LAPSE:2023.26754
Published Article

LAPSE:2023.26754
Methodology for Predictive Assessment of Failures in Power Station Electric Bays Using the Load Current Frequency Spectrum
April 3, 2023
Abstract
This paper presents a novel analysis methodology to detect degradation in electrical contacts, with the main goal of implanting a predictive maintenance procedure for sectionalizing switches, circuit breakers, and current transformers in bays of electric transmission and distribution substations. The main feature of the proposed methodology is that it will produce a predictive failure indication for the system under operation, based on the spectral analysis of the load current that is flowing through the bay’s components, using a defined relationship similar to the signal-to-noise ratio (SNR) used in data communication. A highlight of using the proposed methodology is that it is not necessary to make new investments in measurement devices, as the already-existing oscillography measurement infrastructure is enough. By implementing the diagnostic system proposed here, electrical utilities will have a modern tool for monitoring their electrical installations, supporting the implementation of new predictive maintenance functions typical of the current electrical smart grid scenario. Here, we present the preliminary results obtained by the application of the proposed technique using real data acquired from a 230 kV electrical substation, which indicate the effectiveness of the proposed diagnostic procedure.
This paper presents a novel analysis methodology to detect degradation in electrical contacts, with the main goal of implanting a predictive maintenance procedure for sectionalizing switches, circuit breakers, and current transformers in bays of electric transmission and distribution substations. The main feature of the proposed methodology is that it will produce a predictive failure indication for the system under operation, based on the spectral analysis of the load current that is flowing through the bay’s components, using a defined relationship similar to the signal-to-noise ratio (SNR) used in data communication. A highlight of using the proposed methodology is that it is not necessary to make new investments in measurement devices, as the already-existing oscillography measurement infrastructure is enough. By implementing the diagnostic system proposed here, electrical utilities will have a modern tool for monitoring their electrical installations, supporting the implementation of new predictive maintenance functions typical of the current electrical smart grid scenario. Here, we present the preliminary results obtained by the application of the proposed technique using real data acquired from a 230 kV electrical substation, which indicate the effectiveness of the proposed diagnostic procedure.
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Keywords
circuit breakers, electrical contacts, Fourier transform, predictive maintenance, sectionalizing switches, signal-to-noise ratio, spectral analysis
Subject
Suggested Citation
Bezerra FVV, Cavalcante GPS, Barros FJB, Tostes MEL, Bezerra UH. Methodology for Predictive Assessment of Failures in Power Station Electric Bays Using the Load Current Frequency Spectrum. (2023). LAPSE:2023.26754
Author Affiliations
Bezerra FVV: ELETROBRÁS ELETRONORTE—Electric Generation and Transmission Utility of North of Brazil, Brasilia 68270000, Brazil; Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil [ORCID]
Cavalcante GPS: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Barros FJB: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Tostes MEL: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil [ORCID]
Bezerra UH: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Cavalcante GPS: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Barros FJB: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Tostes MEL: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil [ORCID]
Bezerra UH: Electrical Engineering Post Graduation Course, Federal University of Para, Belém 66075-110, Brazil
Journal Name
Energies
Volume
13
Issue
19
Article Number
E5123
Year
2020
Publication Date
2020-10-01
ISSN
1996-1073
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Original Submission
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PII: en13195123, Publication Type: Journal Article
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LAPSE:2023.26754
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https://doi.org/10.3390/en13195123
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Apr 3, 2023
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