Vibration detection method for core loosening degree of high-voltage direct current converter valve saturable reactor based on variational mode decomposition-symmetrized dot pattern and image matching

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iet Science Measurement & Technology Pub Date : 2022-07-18 DOI:10.1049/smt2.12118
Yunpeng Liu, Tingyu Lai, Jiashuo Liu, Xiaoguang Wei, Shaotong Pei, Chenghao Wang, Xiaolong Zhang
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Abstract

Saturable reactor is the key equipment in converter valves; it is important to evaluate the core condition. In this paper, a recognition model of saturable reactor core loosening based on variational mode decomposition (VMD)-symmetrized dot pattern (SDP) feature fusion images and scale-invariant feature transform (SIFT) was proposed. Firstly, the saturable reactor vibration test under high frequency pulse excitation was carried out, and the vibration signals in different core loosening degrees were collected. Secondly, the VMD algorithm was used to decompose the broadband vibration signal into multiple narrowband functions, which were used to reflect the characteristics of each frequency band. Thirdly, each function and the original signal were transformed by SDP, the generated spiral arms were fused into a new image, and the typical templates in different loosening degrees were selected. Finally, the improved SIFT algorithm was used to obtain the matching results between test sets and templates. The results show that the recognition accuracy of the proposed model for core loosening is 97.5%, which is better than traditional algorithms. It can find the core loosening defect early and avoid further failures such as water pipe break and discharge, which can provide an important basis for saturable reactor monitoring.

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基于变分模态分解-对称点图和图像匹配的高压直流换流阀饱和电抗器铁芯松动程度振动检测方法
饱和电抗器是换流阀中的关键设备;核心状态的评估是非常重要的。提出了一种基于变分模态分解(VMD)-对称点图(SDP)特征融合图像和尺度不变特征变换(SIFT)的饱和电抗器堆芯松动识别模型。首先,进行了高频脉冲激励下的饱和电抗器振动试验,采集了不同铁芯松动程度下的振动信号。其次,利用VMD算法将宽带振动信号分解为多个窄带函数,利用这些窄带函数来反映每个频段的特征;再次,对各函数和原始信号进行SDP变换,将生成的螺旋臂融合成新的图像,选择不同松动程度的典型模板;最后,使用改进的SIFT算法获得测试集与模板之间的匹配结果。结果表明,该模型对铁芯松动的识别精度为97.5%,优于传统算法。可以早期发现堆芯松动缺陷,避免水管破裂、排放等故障的发生,为饱和堆监测提供重要依据。
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来源期刊
Iet Science Measurement & Technology
Iet Science Measurement & Technology 工程技术-工程:电子与电气
CiteScore
4.30
自引率
7.10%
发文量
41
审稿时长
7.5 months
期刊介绍: IET Science, Measurement & Technology publishes papers in science, engineering and technology underpinning electronic and electrical engineering, nanotechnology and medical instrumentation.The emphasis of the journal is on theory, simulation methodologies and measurement techniques. The major themes of the journal are: - electromagnetism including electromagnetic theory, computational electromagnetics and EMC - properties and applications of dielectric, magnetic, magneto-optic, piezoelectric materials down to the nanometre scale - measurement and instrumentation including sensors, actuators, medical instrumentation, fundamentals of measurement including measurement standards, uncertainty, dissemination and calibration Applications are welcome for illustrative purposes but the novelty and originality should focus on the proposed new methods.
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