Neural Network Model Predictive Control for CHB Converters with FPGA Implementation

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 9; pp. 1 - 12
• Main Authors: Simonetti, Francesco, D'Innocenzo, Alessandro, Cecati, Carlo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-09-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Computer networks; Electric converters; Field programmable gate arrays; Hardware-in-the-loop simulation; Inverters; Model Predictive Control; Multilevel converters; Neural Networks; Neurons; Optimal control; Power converters; Prediction algorithms; Predictive control; STATCOM; Static synchronous compensators; Switches
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3233973

Finite Control Set Model Predictive Control appears an interesting and effective control technique for Cascaded H-Bridge converters but, because of its computational complexity, becomes impractical when the number of levels of the converter increases. This paper proposes a Neural Network-based approach capable of overcoming the computational burden of conventional predictive control algorithms. The proposed control is then applied to a Cascaded H-Bridge Static Synchronous Compensator using FPGA and tested via hardware in the loop. Results and analysis demonstrate that optimal control of multilevel converters with many levels can be obtained with low computational effort.