Maximum power point tracking-based model predictive control with reduced sensor count for PV applications

This paper discusses the MPPT based on finite-set model predictive control (FS-MPC) in photovoltaic (PV) systems. Generally, the FS-MPC implementation needs more sensors in comparison with the traditional methods due to the existence of the prediction stage. However, it has a fast transient behaviour in case of fast-changing atmospheric conditions. Thus, to make benefit from the FS-MPC principle without increasing the system’s cost, two algorithms are developed to reduce the number of required sensors without altering the efficiency. First, an accurate model of the PV system including the losses is derived, which enables estimation of the output capacitor voltage. Another approach utilizing an extended Kalman filter (EKF) is proposed. The EKF takes advantage of the derived model of the system and estimates the PV current. In addition, practical PV system applications are considered to have an estimate for cost reduction with the proposed methods. The proposed methodologies are compared with the conventional FS-MPC with full sensor utilization, where analysis and evaluation of the current- and voltage-oriented FS-MPC methods are presented. Moreover, robustness assessment of the proposed algorithms with sensor reduction against parameter variation is examined. All studied methods are validated in simulation and experimentally at different operating conditions.