Deep Reinforcement Learning-based Approach for Online Tuning SMES Damping Controller Parameters

Tao Li, Weihao Hu*, Guozhou Zhang, Jian Li, Qi Huang, Zhe Chen

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

2 Citationer (Scopus)

Abstract

With the penetration of the power electronics dominated large power system, the design of the damping controller needs to ensure the stability of the power system at any disturbance condition. In this context, the closed-loop control agent trained via deep deterministic policy gradient (DDPG) by interacting with massive simulations, for tuning the superconducting magnet energy storage (SMES)-based damping controller parameters, is proposed. It can make autonomous damping mitigation strategy support the grid operator via the current system state. Numerical simulation is tested on the SG-SMES system with a 25MW wind farm, demonstrates the promising performance of the proposed approach.
OriginalsprogEngelsk
Titel2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020
ForlagIEEE Signal Processing Society
Publikationsdato16 okt. 2020
Artikelnummer9276086
ISBN (Trykt)978-1-7281-5216-5
ISBN (Elektronisk)978-1-7281-5215-8
DOI
StatusUdgivet - 16 okt. 2020
Begivenhed2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020 - Virtual, Tianjin, Kina
Varighed: 16 okt. 202018 okt. 2020

Konference

Konference2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020
Land/OmrådeKina
ByVirtual, Tianjin
Periode16/10/202018/10/2020
SponsorIEEE (Beijing Section)
Navn2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020

Fingeraftryk

Dyk ned i forskningsemnerne om 'Deep Reinforcement Learning-based Approach for Online Tuning SMES Damping Controller Parameters'. Sammen danner de et unikt fingeraftryk.

Citationsformater