Abstract
This paper proposes the Marine Predators Algorithm (MPA) as a new bio-inspired optimization algorithm to extract the parameters of three-photo voltaic models of solar cells. These models are three diode model (TDM), double diode model (DDM) and one-diode model (SDM). The MPA is dependent on the manner of a population of Marine Predators. This optimal strategy allows prey to use an optimal foraging strategy and allows predators to use an intelligent rate policy for encounters. The proposed MPA-based parameter estimation algorithm is tested at normal and low radiation operating conditions. The normal operating condition is employed with the 57 mm diameter commercial silicon solar cell (Case 1), while the Case 2 is based on a multi-crystalline silicon solar cell of area 7.7 cm2 from Q6-1380 under low irradiance levels. The capability of MPA is validated for the three models compared with other competitive algorithms. Simulation results show that high closeness between the estimated and experimental records reflects the high capability of the MPA with more accurate parameters. The RMSE of 8.43854E−4, 7.59E−4 and 7.561E−4 are achieved for Case 1 by using SDM, DDM and TDM, respectively. While, the RMSE has the best levels of 1.61E−5, 1.46E−5, and 1.42E−5 in Case 2, respectively. Also, the MPA has competitive results compared with several optimization algorithms in the literature as sine cosine, particle swarm, salp swarm, grey wolf optimization algorithms. The proposed MPA has good convergence and robust statistical analysis for different operating conditions of low and high irradiance.
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Ishaque, K.; Salam, Z.; Mekhilef, S.; Shamsudin, A.: Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl. Energy 99, 297–308 (2012)
Bonanno, F.; Capizzi, G.; Graditi, G.; Napoli, C.; Tina, G.M.: A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl. Energy. 97, 956–961 (2012)
Sandrolini, L.; Artioli, M.; Reggiani, U.: Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl. Energy. 87, 442–451 (2010)
Amrouche, B.; Guessoum, A.; Belhamel, M.: A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison. Appl. Energy. 91, 395–404 (2012)
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 102, 1160–1177 (2013)
Chenouard, R.; El-Sehiemy, R.A.: An interval branch and bound global optimization algorithm for parameter estimation of three photovoltaic models. Energy Convers. Manag. (2020). https://doi.org/10.1016/j.enconman.2019.112400
Abido, M.A.; Khalid, M.S.; Worku, M.Y.: An efficient ANFIS-based PI controller for maximum power point tracking of PV systems. Arab. J. Sci. Eng. 40, 2641–2651 (2015). https://doi.org/10.1007/s13369-015-1749-z
Dash, S.K.; Ray, P.K.: Design and modeling of single-phase PV-UPQC scheme for power quality improvement utilizing a novel notch filter-based control algorithm: an experimental approach. Arab. J. Sci. Eng. 43, 3083–3102 (2018). https://doi.org/10.1007/s13369-018-3116-3
Zaky, A.A.; Ibrahim, M.N.; Rezk, H.; Christopoulos, E.; El Sehiemy, R.A.; Hristoforou, E.; Kladas, A.; Sergeant, P.; Falaras, P.: Energy efficiency improvement of water pumping system using synchronous reluctance motor fed by perovskite solar cells. Int. J. Energy Res. 44, 11629–11642 (2020). https://doi.org/10.1002/er.5788
El-Ela, A.A.A.; El-Sehiemy, R.A.; Kinawy, A.M.; Ali, E.S.: Optimal placement and sizing of distributed generation units using different cat swarm optimization algorithms. In: 2016 18th International Middle-East Power Systems Conference, MEPCON 2016—Proceedings (2017)
Abou El-Ela, A.A.; El-Sehiemy, R.A.; Ali, E.S.; Kinawy, A.M.: Minimisation of voltage fluctuation resulted from renewable energy sources uncertainty in distribution systems. IET Gener. Transm. Distrib. (2019). https://doi.org/10.1049/iet-gtd.2018.5136
Nishioka, K.; Sakitani, N.; Uraoka, Y.; Fuyuki, T.: Analysis of multicrystalline silicon solar cells by modified 3-diode equivalent circuit model taking leakage current through periphery into consideration. Sol. energy Mater. Sol. cells. 91, 1222–1227 (2007)
