High Performance Computing Reinforcement Learning Framework for Power System Control (CROSBI ID 734433)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Damjanović, Ivana ; Pavić, Ivica ; Brčić, Mario ; Jerčić, Roko
engleski
High Performance Computing Reinforcement Learning Framework for Power System Control
In this paper, the integration of a power system simulator and reinforcement learning (RL) tools and frameworks is presented. A proposed framework is easily applicable and can serve as a framework for further developing, training, and benchmarking RL algorithms on more complex tasks of power system control. The usage of standard RL frameworks enables a broad range of state-of-the-art algorithms to be implemented with high performance, scalability, and substantial code reuse. Also, the proposed framework design is suitable for scaling onto high-performance computing (HPC) clusters which significantly speeds up the computation. The IEEE 14-bus system is selected to show the simulation results of the proposed method.
High-performance computing ; power system control ; , reinforcement learning
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
1-5.
2023.
nije evidentirano
objavljeno
978-1-6654-5355-4
10.1109/ISGT51731.2023.10066416
Podaci o matičnoj publikaciji
2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
Washington (MD): Institute of Electrical and Electronics Engineers (IEEE)
2167-9665
2472-8152
Podaci o skupu
2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
poster
16.01.2023-19.01.2023
Washington D.C., Sjedinjene Američke Države
Povezanost rada
Elektrotehnika, Interdisciplinarne tehničke znanosti, Računarstvo, Religijske znanosti (interdisciplinarno polje)