A population diversity-controlled differential evolution for parameter estimation of solar photovoltaic models

2022 ◽  
Vol 51 ◽  
pp. 101938
Author(s):  
Yang Yu ◽  
Kaiyu Wang ◽  
Tengfei Zhang ◽  
Yirui Wang ◽  
Chen Peng ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Population Diversity ◽  
Solar Photovoltaic

Parameter Estimation By Using An Improved Bee Memory Differential Evolution Algorithm (Ibmde) To Simulate Biochemical Pathways

2013 ◽  
Vol 8 (999) ◽  
pp. 1-6
Author(s):  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Mohd Shahir Shamsir ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Biochemical Pathways ◽  
Evolution Algorithm

A novel way of parameter estimation of solar photovoltaic system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rahul Bisht ◽  
Afzal Sikander
Keyword(s):  
Parameter Estimation ◽  
Mean Absolute Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Solar Panels ◽  
Content Type ◽  
Solar Photovoltaic System

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

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