Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach

被引:124
作者
Askarzadeh, Alireza [1 ]
Coelho, Leandro dos Santos [2 ,3 ]
机构
[1] Grad Univ Adv Technol, Inst Sci & High Technol & Environm Sci, Dept Energy Management & Optimizat, Kerman, Iran
[2] Pontificia Univ Catolica Parana, Ind & Syst Engn Grad Program, Curitiba, PR, Brazil
[3] Univ Fed Parana, Dept Elect Engn, BR-80060000 Curitiba, PR, Brazil
关键词
Photovoltaic modules; Parameter estimation; Simplified bird mating optimizer; ARTIFICIAL NEURAL-NETWORK; SOLAR-CELL; MODEL PARAMETERS; ALGORITHM; IDENTIFICATION;
D O I
10.1016/j.enconman.2014.10.025
中图分类号
O414.1 [热力学];
学科分类号
070201 [理论物理];
摘要
The main goal of this paper is to provide a framework to accurately estimate the electrical equivalent circuit parameters of photovoltaic arrays by use of an efficient heuristic technique. Owing to the non-linearity of the current vs. voltage (I-V) characteristics of PV modules, using a superior optimization technique helps to effectively find the real electrical parameters. Inspired by the mating process of different bird species, bird mating optimizer (BMO) is a new invented search technique which has shown superior performance for solving complex optimization problems. In this paper, the original BMO algorithm is simplified and used to estimate the electrical parameters of the module model for an amorphous silicon PV system at different operating conditions. The simplified BMO (SBMO) eliminates tedious efforts of parameter setting in original BMO and also modifies some rules. The usefulness of the proposed algorithm is investigated by comparing the obtained results with those found by two particle swarm optimization (PSO) variants, two harmony search (HS) variants as well as seeker optimization algorithm (SOA). Based on the investigated situations of this paper, SBMO yields more accurate results than the other studied methods. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:608 / 614
页数:7
相关论文
共 22 条
[1]
A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator [J].
Almonacid, F. ;
Perez-Higueras, P. J. ;
Fernandez, Eduardo F. ;
Hontoria, L. .
ENERGY CONVERSION AND MANAGEMENT, 2014, 85 :389-398
[2]
A new estimation approach for determining the I-V characteristics of solar cells [J].
AlRashidi, M. R. ;
AlHajri, M. F. ;
El-Naggar, K. M. ;
Al-Othman, A. K. .
SOLAR ENERGY, 2011, 85 (07) :1543-1550
[3]
Askarzadeh A, 2012, INT J ENERGY RES
[4]
Bird mating optimizer: An optimization algorithm inspired by bird mating strategies [J].
Askarzadeh, Alireza .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (04) :1213-1228
[5]
Artificial bee swarm optimization algorithm for parameters identification of solar cell models [J].
Askarzadeh, Alireza ;
Rezazadeh, Alireza .
APPLIED ENERGY, 2013, 102 :943-949
[6]
Artificial neural network training using a new efficient optimization algorithm [J].
Askarzadeh, Alireza ;
Rezazadeh, Alireza .
APPLIED SOFT COMPUTING, 2013, 13 (02) :1206-1213
[7]
Parameter identification for solar cell models using harmony search-based algorithms [J].
Askarzadeh, Alireza ;
Rezazadeh, Alireza .
SOLAR ENERGY, 2012, 86 (11) :3241-3249
[8]
HYBRID EVOLUTIONARY-HEURISTIC ALGORITHM FOR CAPACITOR BANKS ALLOCATION [J].
Barukcic, Marinko ;
Nikolovski, Srete ;
Jovic, Franjo .
JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2010, 61 (06) :332-340
[9]
A COMPARATIVE-STUDY OF EXTRACTION METHODS FOR SOLAR-CELL MODEL PARAMETERS [J].
CHAN, DSH ;
PHILLIPS, JR ;
PHANG, JCH .
SOLID-STATE ELECTRONICS, 1986, 29 (03) :329-337
[10]
A new method for evaluating illuminated solar cell parameters [J].
Chegaar, M ;
Ouennoughi, Z ;
Hoffmann, A .
SOLID-STATE ELECTRONICS, 2001, 45 (02) :293-296