电力系统自动化2016，Vol.40Issue(21)：203-209,7.DOI:10.7500/AEPS20160614001
采用实测数据和主成分分析的直流输电线路故障识别方法
Fault Identification Method for DC Transmission Lines Using Measured Data and Principal Component Analysis
摘要
Abstract
As the electrical entity boundary installed at both ends of the high voltage direct current (HVDC) transmission line has the ability to block high-frequency components,the start voltage caused by the external fault changes gently and amplitude is small;the start voltage caused by the line fault changes steeply when amplitude is large and the time-domain waveform is shown shaking violently.Principal component analysis (PCA) is used to extract time domain features of pole fault voltage and proj ect the time domain features on to PCA space and form cluster point clusterings of external fault and line fault constituting cPC1 and cPC2 coordinates.Hence the characterization and distinction of external fault and internal line fault.After fault,the Euclidean distance between the proj ection values of fault data mapped to PCA space and the cluster centers of PCA identification element is used to distinguish internal and external faults adaptively.The large number of measured data tests show that this method has the ability to interfere without lightning disturbance,samples dithering,harmonic interference and other factors,while improving the protection performance of traveling wave protection with du/dt as the core.And if the historical fault data is reusable to increase PCA cluster point clusterings,PCA fault identification element can continue to improve.关键词
±800 kV直流输电系统/实测故障数据/直流线路电气边界/故障模态/主成分分析Key words
±800 kV DC transmission system/actual fault data/electrical boundary of DC transmission line/fault pattern/principal component analysis引用本文复制引用
束洪春,田鑫萃,安娜..采用实测数据和主成分分析的直流输电线路故障识别方法[J].电力系统自动化,2016,40(21):203-209,7.基金项目
国家自然科学基金资助项目(51267009) （51267009）
NSFC-云南联合基金资助项目(U1202233)。@@@@This work is supported by National Natural Science Foundation of China (No.51267009) and National Natural Science Foundation of China-Yunnan(No.U1202233) （U1202233）
