Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach  

作　　者：Jingcheng Mei Guojiang Zhang Donglian Qi Jianliang Zhang 

机构地区：[1]College of Electrical Engineering,Zhejiang University,Hangzhou 310027,P.R.China [2]State grid Jiangsu Electric Power Company Limited,Nanjing,210024,P.R.China

出　　处：《Global Energy Interconnection》2021年第5期493-500,共8页全球能源互联网（英文版）

基　　金：supported by the National Key Research and Development Program of China(Basic Research Class)(No.2017YFB0903000);the National Natural Science Foundation of China(No.U1909201).

摘　　要：To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.

关 键 词：Transmission maintenance scheduling Mixed integer programming(MIP) Machine learning Bayesian optimization(BO) BRANCH-AND-BOUND 

分 类 号：TM75[电气工程—电力系统及自动化]