|国家科技期刊平台
| 注册
首页|期刊导航|中国电机工程学会电力与能源系统学报（英文版）|Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

Mariana Kamel Renchang Dai Yawei Wang Fangxing Li Guangyi Liu

中国电机工程学会电力与能源系统学报（英文版）2021，Vol.7Issue(3)：433-442,10.
✕
中国电机工程学会电力与能源系统学报（英文版）2021，Vol.7Issue(3)：433-442,10.DOI:10.17775/CSEEJPES.2020.04570

Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

Mariana Kamel 1Renchang Dai 2Yawei Wang 3Fangxing Li 1Guangyi Liu4

作者信息

  • 1. The University of Tennessee,Knoxville,TN,USA
  • 2. Puget Sound Energy,Bellevue,WA,USA
  • 3. PayPal,San Jose,CA,USA
  • 4. Envision Digital,Redwood City,CA,USA
  • 折叠

摘要

关键词

Deep deterministic policy gradient/generation re-dispatch/hybrid learning/overload relief/reinforcement learning

Key words

Deep deterministic policy gradient/generation re-dispatch/hybrid learning/overload relief/reinforcement learning

引用本文复制引用

Mariana Kamel,Renchang Dai,Yawei Wang,Fangxing Li,Guangyi Liu..Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches[J].中国电机工程学会电力与能源系统学报（英文版）,2021,7(3):433-442,10.

基金项目

This work was supported by the Science and Technology Project of State Grid Corporation of China (No.5100-201958522A-0-0-00). （No.5100-201958522A-0-0-00）

中国电机工程学会电力与能源系统学报（英文版）

OACSCDCSTPCDEISCI

2096-0042

访问量6
|
下载量0
段落导航相关论文