Wavelet-based de-noising of on-line PD signals captured by Pearson coil in covered-conductor overhead distribution networks

Autor: Suhail Aftab Qureshi, Rana A. Jabbar, Mikael Nordman, Matti Lehtonen, Murtaza Hashmi
Jazyk: angličtina
Rok vydání: 2012
Předmět:
Zdroj: INTERNATIONAL JOURNAL OF ELECTRICAL POWER AND ENERGY SYSTEMS. (43):1185-1192
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2012.06.035
Popis: Partial discharge (PD) detection has been regarded as one of the most effective on-line predictive maintenance test and diagnostic tool for the condition monitoring of high voltage (HV) equipments. The relatively new and challenging application is conducting on-line high frequency PD measurements for the monitoring of falling trees on the covered-conductor (CC) overhead distribution lines. Generally, the high frequency PD measurements taken in high voltage (HV) laboratory Faraday cage are not prone to electromagnetic disturbances (EMDs). However, various external interferences may corrupt on-line and on-site measurements. Getting very low magnitude PD signals from measurements taken in corrupt environment is one of the big challenges for continuous monitoring. In this paper, wavelet transform (WT) is suggested as a powerful technique to remove noise and disturbances from PD signals captured in CC overhead distribution networks, which are completely absorbed by electromagnetic interferences (EMIs). The advantages and difficulties faced while taking on-line and on-site PD measurements have been discussed. Pearson coil is used as a sensor to detect PDs for this specific application. In the next stage of future developments, the proposed methodology would be implemented in a real-life environment using many Pearson coils to get reliable on-line and on-site PD measurements to detect and localize falling tress on CC overhead distribution networks. The condition monitoring and insulation diagnosis of the other valuable distribution network components can also be carried out using this technique to cope with the requirements of future smart grids.
Databáze: OpenAIRE
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