Abstract
Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ENR</sub> ) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOA <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ENR</sub> outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.
Highlights
The increasing depletion of traditional energy sources, price reductions, and permanently ubiquitous in nature have all accelerated the development of renewable energy applications in recent decades [1], [2]
In this work, a novel methodology based on Hybridized Arithmetic optimization Algorithm (HAOA) and the Efficient Newton Raphson (ENR) method is proposed to precisely extract the parameters of the single and double-diode models using real experimental data obtained under various weather conditions
The proposed HAOAENR is verified based on several statistical data and compared with three variants of the Arithmetic optimization algorithm (AOA) algorithm and with recently well-published papers such as: HAOALW, HAOANR, AOAL, Farmland Fertility Optimization (FFANR) [117], Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimization (CHCLPSONR) [118], Improved Slime Mould optimizer (ImSMALW) [119], Marine Predators Algorithm (MPALW) [50], Self-adaptive Ensemble-based Differential Evolution algorithm (SEDEL) [94], and Time-Varying Acceleration Coefficients PSO (TVACPSONR) [60]
Summary
The increasing depletion of traditional energy sources, price reductions, and permanently ubiquitous in nature have all accelerated the development of renewable energy applications in recent decades [1], [2]. The Photovoltaic (PV) system is one of these renewable energy sources that can be. There is no perfection in industry and the PV panels that are manufactured do not accurately replicate the specifications in the datasheet. This opens the door for a slew of new difficulties and issues to arise as a result of the new PV technologies. Despite the solar PV system’s exponential expansion, a number of problems such as solar irradiance fluctuations, temperature changes, technology, methods, high initial costs, aging, uncertainties of.
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