Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer; Sustainable Energy Technologies and Assessments; Vol. 52, pt. B

Dettagli Bibliografici
Parent link:Sustainable Energy Technologies and Assessments
Vol. 52, pt. B.— 2022.— [102150, 18 p.]
Ente Autore: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Altri autori: Mokhamed Elsaed (Mohamed Abd Elaziz) A. M. Akhmed Mokhamed, Rolla A. Almodfer, Iman A. Ahmadianfar, Ibrahim A. I. Anwar Ibrahim, Mohammed M. Mudhsh, Laith A. Abualigah, Songfeng Lu, Ahmed A. Abd El-Latif, Yousri D. Dalia
Riassunto:Title screen
An accurate definition of the photovoltaic (PV) models is an essential task to emulate and understand the physical behavior of the PV cell/panels. The highly used PV models are the static equivalent circuits, including single and double diode models. However, the accurate definition of the static models is mainly based on their estimated parameters. Proposing a reliable Optimization-based approached is a challenging aim. So, this paper proposes a novel and efficient optimizer to identify PV single and double diode models' parameters for several PV modules using different sets of experimentally measured data. The developed method depends on improving the gradient-based optimization algorithm (GBO) using a new crossover operator to enhances agents' diversity. Furthermore, a modified local escaping operator is applied to improve exploitation of GBO. The performance of the improvement GBO (IGBO) is evaluated using different experimental datasets for numerous PV modules under several operating conditions of temperature and radiation. The efficiency of IGBO is validated through a massive comparison with a set of recent state-of-the-art techniques. Reported results, fitting curves, and convergence curves provide proof for the efficiency of IGBO in providing high qualifies results with remarkable convergence speed.
Режим доступа: по договору с организацией-держателем ресурса
Lingua:inglese
Pubblicazione: 2022
Soggetti:
Accesso online:https://doi.org/10.1016/j.seta.2022.102150
Natura: Elettronico Capitolo di libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668723

MARC

LEADER 00000naa0a2200000 4500
001 668723
005 20250221104406.0
035 |a (RuTPU)RU\TPU\network\39960 
035 |a RU\TPU\network\39932 
090 |a 668723 
100 |a 20230119d2022 k||y0rusy50 ba 
101 0 |a eng 
135 |a drcn ---uucaa 
181 0 |a i  
182 0 |a b 
200 1 |a Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer  |f A. M. Mokhamed Elsaed (Mohamed Abd Elaziz), A. Rolla, A. Iman [et al.] 
203 |a Text  |c electronic 
300 |a Title screen 
320 |a [References: 72 tit.] 
330 |a An accurate definition of the photovoltaic (PV) models is an essential task to emulate and understand the physical behavior of the PV cell/panels. The highly used PV models are the static equivalent circuits, including single and double diode models. However, the accurate definition of the static models is mainly based on their estimated parameters. Proposing a reliable Optimization-based approached is a challenging aim. So, this paper proposes a novel and efficient optimizer to identify PV single and double diode models' parameters for several PV modules using different sets of experimentally measured data. The developed method depends on improving the gradient-based optimization algorithm (GBO) using a new crossover operator to enhances agents' diversity. Furthermore, a modified local escaping operator is applied to improve exploitation of GBO. The performance of the improvement GBO (IGBO) is evaluated using different experimental datasets for numerous PV modules under several operating conditions of temperature and radiation. The efficiency of IGBO is validated through a massive comparison with a set of recent state-of-the-art techniques. Reported results, fitting curves, and convergence curves provide proof for the efficiency of IGBO in providing high qualifies results with remarkable convergence speed. 
333 |a Режим доступа: по договору с организацией-держателем ресурса 
461 |t Sustainable Energy Technologies and Assessments 
463 |t Vol. 52, pt. B  |v [102150, 18 p.]  |d 2022 
610 1 |a электронный ресурс 
610 1 |a труды учёных ТПУ 
610 1 |a solar energy technology 
610 1 |a gradient-based optimizer 
610 1 |a parameters estimation 
610 1 |a single diode model 
610 1 |a two diode model 
610 1 |a солнечная энергия 
610 1 |a оптимизаторы 
610 1 |a градиент 
701 1 |a Mokhamed Elsaed (Mohamed Abd Elaziz)  |b A. M.  |c Specialist in the field of informatics and computer technology  |c Professor of Tomsk Polytechnic University  |f 1987-  |g Akhmed Mokhamed  |3 (RuTPU)RU\TPU\pers\46943 
701 1 |a Rolla  |b A.  |g Almodfer 
701 1 |a Iman  |b A.  |g Ahmadianfar 
701 1 |a Ibrahim  |b A. I.  |g Anwar Ibrahim 
701 1 |a Mohammed  |b M.  |g Mudhsh 
701 1 |a Laith  |b A.  |g Abualigah 
701 0 |a Songfeng Lu 
701 1 |a Ahmed  |b A.  |g Abd El-Latif 
701 1 |a Yousri  |b D.  |g Dalia 
712 0 2 |a Национальный исследовательский Томский политехнический университет  |b Инженерная школа информационных технологий и робототехники  |b Отделение информационных технологий  |3 (RuTPU)RU\TPU\col\23515 
801 0 |a RU  |b 63413507  |c 20230119  |g RCR 
856 4 |u https://doi.org/10.1016/j.seta.2022.102150 
942 |c CF