Phase-Controlled Closing Strategy for UHV Circuit Breakers with Arc-Chamber Insulation Deterioration Consideration
To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF<sub>6</sub> circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/13/3558 |
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| Summary: | To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF<sub>6</sub> circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for the breakdown voltage of mixed gases is derived based on the synergistic effect. Considering the influence of contact gap on electric field distortion, an adaptive switching strategy is designed to quantify the dynamic relationship among operation times, insulation strength degradation, and electric field distortion. Then, multi-round switching-on and switching-off tests are carried out under the condition of fixed single-arc ablation amount, and the laws of voltage–current, gas decomposition products, and pre-breakdown time are obtained. The test data are processed by the least squares method, adaptive switching algorithm, and machine learning method. The results show that the coincidence degree of the pre-breakdown time obtained by the adaptive switching algorithm and the test value reaches 90%. Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. It can also explain the physical mechanism of the decay of insulation degradation rate with the number of operations. Compared with the machine learning method, this algorithm has stronger generalization ability, effectively overcoming the defects of difficult interpretation of physical causes and the poor engineering adaptability of the black box model. |
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| ISSN: | 1996-1073 |