| 基于功率特征分段提取的隔离开关智能监测与诊断技术研究 |
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| 引用本文： | 杨涛,钱德银,雷东,刘宇飞,周涛涛,王波勇,叶子雍.基于功率特征分段提取的隔离开关智能监测与诊断技术研究[J].陕西电力,2021,0(12):52-58. |
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| 作者姓名： | 杨涛 钱德银 雷东 刘宇飞 周涛涛 王波勇 叶子雍 |
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| 作者单位： | （1.云南电网红河供电局，云南蒙自 661100；2.武汉大学电气与自动化学院，湖北武汉 430072；3.武汉黉门电工科技有限公司，湖北武汉 430072；4.三峡大学电气与新能源学院，湖北宜昌 443000） |
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| 摘 要： | 通过搭建隔离开关机电联合仿真模型并进行故障模拟结果分析，阐释了隔离开关全行程的功率-应力映射关系。根据各故障特征与行程区间的对应关系，提出了典型故障功率特征的分段提取方法。提取了运动时间及不同运动阶段输出功率均值、方差等共10个原始特征量，在仿真模拟的卡涩、三相不同期、动作不到位典型故障的诊断中进行了算法验证，采用SVM算法实现其机械状态的智能监测与故障诊断。最终通过GW4型隔离开关故障模拟实验，验证了所提智能算法在工业实际中的有效性与准确性。
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| 关 键 词： | 隔离开关 机电联合仿真 缺陷诊断 支持向量机 |
Intelligent Monitoring and Diagnosis Technology of Disconnector Based on Power Features Segment Extraction |
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| YANG Tao,QIAN Deyin,LEI Dong,LIU Yufei,ZHOU Taotao,WANG Boyong,YE Ziyong.Intelligent Monitoring and Diagnosis Technology of Disconnector Based on Power Features Segment Extraction[J].Shanxi Electric Power,2021,0(12):52-58. |
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| Authors: | YANG Tao QIAN Deyin LEI Dong LIU Yufei ZHOU Taotao WANG Boyong YE Ziyong |
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| Affiliation: | （1. Honghe Power Supply Bureau of Yunnan Power Grid Co.， Ltd.， Honghe Hani and Yi Autonomous Prefecture 661100， China； 2. School of Electrical Engineering and Automation， Wuhan University， Wuhan 430072， China； 3. Wuhan Hongmen Electrical Technology Co.， Ltd.， Wuhan 430072， China； 4. College of Electrical and New Energy， Three Gorges University， Yichang 443000， China） |
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| Abstract: | By establishing the electromechanical co-simulation model of disconnector and analyzing the fault simulation results， this paper expounds the mapping relationship between the power and stress of the whole stroke of the disconnector， and proposes the segment extraction method of typical fault power characteristics according to the corresponding relationship between the fault characteristics and stroke interval. A total of 10 original characteristic quantities are extracted such as motion time， the mean value and variance of output power in different motion stages. The algorithm is verified in the diagnosis of the typical faults such as jamming， three-phase un-synchronization and inadequate action in the simulation. The SVM algorithm is used to realize the intelligent monitoring and fault diagnosis of the mechanical state of the disconnector. In the end， through the fault simulation experiment of GW4 disconnector， the effectiveness and accuracy of the proposed intelligent algorithm in industrial practice is verified. |
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| Keywords: | disconnector electromechanical co-simulation defect diagnosis support vector machine |
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