全球能源互联网（英文）2022，Vol.5Issue(4)：409-417,9.DOI:10.1016/j.gloei.2022.08.007
一种通过学习分支求解机组检修问题的方法
Learning to branch in the generation maintenance scheduling problem
摘要
Abstract
To maximize the reliability index of a power system, this study modeled a generation maintenance scheduling problem that considers the network security constraints and rationality constraints of the generation maintenance practice in a power system. In view of the computational complexity of the generation maintenance scheduling model, a variable selection method based on a support vector machine (SVM) is proposed to solve the 0–1 mixed integer programming problem (MIP). The algorithm observes and collects data from the decisions made by strong branching (SB) and then learns a surrogate function that mimics the SB strategy using a support vector machine. The learned ranking function is then used for variable branching during the solution process of the model. The test case showed that the proposed variable selection algorithm — based on the features of the proposed generation maintenance scheduling problem during branch-and-bound — can increase the solution efficiency of the generation-scheduling model on the premise of guaranteed accuracy.关键词
机组检修问题/支持向量积/变量选择/强分支Key words
Generation maintenance scheduling/Support vector machine (SVM)/Variable selection/Strong Branching (SB)引用本文复制引用
梅竞成,胡景博,万正东,齐冬莲..一种通过学习分支求解机组检修问题的方法[J].全球能源互联网（英文）,2022,5(4):409-417,9.基金项目
The authors thank the Key R&D Project of Zhejiang Province(No.2022C01056)and the National Natural Science Foundation of China(No.62127803). （No.2022C01056）
