Modified Social Network Search Algorithm Combined With Halley's Method for Parameter Estimation in a Photovoltaic Cell, Module and Array

Document Type : Original Article

Authors

1 School of Geology and Mining Engineering, University of Ngaoundéré, Ngaoundéré. Cameroon.

2 Department of Renewable Energy, National Advanced School of Engineering of Maroua, University of Maroua, Cameroon

3 Department of Electrical and Computer Engineering, University of Quebec, Trois Rivières, Quebec, Canada

4 Laboratory of electrical Engineering, mechatronic and signal treatment, National Advanced School of Engineering, University of Yaoundé 1, Yaoundé, Cameroon.

5 Signal, Image and systems Laboratory, Higher Technical Teacher Training College of Ebolowa, University of Ebolowa, Cameroon.

10.22059/jser.2023.357161.1287

Abstract

A significant research focus is how to make photovoltaic (PV) systems operate as efficiently as possible. A sufficient, accurate, and detailed model of the actual PV system is needed in order to get the best performance out of solar panels. More specifically, the parameters of these models are fitted to actual data to determine the correctness of the models. In order to determine the most accurate parameters of a photovoltaic cell, module, and array using actual data, this research suggests a novel method called MSNS-HAL, which combines Halley's method with a modified social network search algorithm. A control parameter with a Gaussian and Cauchy distribution is randomly added to the search space to improve parameter estimation performance and speed up the agents' convergence to the best solution. The best estimate of currents is then determined using Halley's root-finding technique. The proposed model, which has a best root mean square error of 7.1719 x 10-4 for the RTC cell, 2.0388 x 10-3 for the Photowatt PWP module, and 0.0069 for the experimental field of 18 PV panels, has the highest accuracy when compared to 12 other current optimization approaches.

