Effective PV Parameter Estimation Algorithm Based on Marine Predators Optimizer Considering Normal and Low Radiation Operating Conditions

Abstract

This paper proposes the Marine Predators Algorithm (MPA) as a new bio-inspired optimization algorithm to extract the parameters of three-photo voltaic models of solar cells. These models are three diode model (TDM), double diode model (DDM) and one-diode model (SDM). The MPA is dependent on the manner of a population of Marine Predators. This optimal strategy allows prey to use an optimal foraging strategy and allows predators to use an intelligent rate policy for encounters. The proposed MPA-based parameter estimation algorithm is tested at normal and low radiation operating conditions. The normal operating condition is employed with the 57\xa0mm diameter commercial silicon solar cell (Case 1), while the Case 2 is based on a multi-crystalline silicon solar cell of area 7.7\xa0cm2 from Q6-1380 under low irradiance levels. The capability of MPA is validated for the three models compared with other competitive algorithms. Simulation results show that high closeness between the estimated and experimental records reflects the high capability of the MPA with more accurate parameters. The RMSE of 8.43854E−4, 7.59E−4 and 7.561E−4 are achieved for Case 1 by using SDM, DDM and TDM, respectively. While, the RMSE has the best levels of 1.61E−5, 1.46E−5, and 1.42E−5 in Case 2, respectively. Also, the MPA has competitive results compared with several optimization algorithms in the literature as sine cosine, particle swarm, salp swarm, grey wolf optimization algorithms. The proposed MPA has good convergence and robust statistical analysis for different operating conditions of low and high irradiance.