An Improved Brain Storming Optimization Algorithm for Estimating Parameters of Photovoltaic Models

Abstract

Estimating parameters for various photovoltaic (PV) models is of great importance in simulating, evaluating, and controlling PV systems. To achieve the effective and accurate parameters of PV models, this paper presents an improved brain storming optimization (IBSO). In IBSO, a new individuals' generation scheme is developed to balance the global and local search capability in the entire iterative process. Furthermore, an improved individual clustering scheme is developed to decrease the computational cost of K-means in the original BSO. In addition, a self-adaptive individuals' update scheme is introduced to further balance the global and local exploration capability. The proposed IBSO is employed to identify the parameters of different PV models, i.e., single diode, double diode, and PV module. The experimental results show that the IBSO can generate an extremely promising performance compared with other state-of-the-art algorithms, especially in terms of accuracy and reliability.

Keywords

• Photovoltaic (PV)
• brain storming optimization (BSO)
• global optimization
• population-based stochastic optimization