Partial discharge classification at DC voltage

Fromm, U.; Morshuis, P.H.F.

doi:10.1109/icsd.1995.523017

Cited by 21 publications

(22 citation statements)

References 4 publications

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“…In [4] it was found that the voltage dropped just a couple of volts during a discharge under DC voltage, thus the residual voltage was very close to the cavity ignition voltage. A high residual voltage will make the discharge magnitude smaller, and increase the frequency of the discharges.…”

Section: Discharge Magnitudementioning

confidence: 99%

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Experimental Challenges when Measuring Partial Discharges under Combined DC and High Frequency AC Voltage

Olsen

Velo

Mauseth

2017

NORD-IS

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The present paper reviews some of the challenges involved in designing and building an experimental setup for measurement of partial discharge under combined AC and DC voltage. A design of a modular test cell is discussed, as well as all parameters relevant to proper control of the experiment conditions. The described experimental setup is capable of producing sinusoidal AC voltage at a variable frequency up to 5 kHz superimposed a high DC voltage. The challenges related to intermittent noise reduction was remedied by placing the setup in a Faraday cage and controlling the setup from the outside by optical cables. All equipment in the test setup was connected to the power grid through low-pass filters.

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Section: Discharge Magnitudementioning

confidence: 99%

“…This influence the supply of starting electrons: any electrons must be supplied by bulk conduction [4]. At AC, starting electrons may be available from previous discharges on the cavity surface.…”

Section: Polarity Of Discharges and Waiting Time For Start Electronsmentioning

confidence: 99%

Experimental Challenges when Measuring Partial Discharges under Combined DC and High Frequency AC Voltage

Olsen

Velo

Mauseth

2017

NORD-IS

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“…This determined several key differences between PD under AC and DC conditions, finding that DC discharges occurred less frequently, were of smaller magnitude, and, in general, were more of symptom of defects than a cause of them. Other research has looked into DC testing under a range of conditions [5]- [8], establishing common patterns for corona, surface and void discharges.…”

Section: B Ac Vs DC Partial Dischargementioning

confidence: 99%

Partial discharge activity in polymeric cable insulation under high voltage AC and DC

Morris

Siew

2017

2017 52nd International Universities Power Engineering Conference (UPEC)

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The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.1 Abstract-The focus of this paper is to report on analysis of partial discharge characteristics in insulation samples containing artificially created voids under AC and DC excitation. Samples were initially tested under AC conditions to determine that the artificial void was the dominant source of PD. Once this was proven the samples were tested under DC to generate knowledge on PD under these conditions. The results were analyzed, with differences between AC and DC PD in artificial voids determined. The impact of these differences on potential methods of PD detection are also briefly discussed.

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“…Most of the research was done by Fromm [16][17][18], Morshuis et al [19] and Gulski et al [20]. Fromm has built a model with parameters including discharge magnitude q i , time of discharge interval t i , and instantaneous voltage across the test object, which describes the stochastic discharge process at dc voltage.…”

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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Partial discharge classification at DC voltage

Cited by 21 publications

References 4 publications

Experimental Challenges when Measuring Partial Discharges under Combined DC and High Frequency AC Voltage

Experimental Challenges when Measuring Partial Discharges under Combined DC and High Frequency AC Voltage

Partial discharge activity in polymeric cable insulation under high voltage AC and DC

Remote Detection and Recognition of Electrostatic Discharge From HVDC Transmission Lines

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