标题:An On-line Generator Start-up Strategy Based on Deep Learning and Tree Search
作者:Sun, Runjia; Liu, Yutian
通讯作者:Sun, RJ
作者机构:[Sun, Runjia; Liu, Yutian] Shandong Univ, Sch Elect Engn, Jinan, Shandong, Peoples R China.
会议名称:IEEE-Power-and-Energy-Society General Meeting (PESGM)
会议日期:AUG 05-10, 2018
来源:2018 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)
出版年:2018
关键词:power system restoration; generator start-up; deep learning; tree; search; on-line decision making
摘要:Considering the uncertainty of initial power network topology, restoration time of lines and downtime of generators during generator start-up, this paper presents an on-line decision making strategy of generator start-up based on deep learning and Monte Carlo tree search (MCTS). At first, in order to generate labeled samples of generator start-up atomically, a self-generated samples method is proposed. The sparse autoencoder (SAE) is applied to train the samples to establish a value network, which is used to estimate the optimal value of decision indicator under certain circumstances. Then, the upper confidence bound apply to tree (UCT) algorithm, move pruning technology and value network are applied to MCTS to search the line needed to be restored based on the situation of power system. Finally, a weighted total power generation capability is proposed to integrate the parallel computing results of MCTS and determine the next restored line. The New England 10-unit 39-bus power system is used to show the feasibility and effectiveness of the proposed strategy, and the on-line decision making strategy is compared with traditional method to show its effectiveness for uncertain situations in generator start-up.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060827090&doi=10.1109%2fPESGM.2018.8585913&partnerID=40&md5=399e009e5d19efddfd29b09170410b9b
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