Research Method for Optimized Configuration of Sensors in AC High‐Voltage Circuit Breakers Based on Structural Analysis
To achieve the detectability and isolability of faults in high‐voltage circuit breakers, an optimised sensor configuration method based on structural analysis is proposed. First, the main circuit, control circuit, and mechanical operating mechanism of the circuit breaker are analysed to construct th...
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| Published in | IET science, measurement & technology Vol. 19; no. 1 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.01.2025
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| Online Access | Get full text |
| ISSN | 1751-8822 1751-8830 1751-8830 |
| DOI | 10.1049/smt2.70021 |
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| Summary: | To achieve the detectability and isolability of faults in high‐voltage circuit breakers, an optimised sensor configuration method based on structural analysis is proposed. First, the main circuit, control circuit, and mechanical operating mechanism of the circuit breaker are analysed to construct the dynamic model of each part, determine the common fault modes of each part, and define fault variables for each fault mode. Then, the fault variables are introduced into the dynamic model of the circuit breaker to form a structured model containing fault information. Next, integrate the residual analysis method based on analytical redundancy relations (ARRs) with considerations for the detectability and isolability of circuit breaker faults. By applying Dulmage–Mendelsohn (DM) decomposition to solve the structural model containing fault information, determine the optimal sensor configuration scheme for circuit breakers. Finally, the effectiveness of the optimised solution is demonstrated both theoretically and experimentally using redundancy analysis methods and experimental data. Since the structural analysis method incorporates fault detectability and isolability (FDI) for circuit breakers, it introduces a novel direction for sensor configuration design and offers more practical physical significance compared to data‐driven approaches. |
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| ISSN: | 1751-8822 1751-8830 1751-8830 |
| DOI: | 10.1049/smt2.70021 |