6533b86ffe1ef96bd12ce92e

RESEARCH PRODUCT

An improved MSD-based method for PD defects classification

Pietro RomanoRoberto CandelaA. AbatePietro Lucio BuccheriL. Testa

subject

Contextual image classificationbusiness.industryComputer scienceElectronic Optical and Magnetic MaterialDiagonalFeature extractionWavelet transformPattern recognitionCondensed Matter PhysicSignalpartial dischargeSettore ING-IND/31 - Elettrotecnicawavelet transform.Pattern recognition (psychology)Partial dischargeElectronic engineeringFeature (machine learning)Artificial intelligencebusiness

description

The new proposed method of pattern recognition is based on the application of Multi-resolution Signal Decomposition (MSD) technique of wavelet transform. This technique has showed off interesting properties in capturing the embedded horizontal, vertical and diagonal variations within an image obtained from the PD pattern in a separable form. This feature was exploited to identify in the PD pattern's MSD, relative at various family of partial discharge sources, some detail images typical of a single discharge phenomenon. The classification of a generic PD phenomenon is feasible through a comparison between its detail images and the detail images typical of a single discharge phenomenon. Tests have been performed on specimens having single defects. The obtained results prove that the proposed improved classification methods is quite efficient and accurate. ©2006 IEEE.

yearjournalcountryeditionlanguage
2006-06-012006 IEEE 8th International Conference on Properties & applications of Dielectric Materials
https://doi.org/10.1109/icpadm.2006.284266