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<p>Solar energy is one of the most important renewable energy sources, due to its availability, ecological balance and wide presence. With increasing populations worldwide and growing economies, there is a need to provide electricity from solar energy. Photovoltaic panels are devices that convert sunlight to electrical energy. Estimation of the DC parameters of the PV panel is essential in order to predict its performance. This process is a multi-modal, multi-variable and non-linear problem. In recent years, several metaheuristics have been widely used to estimate the DC parameters of PV panels, because they can solve complex problems with less computational complexity. The Jellyfish Search (JS) optimizer is a recent metaheuristic technique inspired by the behavior of jellyfish in the ocean. It uses a time control mechanism to switch between their two behaviors; follow the ocean current, or move inside the jellyfish swarm. In this paper, a modified version of the JS optimizer (MJS) is proposed to estimate the parameters of a PV panel based on its single and double diode models. The proposed MJS algorithm combines linearly the three updating equations of the JS method into one updating equation, and uses three adaptive weighting parameters to control the behavior of jellyfish, and switch the algorithm from exploration to exploitation. The performance of our algorithm, in terms of estimation accuracy and computational complexity, are evaluated under various solar irradiances and temperature conditions, and it is compared with many other metaheuristics such as particle swarm optimization (PSO), grey wolf optimizer (GWO), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and differential evolution (DE) The simulation results indicate that our algorithm provides a very small RMSE value of 0.0159, with a reduced computational complexity as much as <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(13\%\)</EquationSource> </InlineEquation> lower than the JS method.</p>

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A modified jellyfish search optimizer for fast and accurate estimation of photovoltaic panel parameters

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Abir Betka,
  • Abida Toumi,
  • Amel Terki,
  • Madina Hamiane

摘要

Solar energy is one of the most important renewable energy sources, due to its availability, ecological balance and wide presence. With increasing populations worldwide and growing economies, there is a need to provide electricity from solar energy. Photovoltaic panels are devices that convert sunlight to electrical energy. Estimation of the DC parameters of the PV panel is essential in order to predict its performance. This process is a multi-modal, multi-variable and non-linear problem. In recent years, several metaheuristics have been widely used to estimate the DC parameters of PV panels, because they can solve complex problems with less computational complexity. The Jellyfish Search (JS) optimizer is a recent metaheuristic technique inspired by the behavior of jellyfish in the ocean. It uses a time control mechanism to switch between their two behaviors; follow the ocean current, or move inside the jellyfish swarm. In this paper, a modified version of the JS optimizer (MJS) is proposed to estimate the parameters of a PV panel based on its single and double diode models. The proposed MJS algorithm combines linearly the three updating equations of the JS method into one updating equation, and uses three adaptive weighting parameters to control the behavior of jellyfish, and switch the algorithm from exploration to exploitation. The performance of our algorithm, in terms of estimation accuracy and computational complexity, are evaluated under various solar irradiances and temperature conditions, and it is compared with many other metaheuristics such as particle swarm optimization (PSO), grey wolf optimizer (GWO), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and differential evolution (DE) The simulation results indicate that our algorithm provides a very small RMSE value of 0.0159, with a reduced computational complexity as much as \(13\%\) lower than the JS method.