Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis  

作　　者：Qais H. Alsafasfeh Ikhlas Abdel-Qader Ahmad M. Harb 

机构地区：[1]Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, USA [2]Electrical Engineering Department, Tafila Technical University, Tafila, Jordan [3]Energy Engineering Department, German Jordanian University, Amman, Jordan

出　　处：《Energy and Power Engineering》2012年第6期506-522,共17页能源与动力工程（英文）

摘　　要：A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy.A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy.

关 键 词：FAULT Detection and Classification Protective RELAYING PCA PSCAD 

分 类 号：R73[医药卫生—肿瘤]