High-dimensional Steady-state Security Region Boundary Approximation in Power Systems Using Feature Non-linear Converter and Improved Oblique Decision Tree  

作　　者：Yuxin Dai Jun Zhang Peidong Xu Tianlu Gao David Wenzhong Gao 

机构地区：[1]School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China [2]Department of Electrical and Computer Engineering,Denver,USA

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第6期1786-1797,共12页现代电力系统与清洁能源学报(英文)

基　　金：This work was supported by the National Key Research and Development Program of China(No.2018AAA0101504);the Science and Technology Project of State Grid Corporation of China"fundamental theory of human inthe-loop hybrid-augmented intelligence for power grid dispatch and control".

摘　　要：The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant challenge.To address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree.First,to better characterize the SSRB,boundary samples are generated using the proposed sampling method.These samples are distributed within a limited distance near the SSRB.Then,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear converter.Con-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree.Finally,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.

关 键 词：Steady-state security region boundary sample generation feature non-linear conversion oblique decision tree 

分 类 号：TM73[电气工程—电力系统及自动化]