An enhanced arithmetic optimization algorithm for global maximum power point tracking of photovoltaic systems under dynamic irradiance patterns

Abstract

ABSTRACT The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation.