A multiagent system based cuckoo search optimization for parameter identification of photovoltaic cell using Lambert W-function

被引：29

作者：

Gude, Srihari ^{[1
]}

Jana, Kartick Chandra ^{[2
]}

机构：

[1] Dept EEE, BIT Mesra Off Campus Deoghar, IIT ISM, Dept EE, Dhanbad, Jharkhand, India

[2] Dept EE, IIT ISM, Dhanbad, India

来源：

APPLIED SOFT COMPUTING | 2022年 / 120卷

关键词：

Cuckoo search optimization; Multiagent system; Multiagent system based cuckoo search; optimization; Lambert W-function; Parameter Estimation; MODELS;

D O I：

10.1016/j.asoc.2022.108678

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a multiagent system based cuckoo search optimization (MASCSO) algorithm is developed by combining a multiagent system (MAS) and cuckoo search optimization (CSO) to exploit the complementary nature of the MAS and CSO. The existing behavioral rules in MAS are modified to get improved convergence. The MASCSO algorithm is tested on benchmark single objective bounded constrained functions. Nonparametric statistical analysis is performed to validate the MASCSO algorithm against benchmark algorithms. The proposed MASCSO algorithm is applied to estimate parameters of photovoltaic (PV) cell and module using Lambert W-function (MASCSO(L)) and Direct (MASCSO(D)) current estimation approaches, respectively. The relative power error percentage at the maximum power point (%4PMPP) is proposed to justify the effectiveness of these parameter estimation techniques. The results indicated that parameters estimated from MASCSO(L) technique have lowered %4PMPP by 54.46% and 38.88%, respectively, for PV cell and module. (c) 2022 Elsevier B.V. All rights reserved.

引用

页数：18