An Adaptive Model Predictive Controller for Current Sensorless MPPT in PV Systems

Finite control set model predictive control (MPC) is a model-based control method that can include multi-objective optimization, constrained control, adaptive control, and online auto-tuning of weighting factors all in a single controller that exhibits fast dynamic tracking. This paper utilizes the model-based framework of MPC to develop a sensorless current maximum power point tracking (MPPT) algorithm. Eliminating the current sensor can reduce the cost and improve the reliability of the photovoltaic system. This paper also utilizes constrained control and online auto-tuning of MPC to develop an adaptive perturbation MPPT to reduce steady-state oscillation and improve dynamic performance. This paper builds in a single framework the different layers of the MPPT problem: control, estimation, and MPPT. The proposed adaptive perturbation sensorless current mode MPPT (ASC-MPPT) technique performance is compared to the well-known incremental conductance (InCon) MPPT technique. The EN50530 European industrial test standards were used to demonstrate performance.