Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

Rishabh Bhandia; Milos Cvetkovic; Jose Chavez Muro; Peter Palensky

doi:10.1049/cp.2018.1877

Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

Rishabh Bhandia, Milos Cvetkovic, Jose Chavez Muro, Peter Palensky

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).

Original language	English
Title of host publication	IET Conference Publications
Publisher	IET
Number of pages	6
Edition	CP759
ISBN (Electronic)	978-953-184-249-5
ISBN (Print)	9781785617911, 9781839531330
DOIs	https://doi.org/10.1049/cp.2018.1877
Publication status	Published - 2019
Event	11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion - Dubrovnik, Croatia Duration: 12 Nov 2018 → 15 Nov 2018 http://medpower2018.com/

Publication series

Name	IET Conference Publications
Number	CP759
Volume	2018

Conference

Conference	11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion
Abbreviated title	MEDPOWER 2018
Country/Territory	Croatia
City	Dubrovnik
Period	12/11/18 → 15/11/18
Internet address	http://medpower2018.com/

Bibliographical note

"Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public."

Keywords

Condition Based Maintenance
Distortion Detection
Equipment Failure
Power System Reliability
Waveform Analytics

Access to Document

10.1049/cp.2018.1877Licence: Other

Incipient_equipment_failure_assessment_and_avoidance_through_robust_detection_technqiueFinal published version, 496 KB

Cite this

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title = "Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue",

abstract = "Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).",

keywords = "Condition Based Maintenance, Distortion Detection, Equipment Failure, Power System Reliability, Waveform Analytics",

author = "Rishabh Bhandia and Milos Cvetkovic and {Chavez Muro}, Jose and Peter Palensky",

note = "{"}Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.{"} ; 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2018 ; Conference date: 12-11-2018 Through 15-11-2018",

year = "2019",

doi = "10.1049/cp.2018.1877",

language = "English",

isbn = "9781785617911",

series = "IET Conference Publications",

publisher = "IET",

number = "CP759",

booktitle = "IET Conference Publications",

edition = "CP759",

url = "http://medpower2018.com/",

}

Bhandia, R, Cvetkovic, M , Chavez Muro, J & Palensky, P 2019, Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue. in IET Conference Publications. CP759 edn, IET Conference Publications, no. CP759, vol. 2018, IET, 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, Dubrovnik, Croatia, 12/11/18. https://doi.org/10.1049/cp.2018.1877

Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue. / Bhandia, Rishabh; Cvetkovic, Milos ; Chavez Muro, Jose et al.
IET Conference Publications. CP759. ed. IET, 2019. (IET Conference Publications; Vol. 2018, No. CP759).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

AU - Bhandia, Rishabh

AU - Cvetkovic, Milos

AU - Chavez Muro, Jose

AU - Palensky, Peter

N1 - "Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public."

PY - 2019

Y1 - 2019

N2 - Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).

AB - Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).

KW - Condition Based Maintenance

KW - Distortion Detection

KW - Equipment Failure

KW - Power System Reliability

KW - Waveform Analytics

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DO - 10.1049/cp.2018.1877

M3 - Conference contribution

SN - 9781785617911

SN - 9781839531330

T3 - IET Conference Publications

BT - IET Conference Publications

PB - IET

T2 - 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion

Y2 - 12 November 2018 through 15 November 2018

ER -

Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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