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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.

The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation‐based HGSO (GCMHGSO) algorithm for defining the requirements of the Three‐Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton–Raphson Method (ENRM) is presented to effectively solve the behaviour of the current–voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO‐ENRM to other competitors reveals that the proposed GCMHGSO‐ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO‐ENRM algorithm is stable and robust compared to other competitors.

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This paper discusses a modified V-I relationship for the solar photovoltaic (PV) single diode based equivalent model. The model is derived from an equivalent circuit of the PV cell. A PV cell is used to convert the solar incident light to electrical energy. The PV module is derived from the group of series connected PV cells and PV array, or PV string is formed by connecting the group of series and parallel connected PV panels. The model proposed in this paper is applicable for both series and parallel connected PV string/array systems. Initially, the V-I characteristics are derived for a single PV cell, and finally, it is extended to the PV panel and, to string/array. The solar PV cell model is derived based on five parameters model which requires the data’s from the manufacturer’s data sheet. The derived PV model is precisely forecasting the P-V characteristics, V-I characteristics, open circuit voltage, short circuit current and maximum power point (MPP) for the various temperature and solar irradiation conditions. The model in this paper forecasts the required data for both polycrystalline silicon and monocrystalline silicon panels. This PV model is suitable for the PV system of any capacity. The proposed model is simulated using Matlab/Simulink for various PV array configurations, and finally, the derived model is examined in partial shading condition under the various environmental conditions to find the optimal configuration. The PV model proposed in this paper can achieve 99.5% accuracy in producing maximum output power as similar to manufacturers datasheet.©2020. CBIORE-IJRED. All rights reserved