RF‐based location of partial discharge sources using received signal features. Issue 1 (1st March 2019)

• Record Type: Journal Article
• Authors: Iorkyase, Ephraim T.; Tachtatzis, Christos; Glover, Ian A.; Atkinson, Robert C.
• Is Part Of: High voltage. Volume 4:Issue 1(2019)
• Page Start: 28
• Page End: 32
• Publication Date: 2019-03-01
• Subjects: partial discharge measurement -- feature extraction -- radiowave propagation -- feature selection -- correlation theory; RF‐based location -- partial discharge sources -- insulation system -- electrical equipment -- localisation approach -- RF propagation -- correlation‐based feature selection -- radiofrequency signal measurement -- sensors -- RF signal measurement -- PD location -- received signal features -- PD pulse activity -- RF spatial patterns -- electrical pulse emission -- CFS -- feature extraction; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2018.5027
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : Partial discharges (PDs) are symptomatic of some localised defects in the insulation system of electrical equipment. PD activity emits electrical pulses in the form of radio frequency (RF) signals which can be captured using appropriate sensors. The analysis of the measured RF signals facilitates localisation of PD. This study investigates the plausibility of using purely RF received signal features of PD pulses to locate PD at low cost. A localisation approach based on the analysis of these features has been developed, with the assumption that PDs generate unique RF spatial patterns due to the complexities and nonlinearities of RF propagation. In this approach, two distinct frequency bands which hold different PD information are exploited. PD location features are extracted from the main PD signal and the two sub‐band signals. Correlation‐based feature selection (CFS) is employed for feature selection and dimensionality reduction. Experimental results show that PD location can be inferred from the features of the PD pulses. The application of CFS to PD data reduces the memory/computational demand and improves localisation accuracy.