Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters

Songda Wang, Tomislav Dragicevic, Gustavo Figueiredo Gontijo, Sanjay K. Chaudhary, Remus Teodorescu

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

37 Citationer (Scopus)
209 Downloads (Pure)

Abstract

This paper 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 the 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 lower computational burden. This finding provides ground for many other applications for ML based emulation of complex controllers in power electronic systems.
OriginalsprogEngelsk
TidsskriftIEEE Transactions on Industrial Electronics
Vol/bind68
Udgave nummer11
Sider (fra-til)11628 - 11634
ISSN0278-0046
DOI
StatusUdgivet - nov. 2021

Fingeraftryk

Dyk ned i forskningsemnerne om 'Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters'. Sammen danner de et unikt fingeraftryk.

Citationsformater