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Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics
by
D’Antona, Gabriele
, Riva, Marco
, Trujillo-Arboleda, Camilo
, Amato, Massimiliano
in
arc voltage
/ Comparative analysis
/ data assimilation
/ DC arc model
/ DC circuit breaker
/ Design
/ Electric arc
/ Electric circuit-breakers
/ Identification and classification
/ Inspection
/ Kalman filter
/ Laboratories
/ Maintenance and repair
/ Mathematical models
/ parameter estimation
/ Parameter identification
/ Physics
2025
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Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics
by
D’Antona, Gabriele
, Riva, Marco
, Trujillo-Arboleda, Camilo
, Amato, Massimiliano
in
arc voltage
/ Comparative analysis
/ data assimilation
/ DC arc model
/ DC circuit breaker
/ Design
/ Electric arc
/ Electric circuit-breakers
/ Identification and classification
/ Inspection
/ Kalman filter
/ Laboratories
/ Maintenance and repair
/ Mathematical models
/ parameter estimation
/ Parameter identification
/ Physics
2025
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Do you wish to request the book?
Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics
by
D’Antona, Gabriele
, Riva, Marco
, Trujillo-Arboleda, Camilo
, Amato, Massimiliano
in
arc voltage
/ Comparative analysis
/ data assimilation
/ DC arc model
/ DC circuit breaker
/ Design
/ Electric arc
/ Electric circuit-breakers
/ Identification and classification
/ Inspection
/ Kalman filter
/ Laboratories
/ Maintenance and repair
/ Mathematical models
/ parameter estimation
/ Parameter identification
/ Physics
2025
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Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics
Journal Article
Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics
2025
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Overview
Accurate electrical arc modeling with physically meaningful parameters is essential for the assessment of medium-voltage DC circuit breakers in industrial and railway applications. Laboratory testing and characterization, as outlined in the IEC 61992 standard series for railway applications, typically provide data to asses the operational behavior of the componentsin the power distribution system, including recorded waveforms of terminal voltage and current but not the insights and inputs needed for inner behavior analysis and design optimization. This paper introduces lumped-parameter multi-physics models to describe different phases of arc behavior and outlines a methodology for model–data assimilation. Using experimental test data, the approach enables performance evaluation and supports non-invasive diagnostics and potential condition monitoring of circuit breakers.
Publisher
MDPI AG,MDPI
Subject
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