A Hunter-Prey Algorithm Coordinating Mutual Benefit and Sharing and Interactive Learning for High-Efficiency Design of Photovoltaic Models

被引:3
作者
Qu, Chiwen [1 ]
Lu, Zenghui [1 ]
Peng, Xiaoning [2 ]
Lin, Guangkang [3 ]
机构
[1] Youjiang Med Univ Nationalities, Publ Hlth & Management Inst, Baise 533000, Peoples R China
[2] Hunan Normal Univ, Sch Med, Dept Pathol & Pathophysiol, Changsha 410081, Peoples R China
[3] Guangxi Youjiang Natl Business Sch, Baise 533000, Guangxi, Peoples R China
关键词
PARTICLE SWARM OPTIMIZATION; PARAMETER-ESTIMATION; SOLAR-CELLS; SEARCH ALGORITHM; EXTRACTION; IDENTIFICATION; EVOLUTION;
D O I
10.1155/2023/4831209
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is crucial for the photovoltaic system to have an accurate model and well-estimated parameters to further increase conversion efficiency. Most existing methods for identifying photovoltaic model parameters have problems such as high computational cost, local optimum trouble, or difficulty in providing the best performance due to complex adjustments of algorithm parameters. To improve these defects, a hunter-prey optimization algorithm coordinating mutual benefit and sharing and interactive learning activities (EHPO) is proposed. First, based on hunter-prey optimization, timely information sharing with the mutual benefit and sharing mechanism was achieved when the algorithm was applied to search for prey, thus improving the searching precision and convergence rate of the algorithm. Second, hunters used the history optimization information and useful information from peers to guide the search direction, thus balancing the global and local development abilities of the algorithm. Finally, a lens imaging backward learning strategy was adopted to prevent the algorithm from falling into the local optimum, thus increasing the diversity of varieties and the probability of finding the global optimal solution. The simulation results of the single-diode model (SDM), double-diode model (DDM), triple-diode model (TDM), and other PV models in different environmental conditions show that the improved EHPO algorithm is more advantageous for parameter extraction than other advanced metaheuristic algorithms. This study demonstrates that EHPO is an accurate and reliable tool for predicting the parameters of PV models.
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页数:43
相关论文
共 105 条
[1]   Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach [J].
Abbassi, Abdelkader ;
Abbassi, Rabeh ;
Heidari, Ali Asghar ;
Oliva, Diego ;
Chen, Huiling ;
Habib, Arslan ;
Jemli, Mohamed ;
Wang, Mingjing .
ENERGY, 2020, 198
[2]   An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models [J].
Abbassi, Rabeh ;
Abbassi, Abdelkader ;
Heidari, Ali Asghar ;
Mirjalili, Seyedali .
ENERGY CONVERSION AND MANAGEMENT, 2019, 179 :362-372
[3]   Parameter estimation of photovoltaic models using an improved marine predators algorithm [J].
Abdel-Basset, Mohamed ;
El-Shahat, Doaa ;
Chakrabortty, Ripon K. ;
Ryan, Michael .
ENERGY CONVERSION AND MANAGEMENT, 2021, 227
[4]   Solar photovoltaic parameter estimation using an improved equilibrium optimizer [J].
Abdel-Basset, Mohamed ;
Mohamed, Reda ;
Mirjalili, Seyedali ;
Chakrabortty, Ripon K. ;
Ryan, Michael J. .
SOLAR ENERGY, 2020, 209 :694-708
[5]   Salp swarm algorithm: a comprehensive survey [J].
Abualigah, Laith ;
Shehab, Mohammad ;
Alshinwan, Mohammad ;
Alabool, Hamzeh .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (15) :11195-11215
[6]   A greedy non-hierarchical grey wolf optimizer for real-world optimization [J].
Akbari, Ebrahim ;
Rahimnejad, Abolfazl ;
Gadsden, Stephen Andrew .
ELECTRONICS LETTERS, 2021, 57 (13) :499-501
[7]  
Al-Muhsen N., 2020, J CLEAN PROD, V284
[8]   A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm [J].
Askarzadeh, Alireza .
COMPUTERS & STRUCTURES, 2016, 169 :1-12
[9]   Extraction of uncertain parameters of single and double diode model of a photovoltaic panel using Salp Swarm algorithm [J].
Ben Messaoud, Ramzi .
MEASUREMENT, 2020, 154 (154)
[10]  
Benabdelkrim B., 2019, J NANO AND ELECT PHY, V11, DOI [10.21272/jnep.11(5).05008, DOI 10.21272/JNEP.11(5).05008]