基于多源数据融合与SVM模型的高压真空断路器真空性能监测技术研究

doi:10.13385/j.cnki.vacuum.2025.05.05

摘要/Abstract

摘要： 为提升高压真空断路器真空度劣化的感知精度与早期识别能力,本文提出一种基于多源数据融合与支持向量机（SVM）模型的真空度监测技术。通过构建涵盖电气信号、机械特性与热参数的多源数据采集系统,实现对断路器运行状态的全面感知。结合归一化处理、噪声抑制及主成分分析等方法进行特征提取与信息融合,构建高维特征空间,增强真空度变化的敏感性识别。基于SVM模型完成真空度状态的智能判别与劣化趋势评估。结果表明,该方法在识别精度、泛化能力和实时响应方面均优于传统真空检测手段,能够有效结合真空技术实现非侵入式、高灵敏度的真空度监测。

关键词: 高压真空断路器, 真空度监测, 多源数据融合, SVM

Abstract: To enhance the perception accuracy and early identification capability of vacuum degradation in high-voltage vacuum circuit breakers, this paper proposes a vacuum monitoring technique based on multi-source data fusion and support vector machine (SVM) modeling. A comprehensive multi-source data acquisition system is developed, incorporating electrical signals, mechanical characteristics, and thermal parameters, to enable holistic perception of the circuit breaker’s operating status. Feature extraction and information fusion are performed using methods such as normalization, noise suppression, and principal component analysis, thereby constructing a high-dimensional feature space to improve the sensitivity to vacuum level changes. An SVM model is then employed to intelligently classify vacuum states and assess degradation trends. The results demonstrate that the proposed method outperforms traditional vacuum detection techniques in terms of recognition accuracy, generalization ability, and real-time responsiveness.

Key words: high-voltage vacuum circuit breaker, vacuum monitoring, multi-source data fusion, SVM

中图分类号: TM561.2

张婉明, 刘思宇, 战春鸣. 基于多源数据融合与SVM模型的高压真空断路器真空性能监测技术研究[J]. 真空, 2025, 62(5): 32-38.

ZHANG Wanmingi, LIU Siyu, ZHAN Chunming. Study on Vacuum Performance Monitoring of High-Voltage Vacuum Circuit Breakers Based on Multi-Source Data Fusion and Support Vector Machine (SVM) Model[J]. VACUUM, 2025, 62(5): 32-38.

参考文献

[1] 罗瑞,皮大能.基于容性能量转移变化的高压真空断路器故障智能诊断[J]. 机械设计与研究,2024,40(5):248-253.
[2] 陆旭锋,王霄峰,左昊忱.基于电容耦合法的高压断路器真空度在线监测技术研究[J]. 仪表技术,2024(4):82-86.
[3] 刘兴龙,沈佩,王光文,等.真空电弧源冷却结构对温度场的影响研究[J]. 真空,2022,59(6):29-33.
[4] 张登奎,张立岩,李志兵,等. 基于经验小波变换的真空断路器关合预击穿燃弧时间分析[J]. 电工技术学报,2024,39(增刊1):106-116.
[5] 吴敏. 基于异常电流信号的高压真空断路器故障自动诊断[J]. 自动化应用,2024,65(15):255-257.
[6] 张佳,陈志英,陈丽安,等.基于改进集合模态分解的真空断路器分合闸线圈电流特征值提取[J].高压电器,2020,56(12):116-123.
[7] 赵书涛,许文杰,李云鹏,等. 基于优选泛特征的真空断路器弹簧机构储能状态辨识方法[J]. 高电压技术,2021,47(11):3777-3784.
[8] ZHANG Z, WANG H, CHEN H, et al.A novel IEPE AE-vibration-temperature-combined intelligent sensor for defect detection of power equipment[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 9506809.
[9] GUPTA A, SAHOO R, RAO C, et al.Partial discharge source classification using random forest model[C]// 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE). Rourkela, India: IEEE, 2024.
[10] 骆斌,李金顺,孙雪,等.真空断路器状态监测及故障诊断研究[J].环境技术,2024,42(10):211-216.
[11] 刘成,戴道立,王宁,等. 在线监测中真空断路器刚合点估算方法综述[J]. 电器与能效管理技术, 2022(6):6-9.
[12] SEVILLANO J, COMBARRO L, IRIZAR A, et al.A contactless Pirani approach for vacuum monitoring in vacuum circuit breakers[J]. IEEE Sensors Journal, 2023, 23: 1592-1600.
[13] PAN Z, ZHANG Z, ZHAO P, et al.Multi-sensor signal fusion method for rolling bearing based on the standard relative mean-variance value and random weighting algorithm[J]. Measurement, 2024, 235: 114823.
[14] ZHAO C, MO B, LI D, et al.MCSSAFNet: a multi-scale state-space attention fusion network for RGBT tracking[J]. Optics Communications, 2025, 577:131394.
[15] 雷杨,杨帆,沈煜,等.基于一二次融合真空断路器的在线状态评估方法[J]. 水电能源科学, 2022,40(5):202-205.
[16] 董媛蓉. 基于多源异构数据的高速机电设备运行监测系统设计[J]. 今日制造与升级, 2023(11):78-80.
[17] FERNÁNDEZ R, DURANA G, ALDABALDETREKU G, et al. Analysis of the shaft behavior in aircraft engines using tip clearance data and custom designed laser sensors[C]// Proceedings of Optical Sensors, 2021. 2021:11772.
[18] 吕乾乾,孙振川,周建军,等.低真空管道系统性能室内实验研究[J]. 真空, 2021,58(3):7-12.
[19] 袁艳,李峰,王东.基于开关卡尔曼滤波的叶轮故障振动信号特征提取[J]. 机械制造与自动化, 2024,53(2):67-70.
[20] WANG J, GAO S, ZHAO K, et al.Qualitative identification of vacuum degree of vacuum circuit breaker by terahertz spectrum based on PSO-SVM[C]//Proceedings of the 8th International Conference on Power Energy Systems and Applications (ICoPESA), 2024:194-198.
[21] 肖鹏斌,韦云清.基于改进S变换的高压真空断路器机械故障诊断方法研究[J]. 机械研究与应用,2023,36(6):162-165.
[22] 唐轩,成俊杰,吴琳,等. 基于信号距离度模型和SVM的变压器绕组变形诊断方法研究[J]. 高压电器,2024,60(11):27-36.
[23] SONG X, WAN X, YI W, et al.BOSF-SVM: a thermal image-based fault diagnosis method of circuit boards[J]. Journal of Intelligent & Fuzzy Systems, 2022, 44: 2741-2752.
[24] 唐立,符一凡,廖敏夫.基于SVM算法的真空断路器永磁机构故障诊断方法[J]. 真空电子技术,2023(5):64-69.
[25] CAI C, YANG C, LU S, et al.Human motion pattern recognition based on the fused random forest algorithm[J]. Measurement, 2023, 222: 113540.
[26] 王元麒,胡杨刚,王磊.基于随机森林模型的真空玻璃保温性能预测[J].真空,2023,60(5):55-59.
[27] LIU C.Enhancing IC fault diagnosis with ensemble learning models: a random forest perspective[C]// 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). Hangzhou, China:IEEE, 2024: 1263-1267.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed