A sport inspired kabaddi game optimizer for accurate parameter estimation of solar photovoltaic models
Tummala S. L. V. Ayyarao, G. Indira Kishore, Ark Dev, U. Siddaraj
TL;DR
A new optimization algorithm inspired by the sport of Kabaddi is developed to accurately estimate parameters for solar photovoltaic models.
Contribution
The novel Kabaddi Game Optimizer (KGO) is introduced, which outperforms existing optimizers in PV parameter estimation.
Findings
KGO achieved the best average rank on the CEC 2017 benchmark set compared to seven other optimizers.
KGO coupled with Newton-Raphson method provided accurate PV parameter estimation with low RMSE values.
The method demonstrated robust and fast performance for single, double, triple diode models and a PV module.
Abstract
This paper proposes a Kabaddi Game Optimizer (KGO), a sport-inspired metaheuristic for accurate solar photovoltaic (PV) modelling. KGO models Kabaddi strategies (Dubki and Akraman), adaptive weights and weak-player replacement to balance exploration and exploitation. Its performance is first validated on the CEC 2017 benchmark set against seven well-known optimizers, where KGO consistently attains the best average rank. KGO is then coupled with the Newton–Raphson method to estimate parameters of single, double, and triple diode PV models and a PWP-201 PV module. Using RTC France cell and module data, KGO achieves RMSE values of 7.729857E−04, 7.43146E−04, 7.3771E−04, and 2.0529E−03 for the single-, double-, and triple-diode models, and the PV module, respectively, demonstrating accurate, robust, and fast PV parameter estimation.
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Taxonomy
TopicsPhotovoltaic System Optimization Techniques · Solar Radiation and Photovoltaics · Solar Thermal and Photovoltaic Systems