Kassis, A.; Saad, M.: Analysis of multi-crystalline silicon solar cells at low illumination levels using a modified two-diode model. Sol. Energy Mater. Sol. Cells. (2010). https://doi.org/10.1016/j.solmat.2010.06.036
Chan, D.S.H.; Phang, J.C.H.: Analytical methods for the extraction of solar-cell single-and double-diode model parameters from IV characteristics. IEEE Trans. Electron Devices. 34, 286–293 (1987)
Ishaque, K.; Salam, Z.; Taheri, H.: others: Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model. Simul. Model. Pract. Theory. 19, 1613–1626 (2011)
Elbaset, A.A.; Ali, H.; Abd-El Sattar, M.: Novel seven-parameter model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 130, 442–455 (2014)
Bayoumi, A.S.; El-Sehiemy, R.A.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F.: Assessment of an improved three-diode against modified two-diode patterns of MCS solar cells associated with soft parameter estimation paradigms. Appl. Sci. (2021). https://doi.org/10.3390/app11031055
Jadidbonab, M.; Mohammadi-Ivatloo, B.; Marzband, M.; Siano, P.: Short-term self-scheduling of virtual energy hub plant within thermal energy market. IEEE Trans. Ind. Electron. (2021). https://doi.org/10.1109/TIE.2020.2978707
Gholinejad, H.R.; Loni, A.; Adabi, J.; Marzband, M.: A hierarchical energy management system for multiple home energy hubs in neighborhood grids. J. Build. Eng. (2020). https://doi.org/10.1016/j.jobe.2019.101028
Nazari-Heris, M.; Mirzaei, M.A.; Mohammadi-Ivatloo, B.; Marzband, M.; Asadi, S.: Economic-environmental effect of power to gas technology in coupled electricity and gas systems with price-responsive shiftable loads. J. Clean. Prod. (2020). https://doi.org/10.1016/j.jclepro.2019.118769
Pazouki, S.; Haghifam, M.R.; Moser, A.: Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. Int. J. Electr. Power Energy Syst. (2014). https://doi.org/10.1016/j.ijepes.2014.03.038
Vahid-Pakdel, M.J.; Nojavan, S.; Mohammadi-ivatloo, B.; Zare, K.: Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response. Energy Convers. Manag. (2017). https://doi.org/10.1016/j.enconman.2017.04.074
Abou El-Ela, A.A.; El-Sehiemy, R.A.; Shaheen, A.M. et al: Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable. Neural Comput Applic. 33, 8459–8477 (2021). https://doi.org/10.1007/s00521-020-05599-8
Zaky, A.A.; Ibrahim, M.N.; Rezk, H.; Christopoulos, E.; El Sehiemy, R.A.; Hristoforou, E.; Kladas, A.; Sergeant, P.; Falaras, P.: Energy efficiency improvement of water pumping system using synchronous reluctance motor fed by perovskite solar cells. Int. J. Energy Res. (2020). https://doi.org/10.1002/er.5788
Chan, D.S.H.; Phillips, J.R.; Phang, J.C.H.: A comparative study of extraction methods for solar cell model parameters. Solid. State. Electron. 29, 329–337 (1986)
Jain, A.; Kapoor, A.: Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Sol. Energy Mater. Sol. Cells. 81, 269–277 (2004)
Saleem, H.; Karmalkar, S.: An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J–V curve. IEEE Electron Device Lett. 30, 349–352 (2009)
Agroui, K.; Pellegrino, M.; Giovanni, F.: Analysis techniques for photovoltaic modules based on amorphous solar cells. Arab. J. Sci. Eng. 42, 375–381 (2017). https://doi.org/10.1007/s13369-016-2050-5
AlRashidi, M.R.; AlHajri, M.F.; El-Naggar, K.M.; Al-Othman, A.K.: A new estimation approach for determining the I–V characteristics of solar cells. Sol. Energy. 85, 1543–1550 (2011)
El-Naggar, K.M.; AlRashidi, M.R.; AlHajri, M.F.; Al-Othman, A.K.: Simulated annealing algorithm for photovoltaic parameters identification. Sol. Energy. 86, 266–274 (2012)
Jordehi, A.R.: Parameter estimation of solar photovoltaic (PV) cells: a review. Renew. Sustain. Energy Rev. 61, 354–371 (2016)
Easwarakhanthan, T.; Bottin, J.; Bouhouch, I.; Boutrit, C.: Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Sol. Energy. 4, 1–12 (1986). https://doi.org/10.1080/01425918608909835
Allam, D.; Yousri, D.A.; Eteiba, M.B.: Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm. Energy Convers. Manag. 123, 535–548 (2016)
Awadallah, M.A.: Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data. Energy Convers. Manag. 113, 312–320 (2016)
Chen, X.; Yu, K.; Du, W.; Zhao, W.; Liu, G.: Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy (2016). https://doi.org/10.1016/j.energy.2016.01.052
