小波分析在电力电缆故障诊断中的应用

Yunbo She, Kang Shi
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引用次数: 0

摘要

如果电力电缆上的故障位置不能被迅速发现并及时排除,就会造成生活用电和工矿生产供电中断,造成经济损失。因此,本研究将小波变换与决策树、随机森林和支持向量机(SVM)相结合,提出了一种电缆故障识别方法,并在MATLAB中建立了相应的电缆故障模型。仿真结果表明,该模型能大大提高电缆故障的计算效率,能快速识别并准确定位故障,比其他传统的识别方法具有更高的精度,可广泛应用于电缆故障的在线实时准确监测与诊断。
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Application of Wavelet Analysis in Fault Diagnosis of Power Cable
If the fault location on the power cable can't be quickly found and eliminated in time, it will cause the interruption of electricity for domestic use and power supply for industrial and mining production, resulting in economic losses. Therefore, combining wavelet transform with decision tree, random forest and SVM (support vector machine), this study proposes a method to identify cable faults, and builds the corresponding cable fault model in MATLAB. The simulation results of the model show that the model can greatly improve the calculation efficiency of cable fault, can quickly identify and accurately locate the fault, and has higher accuracy than other traditional identification methods, which can be widely used in online real-time accurate monitoring and diagnosis of cable fault.
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