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```000000012 001__ 12
000000012 037__ \$\$aENY-ARTICLE-2008-010
000000012 041__ \$\$aeng
000000012 088__ \$\$aRZ I07/05/I-033
000000012 100__ \$\$aSchegner, P
000000012 245__ \$\$aClassification of low voltage distribution networks by means of voltage distortion.
000000012 260__ \$\$c2005-03-30
000000012 300__ \$\$a6p
000000012 500__ \$\$a2005 IEEE St. Petersburg PowerTech. Conference proceedings . St. Petersburg, Russia, June 27-30, 2005. Moskva: Laboratorija Bazovych Znanij 2005, ref. 80, 6 p.,
000000012 520__ \$\$aDetermination of power quality becomes more and more important in the future. Low voltage networks are usually large and very complex. Therefore the calculation of power quality parameters by modeling as equivalent network is hardly possible in practice. That’s why new methods for efficient and exact estimation of power quality parameters in low voltage networks are necessary. The presented method is based on the fact that networks of similar structure have a similar behavior in power quality. The Points of Common Coupling (PCC) are divided into different classes, where each class consists of PCC’s with similar characteristics. This way the method allows the estimation of power quality levels based on the class a PCC is assigned to. The paper demonstrates the method for the 5th voltage harmonic as an example power quality parameter. The above-mentioned classification of substations is based on probabilistic neural networks.
000000012 6531_ \$\$aPower distribution
000000012 6531_ \$\$apower quality
000000012 6531_ \$\$aharmonic distortion
000000012 6531_ \$\$apower system identification
000000012 6531_ \$\$aneural network applications
000000012 700__ \$\$aMeyer, J
000000012 700__ \$\$aLobos, T
000000012 700__ \$\$aWaclawek, Z
000000012 700__ \$\$aMuehlwitz, M
000000012 8560_ \$\$fzbigniew.leonowicz@pwr.wroc.pl