Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids
Distributed multi-step Q(λ) learning for Optimal Power Flow of large-scale power grids
复制标题
大规模电网最优潮流的分布式多步Q(δ)学习
DOI：
10.1016/j.ijepes.2012.04.062
复制
发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
J. J. Wang
中科院分区：
文献类型：
--
作者：
T.Yu;J. Liu;K. W. Chan;J. J. Wang
This paper presents a novel distributed multi-step Q(λ) learning algorithm (DQ(λ)L) based on multi-agent system for solving large-scale multi-objective OPF problem. It does not require any manipulation to the conventional mathematical Optimal Power Flow (OPF) model. Large-scale power system is first partitioned to subsystems and each subsystem is managed by an agent. Each agent adopts the standard multi-step Q(λ) learning algorithm to pursue its own objectives independently and approaches to the global optimal through cooperation and coordination among agents. The proposed DQ(λ)L has been thoroughly studied and tested on the IEEE 9-bus and 118-bus systems. Case studies demonstrated that DQ(λ)L is a feasible and effective for solving multi-objective OPF problem in large-scale complex power grid.
登录
查看更多内容
DOI：
10.1109/59.496179
发表时间：
1996-05
期刊：
--
影响因子：
--
作者：
Zheng Yan;N. Xiang;Boming Zhang;S. Y. Wang;T. Chung
通讯作者：
Zheng Yan;N. Xiang;Boming Zhang;S. Y. Wang;T. Chung
DOI：
10.1016/j.ijepes.2011.01.032
发表时间：
2011-06
影响因子：
5.2
作者：
Ke-yan Liu;Yun-hua Li;W. Sheng
通讯作者：
Ke-yan Liu;Yun-hua Li;W. Sheng
DOI：
10.1049/ip-gtd:20045007
发表时间：
2005-03
期刊：
--
影响因子：
--
作者：
C. Jiang;C. Wang
通讯作者：
C. Jiang;C. Wang
影响因子：
6.6
作者：
J. Batut;A. Renaud
通讯作者：
J. Batut;A. Renaud
DOI：
10.1109/ettandgrs.2008.328
发表时间：
2008-12
期刊：
2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing
影响因子：
--
作者：
Bo Fan;J. Pu
通讯作者：
Bo Fan;J. Pu