doi.org/10.1016/10.1109/ACCESS.2020.2984728
  • [20] Ibrahim, I. A., Hossain, M. J., Duck, B. C., & Fell, C. J. (2020). An Adaptive Wind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model. IEEE Transactions on Sustainable Energy, 11(2), 1054-1066. https://doi.org/10.1109/TSTE.2019.2917513
  • [21] Gomes, R. C. M., Vitorino, M. A., Corrêa, M. B. de R., Fernandes, D. A., & Wang, R. (2017). Shuffled Complex Evolution on Photovoltaic Parameter Extraction: A Comparative Analysis. IEEE Transactions on Sustainable Energy, 8(2), 805-815. https://doi.org/10.1109/TSTE.2016.2620941
  • [22] Dkhichi, F., Oukarfi, B., Fakkar, A., & Belbounaguia, N. (2014). Parameter identification of solar cel model using Levenberg-Marquardt algorithm combined with simulated annealing. Solar Energy, 110, 781-788. https://doi.org/10.1016/j.solener.2014.09.033
  • [23] Alam, D. F., Yousri, D. A., & Eteiba, M. B. (2015). Flower Pollination Algorithm based solar PV parameter estimation. Energy Conversion and Management, 101, 410-422. https://doi.org/10.1016/j.enconman.2015.05.074
  • [24] Diab, A. A. Z., Sultan, H. M., Do, T. D., Kamel, O. M., & Mossa, M. A. (2020). Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules. IEEE Access, 8, 111102-111140. https://doi.org/10.1109/ACCESS.2020.3000770
  • [25] Mesbahi, O., Tlemçani, M., Janeiro, F. M., Hajjaji, A., & Kandoussi, K. (2020). A Modified Nelder-Mead Algorithm for Photovoltaic Parameters Identification. International Journal of Smart Grid - IJSmartGrid, 4(1), 28-37.
  • [26] Mesbahi, O., Tlemçani, M., Janeiro, F. M., Abdeloawahed, H., & Khalid, K. (2019). Estimation of Photovoltaic Panel Parameters by a Numerical Heuristic Searching Algorithm. In 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 401-406). IEEE. https://doi.org/10.1109/ICRERA47325.2019.8996779
  • [27] Hutcheson, G. D. (2011). Ordinary least-squares regression. L. Moutinho and G.D. Hutcheson, The SAGE Dictionary of Quantitative Management Research, 224-228.
  • [28] Hadjdida, A., Bourahla, M., Ertan, H. B., & Bekhti, M. (2018). Analytical Modelling, Simulation and Comparative Study of Multi-Junction Solar Cells Efficiency. International Journal of Renewable Energy Research, 8(4), 1824-1832.
  • [29] Salmi, T., Bouzguenda, M., Gastli, A., & Masmoudi, A. (2012). MATLAB / Simulink Based Modelling of Solar Photovoltaic Cell. International Journal of Renewable Energy Research (IJRER), 2(2), 213-218.
  • [30] Dimova-Malinovska, D. (2010). The state-of-the-art and future development of the photovoltaic technologies - The route from crystalline to nanostructured and new emerging materials. Journal of Physics: Conference Series, 253(1). https://doi.org/10.1088/1742-6596/253/1/012007
  • [31] Mahmoud, Y., Xiao, W., & Zeineldin, H. H. (2012). A Simple Approach to Modeling and Simulation of Photovoltaic Modules. IEEE Transactions on Sustainable Energy, 3(1), 185-186. https://doi.org/10.1109/TSTE.2011.2170776
  • [32] Ishaque, K., Salam, Z., & Taheri, H. (2011). Simple, fast and accurate two-diode model for photovoltaic modules. Solar Energy Materials and Solar Cells, 95(2), 586-594. https://doi.org/10.1016/j.solmat.2010.09.023
  • [33] Babu, B. C., & Gurjar, S. (2014). A Novel Simplified Two-Diode Model of Photovoltaic (PV) Module. IEEE Journal of Photovoltaics, 4(4), 1156-1161. https://doi.org/10.1109/JPHOTOV.2014.2316371
  • [34] Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308-313. https://doi.org/10.1093/comjnl/7.4.308
  • [35] Rashel, M. R., Rifat, J., Gonçalves, T., Tlemcani, M., & Melicio, R. (2017). Sensitivity Analysis Through Error Function of Crystalline-Si Photovoltaic Cell Model Integrated in a Smart Grid. International Journal of Renewable Energy Research, 7(4).
Uwagi
1. The authors would like to thank the BRO-CQ project for funding the work and the Instrumentation and Control Laboratory, Institute of Earth Sciences (ICT), University of Évora for enabling it. This work was also funded by the FCT/MCTES through national funds and, when applicable, co-funded EU funds under the project UIDB/50008/2020.
2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-047ff6f8-6f0e-46ad-a03d-b39a749dfab0
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