Parameter estimation of solar PV models using self-adaptive differential evolution with dynamic mutation and pheromone strategy

In this paper we investigate the parameter estimation of solar pho-tovoltaic (PV) models using the self-adaptive differential evolution al-gorithm with dynamic fitness-ranking mutation and pheromone strat-egy (SDE-FMP). The dynamic mutation divides the population into three groups according to fitness values and selects groups and their vectors with adaptive probabilities to create a mutant vector. The algorithm also encodes scaling factor and crossover rate values into target vectors to use in mutation and crossover operations and adjusts them with pheromones in the selection process. Experimental results show that the SDE-FMP algorithm can give the solutions with the lowest errors and is overall competitive with the compared methods regarding the mean errors.