Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources

Dennis Albert; Philipp Schachinger; Herwig Renner; Peter Hamberger; Franz Klammler; Georg Achleitner

doi:10.1007/s00502-020-00846-1

Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources

Dennis Albert^*, Philipp Schachinger, Herwig Renner, Peter Hamberger, Franz Klammler, Georg Achleitner

^*Corresponding author for this work

Institute of Electrical Power Systems (4320)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Low-frequency currents (LFC) or quasi-DC (QDC) in the electrical power transmission network, and especially in power transformers, are causing negative effects such as an increase in noise level, in reactive power consumption and in power losses. Currently, no classification of LFC is available to identify a possible source. In order to identify the origin of undesired LFC, classifications of LFC in current and audio measurements are defined. They are based on a spectrum analysis of current and audio measurements. These classifications are successfully tested in laboratory and field measurements. Consequently, LFC sources are identified by field and laboratory measurements and analytical approaches. For power transformer operators, a user-friendly and fast method is presented to identify LFC in the transformers. The method is based on audible measurements and serves as a first estimator for low-frequency currents in power transformers.

Original language	English
Title of host publication	Cigre 2020 Session
Pages	427-436
Number of pages	10
Volume	137
DOIs	https://doi.org/10.1007/s00502-020-00846-1
Publication status	Published - Dec 2020
Event	CIGRE e-session 2020 - Virtuell, France Duration: 24 Aug 2020 → 3 Sept 2020 https://www.cigre.org/GB/events/cigre-e_session

Publication series

Name	Elektrotechnik und Informationstechnik
Publisher	Springer Wien
ISSN (Print)	0932-383X

Conference

Conference	CIGRE e-session 2020
Country/Territory	France
City	Virtuell
Period	24/08/20 → 3/09/20
Internet address	https://www.cigre.org/GB/events/cigre-e_session

Keywords

DC BIas
GIC
Geomagnetically Induced Currents
transformer
Transformer Audible Noise
Low Frequency Currents
Power Transformers
quasi-DC
transformer neutral point current
low-frequency currents
transformer noise

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/s00502-020-00846-1Licence: Other

Cite this

Albert, D., Schachinger, P., Renner, H., Hamberger, P., Klammler, F., & Achleitner, G. (2020). Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources. In Cigre 2020 Session (Vol. 137, pp. 427-436). (Elektrotechnik und Informationstechnik). https://doi.org/10.1007/s00502-020-00846-1

Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources. / Albert, Dennis; Schachinger, Philipp; Renner, Herwig et al.
Cigre 2020 Session. Vol. 137 2020. p. 427-436 (Elektrotechnik und Informationstechnik).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Albert, D, Schachinger, P, Renner, H, Hamberger, P, Klammler, F & Achleitner, G 2020, Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources. in Cigre 2020 Session. vol. 137, Elektrotechnik und Informationstechnik, pp. 427-436, CIGRE e-session 2020, Virtuell, France, 24/08/20. https://doi.org/10.1007/s00502-020-00846-1

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title = "Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources",

abstract = "Low-frequency currents (LFC) or quasi-DC (QDC) in the electrical power transmission network, and especially in power transformers, are causing negative effects such as an increase in noise level, in reactive power consumption and in power losses. Currently, no classification of LFC is available to identify a possible source. In order to identify the origin of undesired LFC, classifications of LFC in current and audio measurements are defined. They are based on a spectrum analysis of current and audio measurements. These classifications are successfully tested in laboratory and field measurements. Consequently, LFC sources are identified by field and laboratory measurements and analytical approaches. For power transformer operators, a user-friendly and fast method is presented to identify LFC in the transformers. The method is based on audible measurements and serves as a first estimator for low-frequency currents in power transformers.",

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author = "Dennis Albert and Philipp Schachinger and Herwig Renner and Peter Hamberger and Franz Klammler and Georg Achleitner",

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doi = "10.1007/s00502-020-00846-1",

language = "English",

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AU - Albert, Dennis

AU - Schachinger, Philipp

AU - Renner, Herwig

AU - Hamberger, Peter

AU - Klammler, Franz

AU - Achleitner, Georg

PY - 2020/12

Y1 - 2020/12

N2 - Low-frequency currents (LFC) or quasi-DC (QDC) in the electrical power transmission network, and especially in power transformers, are causing negative effects such as an increase in noise level, in reactive power consumption and in power losses. Currently, no classification of LFC is available to identify a possible source. In order to identify the origin of undesired LFC, classifications of LFC in current and audio measurements are defined. They are based on a spectrum analysis of current and audio measurements. These classifications are successfully tested in laboratory and field measurements. Consequently, LFC sources are identified by field and laboratory measurements and analytical approaches. For power transformer operators, a user-friendly and fast method is presented to identify LFC in the transformers. The method is based on audible measurements and serves as a first estimator for low-frequency currents in power transformers.

AB - Low-frequency currents (LFC) or quasi-DC (QDC) in the electrical power transmission network, and especially in power transformers, are causing negative effects such as an increase in noise level, in reactive power consumption and in power losses. Currently, no classification of LFC is available to identify a possible source. In order to identify the origin of undesired LFC, classifications of LFC in current and audio measurements are defined. They are based on a spectrum analysis of current and audio measurements. These classifications are successfully tested in laboratory and field measurements. Consequently, LFC sources are identified by field and laboratory measurements and analytical approaches. For power transformer operators, a user-friendly and fast method is presented to identify LFC in the transformers. The method is based on audible measurements and serves as a first estimator for low-frequency currents in power transformers.

KW - DC BIas

KW - GIC

KW - Geomagnetically Induced Currents

KW - transformer

KW - Transformer Audible Noise

KW - Low Frequency Currents

KW - Power Transformers

KW - quasi-DC

KW - transformer neutral point current

KW - low-frequency currents

KW - transformer noise

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SP - 427

EP - 436

BT - Cigre 2020 Session

Y2 - 24 August 2020 through 3 September 2020

ER -

Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this

Field experience of small quasi-DC bias on power transformers: A first classification of low-frequency current patterns and identification of sources

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

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