Keywords

[1]   P. J. Gnetchejo, S. N. Essiane, P. Ele, A. Dadjé, et Z. Chen, « Faults diagnosis in a photovoltaic system based on multivariate statistical analysis », Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, p. 1‑22, mai 2021, doi: 10.1080/15567036.2021.1919792.
[2]  P. J. Gnetchejo, S. Ndjakomo Essiane, A.Dadjé et al. Improved social network search algorithm coupled with Lagrange method for extracting the best parameter of photovoltaic modules and array. Int J Energy Environ Eng (2022). https://doi.org/10.1007/s40095-022-00532-2
[3] D. M. Djanssou, A.Dadjé, A.Tom, N.Djongyang, "Improvement of the Dynamic Response of Robust Sliding Mode MPPT Controller-Based PSO Algorithm for PV Systems under Fast-Changing Atmospheric Conditions", International Journal of Photo energy, vol. 2021, Article ID 6671133, 13 pages, 2021. https://doi.org/10.1155/2021/6671133
[4]  P. J. N. E. P. E. Gnetchejo, « Diagnostic des défauts d’un champ photovoltaïque par analyse statistique multivariée Fault detection in PV array based on statistical analysis », The 1st International Conference on Local Resource Exploitation, Ngaoundéré, 2021.
[5]   S. Ndjakomo Essiane, P. J. Gnetchejo, P. Ele, et Z. Chen, « Faults detection and identification in PV array using kernel principal components analysis », Int J Energy Environ Eng, août 2021, doi: 10.1007/s40095-021-00416-x.
[6] P. J. Gnetchejo, S. N. Essiane, P. Ele, R. Wamkeue, D. M. Wapet, et S. P. Ngoffe, « Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System », JPEE, vol. 07, no 08, p. 1‑26, 2019, doi: 10.4236/jpee.2019.78001.
[7]   P. J. Gnetchejo, S. Ndjakomo Essiane, A.Dadjé et al. Optimal design of the modelling parameters of photovoltaic modules and array through metaheuristic with Secant method. Energy Conversion and Management (2022). https://doi.org/10.1016/j.ecmx.2022.100273
[8]  A. Dadjé, F. K. Mbakop, D. M. Djanssou, R. Z. Falama. Modeling the Behavior of a Photovoltaic Generator Using a Four-Parameter Electrical Model. Engineering Physics. Vol. 6, No. 1, 2022, pp. 5-12. doi: 10.11648/j.ep.20220601.12
[9] D. E. M. Wapet, S. N. Essiane, R. Wamkeue, et P. J. Gnetchejo, « Hydropower Production Optimization from Inflow: Case Study of Songloulou Hydroplant », JPEE, vol. 08, no 08, p. 37‑52, 2020, doi: 10.4236/jpee.2020.88003.
[10] D. E. Mbadjoun Wapet, S. Ndjakomo Essiane, R. Wamkeue, D. Bisso, et P. J. Gnetchejo, « Optimal management of hydropower production: Case of Memve'ele hydropower reservoir policy », Energy Reports, vol. 8, p. 1425‑1456, nov. 2022, doi: 10.1016/j.egyr.2021.12.047.
[11] A. Dehghanzadeh, G. Farahani, et M. Maboodi, « A novel approximate explicit double-diode model of solar cells for use in simulation studies », Renewable Energy, vol. 103, p. 468‑477, avr. 2017, doi: 10.1016/j.renene.2016.11.051.
[12] A. Senturk et R. Eke, « A new method to simulate photovoltaic performance of crystalline silicon photovoltaic modules based on datasheet values », Renewable Energy, vol. 103, p. 58‑69, avr. 2017, doi: 10.1016/j.renene.2016.11.025.
[13]  A. K. Tossa, Y. M. Soro, Y. Azoumah, et D. Yamegueu, « A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions », Solar Energy, vol. 110, p. 543‑560, déc. 2014, doi: 10.1016/j.solener.2014.09.043.
[14]   F. Ghani, E. F. Fernandez, F. Almonacid, et T. S. O'Donovan, « The numerical computation of lumped parameter values using the multi-dimensional Newton-Raphson method for the characterisation of a multi-junction CPV module using the five-parameter approach », Solar Energy, vol. 149, p. 302‑313, juin 2017, doi: 10.1016/j.solener.2017.04.024.
[15]   A. Chauhan et S. Prakash, « A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation », Int Trans Electr Energ Syst, vol. 31, no 7, juill. 2021, doi: 10.1002/2050-7038.12917.
[16]   F. D. Mengue, A. S. T. Kammogne, M. S. Siewe, R. Yamapi, et H. B. Fotsin, « A new hybrid method based on differential evolution to determine the temperature-dependent parameters of single-diode photovoltaic cells », J Comput Electron, vol. 20, no 6, p. 2511‑2521, déc. 2021, doi: 10.1007/s10825-021-01785-6.
[17]   S. Song, P. Wang, A. A. Heidari, X. Zhao, et H. Chen, « Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction », Engineering Applications of Artificial Intelligence, vol. 109, p. 104608, mars 2022, doi: 10.1016/j.engappai.2021.104608.
