电力系统保护与控制2024，Vol.52Issue(8)：1-9,9.DOI:10.19783/j.cnki.pspc.231036
基于IF-AD-ELM的特高压直流输电系统故障辨识
Fault identification of a UHVDC transmission system based on IF-AD-ELM
摘要
Abstract
There is a problem of low sensitivity and difficulty in identifying high-resistance ground faults in existing fault detection methods for ultra high voltage direct current(UHVDC)transmission system.Thus a fault identification method for a UHVDC transmission system based on the integer factor(IF)-approximate derivative(AD)and an extreme learning machine(ELM)is proposed.The IF is used to analyze the signals at different sampling frequencies,and the AD method is used to obtain different degrees of detail coefficients for the signals.First,the signal is down-sampled based on different IFs,and the AD method is used to calculate the first,second and third order approximate derivatives of the obtained signal.Secondly,the entropy characteristics of each sub-signal are calculated.Then,recursive feature elimination with a cross validation(RFECV)algorithm is used to screen the features of the obtained series of features,and the ELM is used to identify the UHVDC transmission system fault types.Finally,the UHVDC system model of±800 kV is built in the Matlab/Simulink environment to simulate different fault types.The experimental results show that the proposed method has higher accuracy and strong tolerance to transition resistance when identifying different types of faults in a UHVDC transmission system.关键词
特高压直流/下采样/特征选择/极限学习机/故障辨识Key words
UHVDC/down-sampling/feature selection/ELM/fault identification引用本文复制引用
杨新宇,赵庆生,韩肖清,梁定康,王旭平..基于IF-AD-ELM的特高压直流输电系统故障辨识[J].电力系统保护与控制,2024,52(8):1-9,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.51777132). 国家自然科学基金项目资助(51777132) （No.51777132）
国家自然科学青年基金项目资助(51907138) （51907138）
国网山西省电力公司科技项目资助(520510220002) （520510220002）
