Normal Distribution-Based Three-Stage Empirical Teaching Optimization for PV Parameter Extraction

The performance of photovoltaic (PV) systems is critically dependent on the determination of unknown parameters, such as photocurrent and diode ideality factor, which vary with the irradiation intensity and temperature. These parameters exhibit dynamic variations in response to changes in irradiation intensity and operating temperature. Although many identification methods have been proposed for extracting unknown PV cells, the accuracy of unknown parameter identification under different irradiation intensities and temperatures is not high. This paper proposes an efficient Normal Distribution-Based Three-Stage Empirical Teaching Optimization Algorithm (NDTETOA) for accurate identification of unknown parameters in photovoltaic cells under varying irradiation intensities and temperature conditions. To reduce problem dimensionality, a parameter decomposition technique is introduced. This manuscript conducts comparative experiments to evaluate the algorithm's performance on two single-diode models under various irradiation intensities and temperature conditions.