Machine learning based model predictive control for grid connected enhanced switched capacitor cross‐connected switched multi‐level inverter (ESC3SMLI)

Abstract

AbstractThis article describes an enhanced switched capacitor cross‐connected switched multilevel inverter (ESC3SMLI) with a machine learningbased model‐predictive control method (ML‐MPCM). The proposed ESC3SMLI produces nine levels using eight switches, including two bidirectional switching devices, a single DC source, and a capacitor. Additionally, the design generates a negative level without the use of extra circuitry like an H‐bridge, which implies that switches in ESC3SMLI are less subject to voltage stress. In comparison to a conventional H‐Bridge setup, only 3 switches conduct for each operating mode, leading to fewer switching transitions, reduced switching and conduction losses, and better efficiency. The exponential rise in computational complexity required to perform the optimisation, which consumes an unacceptably high quantity of computing resources, is the most important drawback of MPCMs. This article examines inverters static and dynamic behaviour since grid‐connected utility is intended. In specific, ESC3SMLI is controlled with high accuracy using the artificial neural network (ANN) model that was trained offline using the information gathered from the conventional MPC method. The rapid and accurate reaction, as well as the superior function, of the control scheme is demonstrated by its dynamic performance during sudden shifts in current and PF.