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Comparison of On-Line Partial Discharge Detection Techniques for High Voltage Power Cable Joints and Terminations


Prisca Paul Chambela
Aviti Thadei Mushi

Abstract

Cable joints and terminations play a vital role in providing dependable electrical connection, mechanical support and physical safeguard. These provide electrical stress control to shielded power cables. Despite their usefulness, they suffer from partial discharges (PD) because of enhanced voltage stress, moisture ingress and poor workmanship (during installation). Therefore, it is necessary to undertake on-line PD detection to determine their state. Some of these techniques are capacitive coupler (CC); acoustic emission (AE); and high frequency current transducer (HFCT). This article presents a literature review of these techniques based on the cost, availability, and applicability. The comparative analysis is also provided on location of their sensors, quantification and detection ability. The CC technique involves the quantification of the coupler sensor input measured in mV/pC by realizing the time of flight between two sensors for which the results are used to estimate location of the PD. Meanwhile, the AE technique has an advantage of high immunity to electrical noise, with a caveat that acoustic signals are highly attenuated within the cable joints. Additionally, the combination of the acoustic sensors and PD electrical couplers can be used to discriminate PDs from electrical noise. The HFCT has two methods – with demonising (HFCT-WiD) and without demonising (HFCT-WoD). The HFCT-WiD technique can significantly reduce the sensor’s detection sensitivity due to its high value of noise to signal ratio (NSR). Comparatively, the HFCT has the best results of quantification and detection ability for PDs among all three techniques investigated. However, in places where electrical noise is severe, PD activities may be detected effectively with AE technique. Further work is needed to statistically map these methods and establish their correlation with experimental data.


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eISSN: 2619-8789
print ISSN: 1821-536X