An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm

Maximizing the output power of photovoltaic (PV) systems is crucial in all PV applications to improve energy efficiency and system performance. Maximum power point tracking (MPPT) is utilized in PV systems to track the voltage that maximizes their output power under varying conditions. In this paper, an enhanced MPPT estimation algorithm is proposed based on the H-adaptive extended Kalman filter (EKF). The proposed method adapts to possible changes in the system’s noise statistics due to variations in irradiance, operating temperature, and system’s aging. The approach is compared to common MPPT estimation techniques; namely the Perturb and Observe (P&O) method and the EKF. The method was validated using experimental data collected from a PV array, to demonstrate practical applicability. Results showcase the adaptability of the proposed approach to variations in the process noise covariance, measurement noise covariance, and the dynamic system scaling factor parameter. These attributes are essential for a sustained extraction of the maximum power from the PV system.