Open AccessIdentification of generator loss‐of‐excitation from power‐swing conditions using a fast pattern classification method
Author(s) -
Pajuelo Eli,
Gokaraju Ramakrishna,
Sachdev Mohindar S.
Publication year - 2013
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2012.0340
Subject(s) - swing , support vector machine , electrical impedance , generator (circuit theory) , computer science , excitation , control theory (sociology) , power (physics) , pattern recognition (psychology) , artificial intelligence , engineering , electrical engineering , physics , mechanical engineering , control (management) , quantum mechanics
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.