TY - JOUR
T1 - An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models
T2 - Analysis and validations
AU - Abdel-Basset, Mohamed
AU - Mohamed, Reda
AU - Chakrabortty, Ripon K.
AU - Sallam, Karam
AU - Ryan, Michael J.
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many meta-heuristic algorithms have been recently proposed, but the parameters obtained are not as accurate and reliable as is desired, particularly when the PV models have a significant number of unknown parameters. Therefore, in this paper, a modified teaching–learning based optimization (MTLBO) approach is suggested to accurately and reliably extract the unknown parameters of PV models. Our modification to TLBO divides each of the teaching and learning phases into three levels: low, medium, and high according to the scoring level of each learner. The scoring level of each one is measured based on comparison between the fitness of the updated learner and the current leaner; if the fitness of the updated is better, the scoring level is reset to 0, and otherwise, it is increased by 1. Finally, to observe the efficacy of MTLBO, it is investigated on five PV cells and modules: single diode model and double diode model in case of RTC France, Photowatt-PWP201 module, STM6-40/36 module, and STP6-120/36 module. For those PV cells and modules, our proposed could respectively come true the following average outcomes: 0.0009860219, 0.0009825026, 0.0024250749, 0.0017298137, and 0.0166006031. To check the efficacy of MTLBO, it is compared with a number of recent and well-known algorithms. The experimental results show the superiority of the proposed algorithm, especially on double diode model.
AB - Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many meta-heuristic algorithms have been recently proposed, but the parameters obtained are not as accurate and reliable as is desired, particularly when the PV models have a significant number of unknown parameters. Therefore, in this paper, a modified teaching–learning based optimization (MTLBO) approach is suggested to accurately and reliably extract the unknown parameters of PV models. Our modification to TLBO divides each of the teaching and learning phases into three levels: low, medium, and high according to the scoring level of each learner. The scoring level of each one is measured based on comparison between the fitness of the updated learner and the current leaner; if the fitness of the updated is better, the scoring level is reset to 0, and otherwise, it is increased by 1. Finally, to observe the efficacy of MTLBO, it is investigated on five PV cells and modules: single diode model and double diode model in case of RTC France, Photowatt-PWP201 module, STM6-40/36 module, and STP6-120/36 module. For those PV cells and modules, our proposed could respectively come true the following average outcomes: 0.0009860219, 0.0009825026, 0.0024250749, 0.0017298137, and 0.0166006031. To check the efficacy of MTLBO, it is compared with a number of recent and well-known algorithms. The experimental results show the superiority of the proposed algorithm, especially on double diode model.
KW - Environmental factors
KW - Optimization
KW - PV models
KW - Solar energy
KW - Teaching-learning
UR - http://www.scopus.com/inward/record.url?scp=85096181373&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2020.113614
DO - 10.1016/j.enconman.2020.113614
M3 - Article
AN - SCOPUS:85096181373
SN - 0196-8904
VL - 227
SP - 1
EP - 21
JO - Energy Conversion and Management
JF - Energy Conversion and Management
M1 - 113614
ER -