Separation of Radio-Frequency Sources and Localization of Partial Discharges in Noisy Environments

Sensors

Guillermo Robles¹  José Manuel Fresno²  Juan Manuel Martínez-Tarifa² 

[1] Department of Electrical Engineering, University Carlos III of Madrid, Avda. Universidad, 30, Leganés 28911, Spain;

关键词: partial discharges;    spectral power;    Condition Monitoring;    RF Measurements;    RF Localization;    particle swarm optimization;   

DOI  :  10.3390/s150509882

来源: mdpi

【 摘 要 】

The detection of partial discharges (PD) can help in early-warning detection systems to protect critical assets in power systems. The radio-frequency emission of these events can be measured with antennas even when the equipment is in service which reduces dramatically the maintenance costs and favours the implementation of condition-based monitoring systems. The drawback of these type of measurements is the difficulty of having a reference signal to study the events in a classical phase-resolved partial discharge pattern (PRPD). Therefore, in open-air substations and overhead lines where interferences from radio and TV broadcasting and mobile communications are important sources of noise and other pulsed interferences from rectifiers or inverters can be present, it is difficult to identify whether there is partial discharges activity or not. This paper proposes a robust method to separate the events captured with the antennas, identify which of them are partial discharges and localize the piece of equipment that is having problems. The separation is done with power ratio (PR) maps based on the spectral characteristics of the signal and the identification of the type of event is done localizing the source with an array of four antennas. Several classical methods to calculate the time differences of arrival (TDOA) of the emission to the antennas have been tested, and the localization is done using particle swarm optimization (PSO) to minimize a distance function.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.