Hybrid Maximum Power Point Tracking Method Based on Iterative Learning Control and Perturb & Observe Method
Xibeng Zhang, Don Gamage, Benfei Wang, Abhisek Ukil
Abstract
Maximum power point tracking (MPPT) is used to utilize intermittent solar power fully in the photovoltaic (PV) systems. Tracking the MPP fast, and accurately with changes in the solar irradiance, and the temperature is the goal of MPPT techniques. In this paper, a hybrid MPPT method based on iterative learning control (ILC), and perturb, and observe (P&O) algorithm is proposed. ILC can deal with the periodic variations to eliminate the steady-state oscillations, and errors, when the operation point is close to the MPP or a small irradiance variation occurs. In the proposed hybrid MPPT technique, a high frequency power P&O method without deadtime is used to improve the dynamic response when the irradiance changes rapidly. This paper presents the theoretical background of the hybrid MPPT algorithm, design, and stability analysis. Simulation, and hardware validation results substantiate the effectiveness of the proposed method.