The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.
This paper presents a simplified parameter estimation procedure for a photovoltaic (PV) single diode model (SDM). Based on an iterative method, the module parameters are estimated for the SDM using the information from the datasheet. The five parameters of the SDM are estimated based on the datasheet using two steps. In the first step, the best ideality factor, n, value is determined, and in the next step, the value of the shunt resistance, R
sh
, can be calculated to increase its accuracy. For validating the performance of the estimated parameters, a model that considers the difference in the solar irradiance and the temperature. In the standard testing conditions (STC), the proposed procedure shows the best results relative to other methods. The optimization of the parameters, such as n, R
sh
, and series resistance, R
s
e, allows the minimum error between the values obtained from the proposed technique and datasheet.
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