Machine Learning Methods for Power Flow Control of Multi-Active-Bridge Converters

Mian Liao, Haoran Li, Ping Wang, Yenan Chen, Minjie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Controlling the multiway power flow in a multi-active-bridge (MAB) converter is important for maintaining high performance and realizing sophisticated functions. Traditional methods for MAB converter control rely on precise circuit models of the system. This paper presents machine learning (ML) methods for controlling the power flow in a MAB converter. A feedforward neural network (FNN) was developed to capture the non-linear characteristics and predict the phases needed to achieve the targeted power flow. The neural network was trained using a large amount of data collected with a set of known phase angles, and was used to predict the phase to achieve the targeted multiway power flow. A 6-port MAB converter was built and tested to verify the theory, validate the methodology, and demonstrate the hardware implementation of the ML-based control strategy. Machine learning based power flow control methods can achieve comparable accuracy as traditional model-based power flow control methods, and can function without a precise lumped circuit element model of the MAB converter.

Original languageEnglish (US)
Title of host publication2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436359
DOIs
StatePublished - 2021
Event22nd IEEE Workshop on Control and Modelling of Power Electronics, COMPEL 2021 - Cartagena, Colombia
Duration: Nov 2 2021Nov 5 2021

Publication series

Name2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021

Conference

Conference22nd IEEE Workshop on Control and Modelling of Power Electronics, COMPEL 2021
Country/TerritoryColombia
CityCartagena
Period11/2/2111/5/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Keywords

  • Machine learning
  • Multi-active-bridge converter
  • Neural network
  • Power flow control

Fingerprint

Dive into the research topics of 'Machine Learning Methods for Power Flow Control of Multi-Active-Bridge Converters'. Together they form a unique fingerprint.

Cite this