Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters
Songda Wang, Tomislav Dragičević, Gustavo Gontijo, Sanjay K. Chaudhary, Remus Teodorescu
Abstract
This article proposes a machine learning (ML)-based emulation of model predictive control (MPC) for modular multilevel converters (MMCs). In particular, the artificial neural network model, trained offline by the data collected from the traditional fast MPC method, is used to control the MMCs with high accuracy. With this offline training, the majority of computational burden is transferred from online to offline. Therefore, the proposed ML MPC can replace the role of the traditional MPC. The experimental results show that the proposed ML-based MPC has the same performance as the conventional MPC but a significantly computationally efficient structure. The finding from the letter provides ground for many other applications for ML-based emulation of complex controllers in power electronic systems.