Improved Quality Parameter Estimation of Photovoltaic System Models based on SAO Algorithm
Received: 23 May 2024 | Revised: 8 June 2024 | Accepted: 16 June 2024 | Online: 2 August 2024
Corresponding author: Aymen Flah
Abstract
Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve.
Keywords:
photovoltaic systems, parameter identification, SAO algorithmDownloads
References
D. Yousri, D. Allam, M. B. Eteiba, and P. N. Suganthan, "Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants," Energy Conversion and Management, vol. 182, pp. 546–563, Feb. 2019.
M. Bechouat et al., "Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods," International Journal of Energy and Environmental Engineering, vol. 8, no. 4, pp. 331–341, Dec. 2017.
K. Ishaque, Z. Salam, H. Taheri, and A. Shamsudin, "A critical evaluation of EA computational methods for Photovoltaic cell parameter extraction based on two diode model," Solar Energy, vol. 85, no. 9, pp. 1768–1779, Sep. 2011.
R. Benkercha, S. Moulahoum, and B. Taghezouit, "Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm," Renewable Energy, vol. 143, pp. 1698–1709, Dec. 2019.
A. Al Tarabsheh, M. Akmal, and M. Ghazal, "Series Connected Photovoltaic Cells—Modelling and Analysis," Sustainability, vol. 9, no. 3, Mar. 2017, Art. no. 371.
V. P. Gantasala, "Solar augmentation of power plants in the UAE," Applied Solar Energy, vol. 52, no. 4, pp. 271–277, Oct. 2016.
W. Yang, R. R. Prabhakar, J. Tan, S. D. Tilley, and J. Moon, "Strategies for enhancing the photocurrent, photovoltage, and stability of photoelectrodes for photoelectrochemical water splitting," Chemical Society Reviews, vol. 48, no. 19, pp. 4979–5015, Sep. 2019.
A. Gholami, M. Ameri, M. Zandi, and R. Gavagsaz Ghoachani, "A single-diode model for photovoltaic panels in variable environmental conditions: Investigating dust impacts with experimental evaluation," Sustainable Energy Technologies and Assessments, vol. 47, Oct. 2021, Art. no. 101392.
M. Dashtdar and M. Dashtdar, "Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control," American Journal of Electrical and Computer Engineering, vol. 4, no. 2, Aug. 2020, Art. no. 35.
A. Gholami, M. Ameri, M. Zandi, R. Gavagsaz Ghoachani, and M. Gholami, "A fast and precise double-diode model for predicting photovoltaic panel electrical behavior in variable environmental conditions," International Journal of Ambient Energy, vol. 44, no. 1, pp. 1298–1315, Dec. 2023.
M.-N. Khursheed et al., "Review of Flower Pollination Algorithm: Applications and Variants," in International Conference on Engineering and Emerging Technologies, Lahore, Pakistan, Feb. 2020, pp. 1–6.
M. Dashtdar and M. Dashtdar, "Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control," American Journal of Electrical and Computer Engineering, vol. 4, no. 2, pp. 35–48, 2020.
J. P. Ram, T. S. Babu, T. Dragicevic, and N. Rajasekar, "A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation," Energy Conversion and Management, vol. 135, pp. 463–476, Mar. 2017.
L. Sandrolini, M. Artioli, and U. Reggiani, "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, vol. 87, no. 2, pp. 442–451, Feb. 2010.
C. Kumar and D. M. Mary, "Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method," Journal of Computational Electronics, vol. 20, no. 6, pp. 2563–2593, Dec. 2021.
M. T. Benmessaoud, P. Vasant, A. B. Stambouli, and M. Tioursi, "Modeling and parameters extraction of photovoltaic cell and modules using the genetic algorithms with lambert W-function as objective function," Intelligent Decision Technologies, vol. 14, no. 2, pp. 143–151, Jan. 2020.
H. Kraiem, W. Gadri, and A. Flah, "Efficient Energy Management with Emphasis on EV Charging/Discharging Strategy," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13143–13147, Apr. 2024.
A. S. E. Souissi, H. Kraiem, A. Flah, and A. E. Madani, "Improving Electric Vehicle Autonomy in the Smart City Concept," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13299–13304, Apr. 2024.
Y. I. Mesalam, S. Awdallh, H. Gaied, and A. Flah, "Interleaved Bidirectional DC-DC Converter for Renewable Energy Application based on a Multiple Storage System," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13329–13334, Apr. 2024.
M. Merchaoui, A. Sakly, and M. F. Mimouni, "Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction," Energy Conversion and Management, vol. 175, pp. 151–163, Nov. 2018.
Q. Niu, L. Zhang, and K. Li, "A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells," Energy Conversion and Management, vol. 86, pp. 1173–1185, Oct. 2014.
X. Chen, K. Yu, W. Du, W. Zhao, and G. Liu, "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, vol. 99, pp. 170–180, Mar. 2016.
R. A. Eltuhamy, M. Rady, E. Almatrafi, H. A. Mahmoud, and K. H. Ibrahim, "Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme," Sensors, vol. 23, no. 3, Jan. 2023, Art. no. 1280.
S. Z. Mirbagheri, M. Aldeen, and S. Saha, "A PSO-based MPPT re-initialised by incremental conductance method for a standalone PV system," in 23rd Mediterranean Conference on Control and Automation, Torremolinos, Spain, Jun. 2015, pp. 298–303.
Downloads
How to Cite
License
Copyright (c) 2024 Rim Attafi, Naoufal Zitouni, Masoud Dashtdar, Aymen Flah, Mohamed F. Elnaggar, Mohammad Kanan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
