Improved gradient‐based optimizer for parameters extraction of photovoltaic models

Abstract

An efficient operation of the solar photovoltaic (PV) system relies on accurate and reliable equivalent models and parameters. For different modular circuit models, the parameter estimation of PV solar cells is a key task that is usually translated into optimization problems solved by metaheuristic algorithms. Gradient-based optimizer (GBO), which was proposed in 2020, is a method with swarm characteristics that was developed from Newton's method. Gradient search rule (GSR) and local escape operator (LEO) is main components of the GBO algorithm. This paper proposes an improved GBO (IGBO) optimization algorithm based on the original algorithm. In IGBO, two strategies, adaptive weights and chaotic behaviour, are introduced to adjust the adaptive parameters. The purpose of adaptive weights is to effectively approach the optimal solution and avoid falling into local optimum in different search phases of the algorithm. Chaotic behaviour aims to replace the randomness of the metaheuristic algorithm and to improve the convergence speed and accuracy of the algorithm. To validate the performance of IGBO, it is applied to the parameter extraction of different PV models. The PV models used in this paper include single diode model (SDM), double diode model (DDM) and PV module model. Finally, comparing with the original algorithm and six well-established algorithms, the experimental data show that the IGBO algorithm is highly competitive, as reflected by the best results obtained in terms of root mean square error values. It can be concluded that IGBO is more competitive in robustness, convergence and accuracy.