Open AccessDe‐noising of time‐domain spectroscopy data for reliable assessment of power transformer insulation
Author(s) -
Mishra Deepak,
Baral Arijit,
Chakravorti Sivaji
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.0974
Subject(s) - transformer , wavelet transform , wavelet , noise (video) , computer science , current transformer , electronic engineering , engineering , acoustics , electrical engineering , artificial intelligence , physics , voltage , image (mathematics)
Polarisation–depolarisation current (PDC) measurement and its analysis is a popular technique for assessing the condition of transformer insulation. Owing to the low magnitude of PDC, recording noise‐free PDC data from in‐situ power transformers is a challenge. Once the relaxation current data get affected by noise, it becomes difficult to formulate insulation model (as recorded data loses its characteristic shape). This further makes the data difficult to analyse and predict insulation condition. In this study, two de‐noising techniques are discussed (one is based on Wavelet Transform while the other is based on Stockwell Transform) for eliminating low‐frequency non‐stationary noise from recorded PDC data. Comparison between these two techniques suggests de‐noising using Stockwell Transform is advantageous over wavelet analysis. The proposed methodology is first tested on data recorded from the sample prepared in the laboratory and then on data measured from real‐life in‐service power transformer.