Title :
Identification of generator loss-of-excitation from power-swing conditions using a fast pattern classification method
Author :
Pajuelo, Eli ; Gokaraju, Ramakrishna ; Sachdev, M.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
Abstract :
This study describes a support vector machine (SVM)-based technique for identifying loss-of-excitation (LOE) condition in synchronous generators from other disturbances such as external faults and power-swing conditions. In this new approach, only one zone of LOE is required and the time coordination is reduced significantly. The proposed method is compared with traditional two-zone impedance method. Several operating conditions within the generator capability are used to verify the generality of the SVM-based classifier. The proposed classifier identifies an LOE condition in all cases before the impedance enters the larger mho impedance zone. Faults and power-swing conditions are identified correctly, thereby preventing incorrect operation of the LOE impedance zone.
Keywords :
electric machine analysis computing; fault diagnosis; pattern classification; support vector machines; synchronous generators; LOE condition; LOE impedance zone; SVM-based classifier; SVM-based technique; fast pattern classification method; generator loss-of-excitation identification; mho impedance zone; power-swing conditions; support vector machine; synchronous generators; time coordination; two-zone impedance method;
Journal_Title :
Generation, Transmission & Distribution, IET
DOI :
10.1049/iet-gtd.2012.0340