A multiple learning moth flame optimization algorithm with probability-based chaotic strategy for the parameters estimation of photovoltaic models

Abstract

The parameters of the photovoltaic (PV) models affect the accuracy in the evaluation and control of PV systems. To estimate the parameters of various PV models accurately and reliably, we propose a multiple learning moth flame optimization algorithm with a probability-based chaotic strategy (MLMFO-PBCS). In MLMFO-PBCS, the multiple learning strategy effectively combines the information of flame and moth population in different stages of iteration, providing more chances for moths to update and supplying eminent exploration and exploitation capabilities. Moreover, a probability-based chaotic strategy is introduced to the global optimal solution on each iteration so that a promising solution can be established to update the worst moth, avoiding premature and enhancing the exploitation ability. The proposed MLMFO-PBCS has been used to evaluate parameters of different PV models including single diode, double diode, and PV module. Comprehensive experimental results indicate that MLMFO-PBCS is highly competitive on parameter estimations of PV models in accuracy, reliability, and convergence speed, compared with all compared algorithms.