Format: HTML | BibTeX | DC | EndNote | NLM | MARC | MARCXML
000000062 001__ 62
000000062 037__ $$aENY-ARTICLE-2008-053
000000062 041__ $$aeng
000000062 100__ $$aJanik, P
000000062 245__ $$aRBF and SVM Neural Networks for Power Quality Disturbances Analysis
000000062 260__ $$c2005-09-10
000000062 500__ $$aElectrical power quality and utilisation. EPQU '05. 8th International conference. Proceedings, Cracow, September 21-23, 2005 / [Ed. by R. Pawełek] Lodz : Institute of Electrical Power Engineering. Technical University of Lodz, cop. 2005. s. 191-198
000000062 520__ $$aThis paper presents classification results of different power quality disturbances. SVM and RBF neural networks are considered as appropriate classifiers for power quality issues, however SVM networks show better performance. Simulation of disturbed signals by parametric equations enabled the assessment of signal parameters influence on classification rate. Positive results encouraged further research. Model of supply system suffering from sags was simulated. Independent from line length and sag duration the classifier was set to recognize different sag types. The idea of space phasor was applied to obtain distinctive patterns from three phase system. Wavelet transform was used to find the beginning of sags. Positive classification results were obtained.
000000062 700__ $$aLobos, T
000000062 700__ $$aSchegner, P
000000062 8560_ $$fprzemyslaw.janik@pwr.wroc.pl
000000062 8564_ $$uhttp://zet10.ipee.pwr.wroc.pl/record/62/files/$$zAccess to Fulltext
000000062 88__a $$aI07/1996/I-278
000000062 909CO $$ooai:zet10.pwr.wroc.pl:62$$pglobal
000000062 980__ $$aARTICLE