# A sport inspired kabaddi game optimizer for accurate parameter estimation of solar photovoltaic models

## 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.

## Key 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.