Title :
A new feature extraction and pattern recognition of partial discharge in solid Material by Neural network
Author :
Oskuoee, M. ; Yazdizadeh, A.R. ; Mahdiani, H.R.
Author_Institution :
Niroo Res. Inst. (NRI), Tehran, Iran
Abstract :
Partial discharge measuring is the mean tool of diagnosis in High voltage systems, equipment and solid dielectrics. Void or any defect in solid dielectrics will produce the partial discharge and may cause permanent failure after some time. According to type of defect, it will produce different patterns of partial discharge. In this paper we will study patterns of partial discharge in solid dielectric that voids are artificially have made in this materials according to experimental measuring in High voltage laboratory. The patterns will distinct with neural network and results of different type of neural network will be discussed.
Keywords :
computerised instrumentation; dielectric materials; fault diagnosis; feature extraction; high-voltage techniques; neural nets; partial discharge measurement; power engineering computing; voids (solid); feature extraction; high voltage equipment; high voltage laboratory; high voltage system diagnosis; neural network; partial discharge measurement; partial discharge patterns; pattern recognition; permanent failure; solid dielectrics; solid material; void; Biological neural networks; Discharges (electric); Electrodes; Feature extraction; Materials; Partial discharges; Voltage measurement; feature; pattern recognition; pd; solid material;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234658