A Path to Configurable Solid State Transformers and Energy Routers: Introduction to Modular Active Cell Control

This paper introduces a control strategy for a Modular Multi-Active Bridge (MMAB) architecture that extends the single phase shift as a novel approach to enable fully decentralized control of the Modular Active Cell (MAC) on a single, arbitrarily large multi-winding transformer. The MAC aims to provide a fully modular, configurable, and scalable solution for Solid State Transformers (SSTs) and Energy Routers, enabling full power and voltage variability in isolated interconnections of diverse DC networks. A novel modeling approach based on discrete-time state-space is introduced. The model is verified through measurements and simulation, showing high accuracy and prediction capability.

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Publication Details

Journal
IEEE Transactions on Power Electronics
Published
2025-10-15
DOI
https://doi.org/10.1109/tpel.2025.3621885
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

A Path to Configurable Solid State Transformers and Energy Routers: Introduction to Modular Active Cell Control

Xiaotian Yang, Raffael Schwanninger, Nikolai Weitz, Martin März et al.
IEEE Transactions on Power Electronics
Advanced Memory and Neural Computing
article

A Path to Configurable Solid State Transformers and Energy Routers: Introduction to Modular Active Cell Control

Xiaotian Yang, Raffael Schwanninger, Nikolai Weitz, Martin März, Niklas Stöcklein
article en

Abstract

This paper introduces a control strategy for a Modular Multi-Active Bridge (MMAB) architecture that extends the single phase shift as a novel approach to enable fully decentralized control of the Modular Active Cell (MAC) on a single, arbitrarily large multi-winding transformer. The MAC aims to provide a fully modular, configurable, and scalable solution for Solid State Transformers (SSTs) and Energy Routers, enabling full power and voltage variability in isolated interconnections of diverse DC networks. A novel modeling approach based on discrete-time state-space is introduced. The model is verified through measurements and simulation, showing high accuracy and prediction capability.

IEEE Transactions on Power ElectronicsVol. 41(4)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Fraunhofer Institute for Integrated Systems and Device Technology (DE)
Openalex Percentile: Top 71%
Advanced Memory and Neural Computing
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