基于IIST与SVM的串联故障电弧诊断方法研究
CSTR:
作者:
作者单位:

(1.厦门理工学院电气工程与自动化学院,福建 厦门 361024;2.厦门市高端电力装备及智能控制重点实验室, 福建 厦门 361024)

通讯作者:

陈丽安(1966—),女,博士,教授,主要从事电器智能化技术及应用的研究;E?mail:chenla@xmut.edu.com

中图分类号:

TM501.2

基金项目:

福建省自然科学基金(2022J011259,2023J011443)


Research on arc diagnosis method of series faults based on IIST and SVM
Author:
Affiliation:

(1.School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China;2.Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China)

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    摘要:

    针对S变换的时频分辨能力不足导致串联故障电弧特征难以准确提取的弊端,以中心频率测度为标准筛选低频和高频段的主要频率点,分别引入低频和高频段对应的高斯窗口系数,形成一种改进的不完全S变换(improvement incomplete S?transform,IIST)时频分析方法。首先,依据标准搭建串联电弧故障试验采集平台,采集不同负载情况下的电流信号;其次,采用IIST对信号进行时频分析并提取低频和高频段的对应特征量,形成特征向量样本集;最后,在此基础上构建故障电弧诊断模型,对样本集进行分类识别。结果表明,该特征提取方法在支持向量机(support vector machine,SVM)中识别准确率达到98.29%,能有效地提取电流故障特征,通过增设对照实验,探究不同特征提取方法、不同核函数的SVM对诊断结果的影响,进一步验证了IIST与SVM故障诊断方法是有效的。

    Abstract:

    Due to the low time?frequency resolution of S?transform (ST), it is difficult to accurately extract the characteristics of series fault arcs. In this paper, an improved incomplete S?transform (IIST) time?frequency analysis method was developed, where the center frequency measure is selected as the standard to screen the main frequency points in low and high frequency bands, and the Gaussian window coefficients corresponding to such low and high frequency bands is introduced, respectively. Firstly, a standard series arc fault test acquisition platform was built to collect current signals under different loads. Secondly, IIST was used for time?frequency analysis of the signals, and the corresponding features of low and high frequency bands were extracted to form the feature vector sample set. Finally, a fault arc diagnosis model was constructed to classify and identify the sample set. The results show that the recognition accuracy of this feature extraction method in support vector machine (SVM) is up to 98.29%, validating that the current fault features can be extracted effectively. By adding comparison experiments, the influence of different feature extraction methods and the SVMs with different kernel functions on diagnosis results was explored, which further verified the effectiveness of IIST and SVM fault diagnosis methods.

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江永鑫,陈丽安.基于IIST与SVM的串联故障电弧诊断方法研究[J].电力科学与技术学报,2023,38(5):159-168.
JIANG Yongxin, CHEN Li’an. Research on arc diagnosis method of series faults based on IIST and SVM[J]. Journal of Electric Power Science and Technology,2023,38(5):159-168.

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