ArticleHeliyon2024
An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Optimized parameter estimation of solar PV models using an improved red-tailed hawk algorithm.Scientific reports · 2026Article
- A sport inspired kabaddi game optimizer for accurate parameter estimation of solar photovoltaic models.Scientific reports · 2026Article
- A novel kangaroo escape optimizer for parameter estimation of solar photovoltaic cells/modules via one, two and three-diode equivalent circuit modeling.Scientific reports · 2025Article
- Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models.Scientific reports · 2025Article
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4 authors.
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Abstract
Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results.
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