Electromagnetic Interference Measurements at 900 MHZ on 230-KV and 500-KV Transmission Lines

Chartier, V.L.; Sheridan, R.; DiPlacido, J. N.; Loftness, Marvin O.

doi:10.1109/tpwrd.1986.4307944

Cited by 12 publications

(1 citation statement)

References 4 publications

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“…Some measurements have also been made in the TV band. In 1986, Chartier et al detected corona interference up to 800 ∼ 1000 MHz by using a high gain parabolic antenna and low-noise preamplifier [9]. Recently, large number of researches have been employed to the transmission lines detecting applications.…”

Section: Introductionmentioning

confidence: 99%

Remote Detection and Recognition of Electrostatic Discharge From HVDC Transmission Lines

Zhang¹,

Liu²,

Liu³

2014

PIER C

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Abstract-To remotely detect corona discharge from High-Voltage Direct Current (HVDC) transmission lines, a detecting system combining detecting platform and data progressing system is designed. Detecting platform is developed resorting to the principle of differential noise reduction, which can fulfill narrow-band detection breaking away interference from broadcasting and easily catch the electrostatic discharge signal. To get rid of interference from spark discharge, a data progressing system containing feature extractions, clustering and recognition technologies is developed. Clustering is realized by extracting five discharge features, including peak factor, form factor, skewness, kurtosis and mean square error. The unsupervised clustering Fuzzy C-Means (FCM) method is used to achieve fast separation for electrostatic discharges and provide training set for pattern recognition. Pattern recognition resorts to Support Vector Machine (SVM) method. For comparison, Back Propagation (BP) and Learning Vector Quantization (LVQ) approaches are taken to test the recognition ability. The results show that SVM recognizer with a recognition rate of 97.5% achieves higher performance than BP and LVQ methods. It can be concluded that the detecting system can be an interesting alternative for electrostatic discharge detection.

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Section: Introductionmentioning

confidence: 99%