Early warning method of HVDC line continuous commutation failure based on Adaboost algorithm

被引：0
|
作者：
Zhang G. [1 ]
Li Z. [2 ]
Wang B. [2 ]
Liu M. [1 ]
Dong X. [2 ]
机构：
[1] State Grid Shandong Electric Power Research Institute, Jinan
[2] State Key Lab of Control and Simulation of Power Systems and Generation Equipments, Department of Electrical Engineering, Tsinghua University, Beijing
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 19期
基金：
中国国家自然科学基金;
关键词：
Adaboost; Continuous commutation failures; Failure early warning; Statistical learning;
D O I：
10.19783/j.cnki.pspc.181477
中图分类号：
学科分类号：
摘要：
In order to overcome the difficulty in Continuous Commutation Failure (CCF) early warning caused by the lack of CCF analytical mechanism, a CCF warning system is proposed based on Adaboost algorithm in statistical learning modeling. The eigenvector of the classifier considers the analytical expression of single commutation failure, and makes full use of the measured data during commutation failure from simulation or actual data from industrial field to obtain the nonlinear decision plane that corresponds to CCF early warning problem. The model verification is evaluated based on multi-scene fault simulation with CIGRE standard model in PSCAD, where the proposed warning system is able to send out warning signal with high precision based on the considerably small data set as well as sufficiently small measured data required, proving the validity and practicability of the proposed method in engineering practice. © 2019, Power System Protection and Control Press. All right reserved.
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页码：69 / 77
页数：8
相关论文
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