Multi-Agent Reinforcement Learning-Based Decentralized Controller for Battery Modular Multilevel Inverter Systems

Autor: Ali Mashayekh, Sebastian Pohlmann, Julian Estaller, Manuel Kuder, Anton Lesnicar, Richard Eckerle, Thomas Weyh
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Electricity, Vol 4, Iss 3, Pp 235-252 (2023)
Druh dokumentu: article
ISSN: 2673-4826
DOI: 10.3390/electricity4030014
Popis: The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of 1.1×10−5. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled.
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