Reduced-Sensor-Based Optimal Switching Sequence Model Predictive Current Control for Grid-Tied Inverter With LCL Filter

This paper presents an improved reduced-sensor based Optimal-Switching-Sequence Model-Predictive-Current-Control (OSS-MPCC) algorithm for a grid-tied inverter with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filter with only injected grid current measurements. The OSS-MPCC algorithm utilizes four types of signals, namely, the estimated grid voltage symmetrical components obtained from the virtual flux observer, the state estimates obtained from the full-state Luenberger observer, the reference of positive sequence injected grid current obtained from the SOGI-QSG based symmetrical voltage estimations, and switching states of the inverter to perform predictive current tracking in grid-tied operation. The proposed algorithm offers several advantages simultaneously. It successfully limits the switching frequency spectrum of inverter voltage, reduces the number of sensors from nine to three, keeps the grid current THD, even in the advent of voltage unbalancing, below 2.5%, demonstrates robustness against the filter parameter mismatches, and still keeps the computational time below 15μs comparable to previous OSS-MPC algorithms. The results were numerically verified through simulations in MATLAB and validated using an 1.5 kW experimental setup with a two-level inverter, programmable AC supply as grid, and Microlab box controller with EMC blockset for PWM generation.