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000000194 001__ 194
000000194 037__ $$aENY-ARTICLE-2008-152
000000194 041__ $$aeng
000000194 088__ $$aI08/2000/I-133
000000194 100__ $$aRebizant, W$$uInstitute of Power Transmission and High Voltage Technology, University of Stuttgart, Germany. (on leave from Wroclaw University of Technology, Wroclaw, Poland)
000000194 245__ $$aANN-based Detection of OS Conditions in Power System
000000194 260__ $$c2000-09-18
000000194 300__ $$a6p
000000194 500__ $$aProceedings of the 12th International Conference on Power System Protection. PSP 2000, Bled, Slovenia, September 27th-29th, 2000 / [Ed. by Tadeja Babnik, Ferdinand Gubina]. Ljubljana : Faculty of Electrical Engineering, 2000. pp. 51-56.
000000194 520__ $$aIn the paper a new neural network based out-of-step protection scheme is presented. Numerous ANNs have been trained and tested with input patterns calculated from voltage and current signals generated with use of EMTP/ATP programme. Optimal selection of decision signals for ANN feeding has been done with help of proposed statistical PDF distance indices. The OS detection/prediction efficiency was assessed for various ANN sizes and data window lengths. The impact of measurement rate on the classification results has been analysed. Wide robustness checking of developed solutions has been done against different fault types and synchronous machine ratings.
000000194 6531_ $$aneural network
000000194 6531_ $$aANN
000000194 6531_ $$astatistical distance indices
000000194 6531_ $$aprobability density function
000000194 6531_ $$aEMTP
000000194 6531_ $$apower system
000000194 8560_ $$fleon99@pwr.wroc.pl
000000194 8564_ $$uhttp://zet10.ipee.pwr.wroc.pl/record/194/files/$$zAccess to Fulltext
000000194 909CO $$ooai:zet10.pwr.wroc.pl:194$$pglobal
000000194 980__ $$aARTICLE