Artificial neural network controller for grid current quality improvement in solid-state transformers

In this paper, an improved modular multilevel converter (MMC) current controller is proposed for grid current harmonic mitigation in solid-state transformers (SSTs), regardless of the non-linear or unbalanced load positions at the SST stages. The proposed MMC current controller is achieved using three control strategies based on the harmonic order and the harmonic sequence: a low-order harmonic compensator, a negative-sequence harmonic compensator for unbalanced control, and a high-order harmonic compensator based on an artificial neural network (ANN) controller, trained offline using a fuzzy logic (FL) controller. The use of such non-linear controllers for both training and control ensures an active filtering feature for the MMC controller. This makes the proposed solution a good alternative to solutions based on extra filters or an increased switching frequency, which inevitably increases the system costs and losses. The proposed control strategy is implemented and evaluated in MATLAB/Simulink software under various load conditions and parameter changes, and results from multiple simulations are presented.