An Application of Neural Network-based Sliding Mode Control for Multilevel Inverters

Abstract

Multi-level 3-phase inverters using cascaded H-bridges are becoming prominent in the electric drive and renewable energy sectors due to their high capacity and ability to withstand high voltage shocks. Therefore, the modulation and control techniques used in these multilevel inverters have a crucial influence on the quality of the output voltage they produce. The significantly high common-mode voltage amplitude they generate is one of their disadvantages, causing leakage currents and harmonics. This article proposes a new technique using sliding mode control combined with neural networks to manage a three-phase multi-level inverter. The research objective of this innovative technique is to eliminate the need for current controllers and conventional modulation that relies on carrier signals, reducing hardware calculations and enhancing dynamic response. In addition, it demonstrates the ability to minimize harmonics, common mode voltage, and the number of switching counts, thereby limiting the inverter switching losses and increasing device performance. Simulation results performed on a 5-level 3-phase inverter using cascaded H-bridges have confirmed the effectiveness of the proposed method.