Parameter identification of the photovoltaic cell model with a hybrid Jaya-NM algorithm

Optik ◽  
2018 ◽  
Vol 171 ◽  
pp. 200-203 ◽  
Author(s):  
Xiong Luo ◽  
Longpeng Cao ◽  
Long Wang ◽  
Zihan Zhao ◽  
Chao Huang
Keyword(s):  
Parameter Identification ◽  
Cell Model ◽  
Photovoltaic Cell

An improved lion swarm optimization for parameters identification of photovoltaic cell models

2019 ◽  
Vol 42 (6) ◽  
pp. 1191-1203
Author(s):  
Zhong-qiang Wu ◽  
Zong-kui Xie ◽  
Chong-yang Liu
Keyword(s):  
Parameter Identification ◽  
Cell Model ◽  
Chaotic Map ◽  
Photovoltaic Cell ◽  
Standard Test ◽  
Local Optimum ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Test Function ◽  
Adaptive Parameter

In this paper, a parameter identification method of photovoltaic cell model based on improved lion swarm optimization is presented. Lion swarm optimization is a novel intelligent algorithm proposed in recent years, but it has problems such as local optimum and slow convergence. To overcome such limitations, we can combine the tent chaotic map, adaptive parameter and chaotic search strategy to further improve the search ability of the algorithm and avoid trapping in local optimum. The simulation of standard test function shows that the performance of improved lion swarm algorithm is superior to the other six algorithms. Then the algorithm is applied to the parameter identification of photovoltaic cells under two kinds of models and different irradiance, the simulation results verify the superiority and effectiveness of the improved lion swarm optimization in the application of photovoltaic cell parameter identification.

scholarly journals Parameter Identification of Photovoltaic Cell Model Based on Enhanced Particle Swarm Optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 840
Author(s):  
Rongjie Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Mean Squared Error ◽  
Cell Model ◽  
Particle Swarm ◽  
Photovoltaic Cell ◽  
Estimation Accuracy ◽  
Swarm Optimization ◽  
Minimum Mean Squared Error ◽  
Enhanced Particle Swarm Optimization

Photovoltaic (PV) cell (PVC) modeling predicts the behavior of PVCs in various real-world environmental settings and their resultant current–voltage and power–voltage characteristics. Focusing on PVC parameter identification, this study presents an enhanced particle swarm optimization (EPSO) algorithmto accurately and efficiently extract optimal PVC parameters. Specifically, the EPSO algorithm optimizes the minimum mean squared error between measured and estimated data and, on this basis, extractsthe parameters of the single-, double-, and triple-diode models and the PV module. To examine its effectiveness, the proposed EPSO algorithm is compared with other swarm optimization algorithms. The effectiveness of the proposed EPSO algorithm is validated through simulation. In addition, the proposed EPSO algorithm also exhibits advantages such as an excellent optimization performance, a high parameter estimation accuracy, and a low computational complexity.

scholarly journals Understanding Photovoltaic Cell Dynamic Resistance Behavior with Changing Incident Light Intensity - draftv1.pdf

2019 ◽  
Author(s):  
FRANCISCO J. GARCIA-SANCHEZ
Keyword(s):  
Equivalent Circuit ◽  
Cell Model ◽  
Short Circuit ◽  
Incident Light ◽  
Photovoltaic Cell ◽  
Open Circuit ◽  
Photovoltaic Device ◽  
Dynamic Resistance ◽  
Analytic Expressions ◽  
Static Conditions

A theoretical examination of the general behavior that should be expected to be displayed by the magnitude of the dynamic resistance of a conventional illuminated photovoltaic device within the power-generating quadrant of its <i>I-V</i> characteristics, when measured in quasi-static conditions from the short-circuit point to the open-circuit point, at various incident illumination intensities. The analysis is based on assuming that the photovoltaic device in question may be adequately described by a simple conventional d-c lumped-element single-diode equivalent circuit solar cell model, which includes significant constant series and shunt resistive losses, but lacks any other secondary effects. Using explicit analytic expressions for the dynamic resistance, we elucidate how its magnitude changes as a function of the terminal variables, the incident illumination intensity and the model’s equivalent circuit elements’ parameters.

Implementation of digital temperature control system on photovoltaic cell model: An experimental analysis

Optik ◽  
2019 ◽  
Vol 176 ◽  
pp. 324-333 ◽  
Author(s):  
Asita Samal ◽  
A. Mohanty ◽  
P.K. Ray ◽  
S. Mohanty ◽  
P.P. Mohanty
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Experimental Analysis ◽  
Cell Model ◽  
Photovoltaic Cell ◽  
Temperature Control System
Sign in / Sign up

Export Citation Format

Share Document