Parameter estimation of photovoltaic models with plant-based optimization algorithms

There are many meta-heuristic algorithms inspired by nature for solving difficult or complex optimization problems. In this study, the carnivorous plant algorithm (CPA) and tree seed algorithm (TSA), which are recently proposed plant-based meta-heuristic algorithms, are focused on. TSA has an efficient algorithm structure inspired by the connection between trees and seeds. At the same time, the use of two different solution generation mechanisms depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities. However, when the structure of the algorithm is examined in general, it is seen that there are some tendencies such as loss of population diversity and getting stuck in local minima. In order to find solutions to these situations, three different approaches were added to TSA under the name of multi-strategies. The algorithm improved with these approaches is named as the multi strategy-based tree seed algorithm (MS-TSA). In CPA, a teaching factor strategy was added to CPA to minimize the tendency to get stuck in local minima and to improve the solution quality. The algorithm improved with this strategy is called I-CPA. The performance of both proposed methods is first tested on CEC2017 functions. Then, using the input data of photovoltaic (PV) modules, the parameter values of single diode, double diode and photovoltaic module models are identified by both I-CPA and MS-TSA methods. The results obtained with both methods are compared with the results of some classical and modern meta-heuristic algorithms. In addition, the performances of the proposed methods were analyzed with the convergence curve, box plots, current (I) – voltage (V) and power (P) – voltage (V) characteristic curves obtained according to the experimental results. All the results and analyses have shown that both proposed methods are more successful and effective than the compared algorithms.