Parameter Estimation of Photovoltaic Module Using Sine Cosine Algorithm

The increasing demand for electrical energy has made it inevitable to bring forth an environment-friendly energy resource which has to be robust as well as economic. Solar energy fulfils these criteria significantly, and to cultivate such energy, a reliable photovoltaic (PV) model is required. But most of the typical PV system undergoes a low energy conversion ratio because of improper choice of PV parameters. To build a proficient PV model, the estimation of precise and accurate parameters is mandatory. This paper portrays the Sine Cosine Algorithm (SCA) for the estimation of photovoltaic (PV) module parameters. Unknown parameters of the PV model of a single diode PV module are estimated under the standard test condition (STC). PV parameter estimation using SCA has shown a significant minimum value of the fitness function hence maximizing the convergence. A comparative study has been done between SCA and other existing popular techniques named as the nonlinear least square (NLS) method and the modified Newton–Raphson (N-R) method. From the power–voltage (P-V) and current–voltage (I-V) characteristics, it’s found that the SCA model matches more accurately with the datasheet values.