Muhsen, D.H.; Ghazali, A.B.; Khatib, T.; Abed, I.A.: Extraction of photovoltaic module model’s parameters using an improved hybrid differential evolution/electromagnetism-like algorithm. Sol. Energy. 119, 286–297 (2015)
Muhsen, D.H.; Ghazali, A.B.; Khatib, T.; Abed, I.A.: Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm. Energy Convers. Manag. 105, 552–561 (2015)
Alam, D.F.; Yousri, D.A.; Eteiba, M.B.: Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manag. 101, 410–422 (2015)
Yuan, X.; Xiang, Y.; He, Y.: Parameter extraction of solar cell models using mutative-scale parallel chaos optimization algorithm. Sol. Energy. 108, 238–251 (2014)
Oliva, D.; Cuevas, E.; Pajares, G.: Parameter identification of solar cells using artificial bee colony optimization. Energy (2014). https://doi.org/10.1016/j.energy.2014.05.011
Askarzadeh, A.; Rezazadeh, A.: Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl. Energy. 102, 943–949 (2013)
Askarzadeh, A.; Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy. (2012). https://doi.org/10.1016/j.solener.2012.08.018
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. Manag. 73, 10–25 (2013)
Askarzadeh, A.; dos Santos Coelho, L.: Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach. Energy Convers. Manag. 89, 608–614 (2015)
Qais, M.H.; Hasanien, H.M.; Alghuwainem, S.; Nouh, A.S.: Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy (2019). https://doi.org/10.1016/j.energy.2019.116001
Zaky, A.A.; Sehiemy, R.A.E.; Rashwan, Y.I.; Elhossieni, M.A.; Gkini, K.; Kladas, A.; Falaras, P.: Optimal performance emulation of PSCs using the elephant herd algorithm associated with experimental validation. ECS J. Solid State Sci. Technol. (2019). https://doi.org/10.1149/2.0271912jss
Bayoumi, A.S.; El-sehiemy, R.A.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F.: Assessment of an Improved Three-Diode against Modified Two-Diode Patterns of MCS Solar Cells Associated with Soft Parameter Estimation Paradigms (2021)
Chen, H.; Jiao, S.; Heidari, A.A.; Wang, M.; Chen, X.; Zhao, X.: An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers. Manag. 195, 927–942 (2019)
Abbassi, R.; Abbassi, A.; Asghar, A.; Mirjalili, S.: An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers. Manag. 179, 362–372 (2019). https://doi.org/10.1016/j.enconman.2018.10.069
Khanna, V.; Das, B.K.; Bisht, D.; Singh, P.K.: others: A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renew. Energy. 78, 105–113 (2015)
Robandi, I., et al.: Photovoltaic parameter estimation using grey wolf optimization. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 593–597 (2017)
El-Sehiemy, R.A.; Shaheen, A.M.; Ginidi, A.; Ghoneim, S.S.M.: A forensic-based investigation algorithm for parameter extraction of solar cell models. IEEE Access. (2020). https://doi.org/10.1109/ACCESS.2020.3046536
Faramarzi, A.; Heidarinejad, M.; Mirjalili, S.; Gandomi, A.H.: Marine predators algorithm: A nature-inspired metaheuristic. Expert Syst. Appl. 152, 113377 (2020)
Wolf, M.; Noel, G.T.; Stirn, R.J.: Investigation of the double exponential in the current–voltage characteristics of silicon solar cells. IEEE Trans. Electron Devices. 24, 419–428 (1977)
Joshi, D.P.; Sharma, K.: Effects of grain boundaries on the performance of polycrystalline silicon solar cells. Ind. J Pure Appl. Phys. 50(9), 661–669 (2012)
Fossum, J.G.; Lindholm, F.A.: Theory of grain-boundary and intragrain recombination currents in polysilicon pn-junction solar cells. IEEE Trans. Electron Devices. 27, 692–700 (1980)
Koohi-Kamali, S.; Rahim, N.A.; Mokhlis, H.; Tyagi, V.V.: Photovoltaic electricity generator dynamic modeling methods for smart grid applications: a review. Renew. Sustain. Energy Rev. 57, 131–172 (2016)
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Bayoumi, A.S.A., El-Sehiemy, R.A. & Abaza, A. Effective PV Parameter Estimation Algorithm Based on Marine Predators Optimizer Considering Normal and Low Radiation Operating Conditions. Arab J Sci Eng 47, 3089–3104 (2022). https://doi.org/10.1007/s13369-021-06045-0
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DOI: https://doi.org/10.1007/s13369-021-06045-0