A novel way of parameter estimation of solar photovoltaic system

Abstract

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.

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…Bisht and Sikander 108 used JSO to optimize the parameters of the solar photovoltaic (PV) model. They used JSO to optimize the parameters of a single-diode PV model using various performance measures, such as PV characteristics, power-voltage, and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE), and normalized mean absolute error (NMAE).…”

“…Optimizing hyper-parameters of deep learning JSO Chou et al 102 Optimizing hyper-parameters of LSSVR JSO-LSSVR Chou and Truong 88 Estimating parameters of a single-phase power transformer JSO Youssef et al 106 Optimizing parameters of solar photovoltaic (PV) model JSO Bisht and Sikander 108 Finding optimal coefficients of DWT JSO Dhevanandhini and Yamuna 103 Finding optimal configurations of RVFL JSO Elkabbash et al 104 Identification of parameters of PEMFCs JSO Gouda et al (1) Self-adaption: adaptive or self-adaptive algorithms are those that can self-tune their algorithm-specific and common control parameters. The algorithm-specific parameters in JSO include the number of iterations, population size, spatial distribution coefficients, and motion coefficients.…”

Design and analysis of electric spring based on battery-less current-source inverter

“…To reduce carbon emissions, the contribution of renewable energy, such as wind, solar and biomass energy, is increasing rapidly in many countries (Bisht and Sikander, 2022). However, the fluctuations and intermittency of renewable energy pose challenges to the power system, leading to degradation of power quality and even instability of the power system (Çelik et al , 2022).…”

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

“…20 The saturation current and shunt resistance values are determined using the bond graph model, ignoring series or shunt resistance, and a combination of iterative and computational methods. [21][22][23] A curve fitting technique using a streamlined electrical model is also provided in. 24 This technique may obtain the characteristic I-V curve in real-time with just four current and voltage coordinates.…”

“…By offering an equation for ideality factors as a function of datasheet parameters, 19 the researchers assume that series and shunt resistance values are constant 20 . The saturation current and shunt resistance values are determined using the bond graph model, ignoring series or shunt resistance, and a combination of iterative and computational methods 21‐23 . A curve fitting technique using a streamlined electrical model is also provided in 24 .…”