Maximum Power Point Tracking Using a State-dependent Riccati Equation-based Model Reference Adaptive Control

The present paper proposes an adaptive control method for maximum power point tracking (MPPT) in photovoltaic (PV) systems. To improve the performance of the MPPT, the study develops a two-level adaptive control structure that can facilitate system control and efficiently handle uncertainties and perturbations in the PV systems and in the environment. The first control level is a ripple correlation control (RCC), and the second is a model reference adaptive control (MRAC). The paper emphasizes mainly on designing an MRAC algorithm that improves the underdamped dynamic response of the PV system. The original state-space equation of the PV system is time-varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using the extended state-dependent Riccati equation (ESDRE) approach, an optimal law of the controller is derived for the MRAC system to remove the underdamped modes in the PV systems. An algorithm of scanning the P-V curve of the PV array is proposed to seek the global maximum power point (GMPP) in the partial shading conditions (PSCs). It is shown that the proposed control algorithm enables the system to converge to the maximum power point in partial shading conditions in milliseconds.