[18]   D. S. AbdElminaam, E. H. Houssein, M. Said, D. Oliva, et A. Nabil, « An Efficient Heap-Based Optimizer for Parameters Identification of Modified Photovoltaic Models », Ain Shams Engineering Journal, vol. 13, no 5, p. 101728, sept. 2022, doi: 10.1016/j.asej.2022.101728.
[19] M. F. Tefek, « Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems », J Comput Electron, vol. 20, no 6, p. 2530‑2562, déc. 2021, doi: 10.1007/s10825-021-01796-3.
[20]  I. A. Ibrahim, M. J. Hossain, et B. C. Duck, « A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects », Sustainable Energy Technologies and Assessments, vol. 50, p. 101685, mars 2022, doi: 10.1016/j.seta.2021.101685.
[21] S. M. Parida et P. K. Rout, « Differential evolution with dynamic control factors for parameter estimation of photovoltaic models », J Comput Electron, vol. 20, no 1, p. 330‑343, févr. 2021, doi: 10.1007/s10825-020-01617-z.
[22]  N. F. Nicaire, P. N. Steve, N. E. Salome, et A. O. Grégroire, « Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm », International Journal of Photoenergy, vol. 2021, p. 1‑20, oct. 2021, doi: 10.1155/2021/4343203.
[23] D. M. Djanssou, A. Dadjé, N. Djongyang, Estimation of Photovoltaic Cell Parameters Using the Honey Badger Algorithm; International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958 (Online), Volume-11 Issue-5, June 2022. PP: 105-108 DOI: 10.35940/ijeat.E3552.0611522
[24]  P. J. Gnetchejo, S. Ndjakomo Essiane, P. Ele, R. Wamkeue, D. Mbadjoun Wapet, et S. Perabi Ngoffe, « Reply to comment on "Important notes on parameter estimation of solar photovoltaic cell", by Gnetchejo et al. [Energy Conversion and Management, https://doi.org/10.1016/j.enconman.2019.111870.] », Energy Conversion and Management, vol. 201, p. 112132, déc. 2019, doi: 10.1016/j.enconman.2019.112132.
[25]  P. J. Gnetchejo, S. Ndjakomo Essiane, A. Dadjé, et P. Ele, « A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules », Heliyon, vol. 7, no 4, p. e06673, avr. 2021, doi: 10.1016/j.heliyon.2021.e06673.
[26]  S. Talatahari, H. Bayzidi, et M. Saraee, « Social Network Search for Global Optimization », IEEE Access, vol. 9, p. 92815‑92863, 2021, doi: 10.1109/ACCESS.2021.3091495.
[27]  P. J. Gnetchejo et al., « A Self-adaptive Algorithm with Newton Raphson Method for Parameters Identification of Photovoltaic Modules and Array », Trans. Electr. Electron. Mater., vol. 22, no 6, p. 869‑888, déc. 2021, doi: 10.1007/s42341-021-00312-5.
[28]  S. M. Parida et P. K. Rout, « Differential evolution with dynamic control factors for parameter estimation of photovoltaic models », J Comput Electron, vol. 20, no 1, p. 330‑343, févr. 2021, doi: 10.1007/s10825-020-01617-z.
[29]  M. A. E. Sattar, A. Al Sumaiti, H. Ali, et A. A. Z. Diab, « Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions », Neural Comput & Applic, vol. 33, no 18, p. 11799‑11819, sept. 2021, doi: 10.1007/s00521-021-05822-0.
[30]  P. J. Gnetchejo, S. Ndjakomo Essiane, P. Ele, R. Wamkeue, D. Mbadjoun Wapet, et S. Perabi Ngoffe, « Important notes on parameter estimation of solar photovoltaic cell », Energy Conversion and Management, vol. 197, p. 111870, oct. 2019, doi: 10.1016/j.enconman.2019.111870.
[31]  S. Yu, A. A. Heidari, G. Liang, C. Chen, H. Chen, et Q. Shao, « Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization », Optik, vol. 252, p. 168513, févr. 2022, doi: 10.1016/j.ijleo.2021.168513.
[32]  M. Premkumar, P. Jangir, R. M. Elavarasan, et R. Sowmya, « Opposition decided gradient-based optimizer with balance analysis and diversity maintenance for parameter identification of solar photovoltaic models », J Ambient Intell Human Comput, nov. 2021, doi: 10.1007/s12652-021-03564-4.
[33]  Y. Kharchouf, R. Herbazi, et A. Chahboun, « Parameter's extraction of solar photovoltaic models using an improved differential evolution algorithm », Energy Conversion and Management, vol. 251, p. 114972, janv. 2022, doi: 10.1016/j.enconman.2021.114972.
[34]  S. Sreekantha Reddy et C. Yammani, « Parameter extraction of single‐diode photovoltaic module using experimental current–voltage data », Circuit Theory & Apps, vol. 50, no 2, p. 753‑771, févr. 2022, doi: 10.1002/cta.3133.
[35]  L. Wu et al., « Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm », Applied Energy, vol. 232, p. 36‑53, déc. 2018, doi: 10.1016/j.apenergy.2018.09.161.