Oscillation Damping Using Reinforcement Learning Controlled HVDC Transmission

doi:10.1109/GTD49768.2023.00039

	Oscillation Damping Using Reinforcement Learning Controlled HVDC Transmission
	Rui Fan 1; Renke Huang 2; Qiuhua Huang 3; Jie Tan 4; Yu Liu5
	2023-05-22
会议录名称	2023 IEEE PES GTD INTERNATIONAL CONFERENCE AND EXPOSITION (GTD)
页码	67-71
发表状态	已发表
DOI	10.1109/GTD49768.2023.00039
摘要	This paper presents a novel deep reinforcement learning based control method for damping power system inter-Area oscillations. A guided surrogate-gradient-based evolutionary strategy (GSES) is used to control the transferred power on high voltage DC (HVDC) transmissions, which could help improve the damping effect when inter-Area oscillations occur. The GSES algorithm trains a reinforcement learning agent to learn the best parameters of the HVDC controller, with an objective of reducing the magnitude and duration of inter-Area oscillations. Unlike many existing reinforcement learning methods, the GSES algorithm does not require a traditional back-propagation process to update the policy parameters, enabling an easier, more robust and efficient training procedure with less required hyper-parameters. In addition, as an evolutionary strategy, the proposed GSES-based HVDC oscillation damping control approach can engage with multiple individual workers during the training process through parallel computation techniques, which significantly accelerates the computational speed and improves the training efficiency. The proposed GSES-based HVDC controller is compared with a conventional oscillation damping method on the IEEE 39-Bus New England system. Results indicate that the proposed GSES-based HVDC control approach performs better and it can effectively damp power system inter-Area oscillations. © 2023 IEEE.
会议录编者/会议主办者	EnerjiSA ; IEEE PES ; Omicron Bahrain ; SEL
关键词	High voltage DC transmission inter-area oscillation deep reinforcement learning transient stability
会议名称	2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023
会议地点	Istanbul, Turkiye
会议日期	22-25 May 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234515012042
EI主题词	HVDC power transmission
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 703.1 Electric Networks ; 706.1 Electric Power Systems ; 706.1.1 Electric Power Transmission ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 731.2 Control System Applications ; 732.1 Control Equipment ; 931.1 Mechanics
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/333542
专题	信息科学与技术学院_PI研究组_刘宇组
作者单位	1.University of Denver, Denver, USA 2.Shanghai Jiaotong University, Shanghai, China 3.Colorado School of Mines, Golden, USA 4.Google, Mountain View, USA 5.ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Rui Fan,Renke Huang,Qiuhua Huang,et al. Oscillation Damping Using Reinforcement Learning Controlled HVDC Transmission[C]//EnerjiSA, IEEE PES, Omicron Bahrain, SEL:Institute of Electrical and Electronics Engineers Inc.,2023:67-71.