[1]高树国,岳国良,周 聪,等.应用改进Hilbert-Huang变换下的Volterra模型诊断OLTC机械故障[J].高压电器,2020,56(07):173-180.[doi:10.13296/j.1001-1609.hva.2020.07.025]
 GAO Shuguo,YUE Guoliang,ZHOU Cong,et al.Applying Hilbert-Huang Transform of the Volterra Model to Diagnosing Mechanical Fault for On-load Tap Changer[J].High Voltage Apparatus,2020,56(07):173-180.[doi:10.13296/j.1001-1609.hva.2020.07.025]
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应用改进Hilbert-Huang变换下的Volterra模型诊断OLTC机械故障()
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《高压电器》[ISSN:1001-1609/CN:61-11271/TM]

卷:
第56卷
期数:
2020年07期
页码:
173-180
栏目:
研究与分析
出版日期:
2020-07-20

文章信息/Info

Title:
Applying Hilbert-Huang Transform of the Volterra Model to Diagnosing Mechanical Fault for On-load Tap Changer
作者:
高树国1 岳国良2 周 聪3 耿江海4 邢 超1 丁 钰5 孟立会2
(1. 国网河北省电力有限公司电力科学研究院, 石家庄 050021; 2. 国网河北省电力有限公司, 石家庄 050000; 3. 国网天津市电力公司, 天津 300010; 4. 华北电力大学河北省输变电设备安全防御重点实验室, 河北 保定 071003; 5. 中国电力工程顾问集团华北电力设计院有限公司, 北京 100120)
Author(s):
GAO Shuguo1 YUE Guoliang2 ZHOU Cong3 GENG Jianghai4 XING Chao1 DING Yu5 MENG Lihui2
(1. State Grid Hebei Electric Power Research Institute,Shijiazhuang 050021, China; 2. State Grid Hebei Electric Power Supply Company Limited,Shijiazhuang 050000, China; 3. State Grid Tianjin Electric Power Company, Tianjin 300010, China; 4. Hebei Provinci
关键词:
振动检测 Hilbert-Huang变换 Volterra模型 信号分解 特征提取
Keywords:
vibration detection Hilbert-Huang transform Volterra model signal decomposition feature extraction
DOI:
10.13296/j.1001-1609.hva.2020.07.025
摘要:
为提高有载分接开关(OLTC)机械故障诊断的自适应性、特征分辨率以及识别效率,提出一种包含聚合经验模态分解(EEMD)分解和Hilbert边际谱分析的改进HHT方法,与混沌时间序列的Volterra模型相结合来提取OLTC的机械故障特征。具体应用时,首先对OLTC切换过程中的多通道振动信号进行EEMD分解得到固有模态函数(IMF)分量,然后应用Hilbert谱分析法求取各IMF的Hilbert边际谱。进一步,应用Volterra模型根据Hilbert边际谱构建Volterra特征矩阵,以矩阵奇异值为故障特征参量。最后搭建了OLTC典型机械故障真型实验平台,采用文中方法获取并分析了几种典型机械故障的振动信号,并借助多分类支持向量机对数据集进行分类识别,验证了所提出故障诊断方法的有效性。与其他方法对比得知,新方法取得了较高的故障识别准确率。
Abstract:
To improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer (OLTC) of a transformer, an improved Hilbert-Huang transform containing the Ensemble Empirical Mode Decomposition (EEMD) and the Hilbert marginal spectrum analysis was proposed, combined with the Volterra model of chaotic time series to mechanical fault feature extraction OLTC. In detail, the EEMD is applied to decompose the multi-channel vibration signal in the OLTC switching process to obtain the intrinsic mode function (IMF) component, and then the Hilbert spectral spectrum of each IMF is obtained by Hilbert spectrum analysis. Furthermore, the Volterra feature matrix is constructed based on the Hilbert marginal spectrum by using the Volterra model, and the matrix singular values are used as the fault characteristic parameters. Finally, the test platform for simulating mechanical faults in the OLTC was built. On this basis, the vibration signals generated due to typical mechanical faults were acquired and analyzed. And multi-classification SVM was established to identify data sets, thus validating the effectiveness of the method. Compared with other methods, the new method achieved a higher fault recognition accuracy.

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备注/Memo

备注/Memo:
收稿日期:2020-01-18; 修回日期:2020-03-15 基金项目:国家电网公司科技项目(GY71-18-001)。 Project Supported by Science and Technology of SGCC(GY71-18-001).高树国(1982—),男,硕士,教授级高级工程师,主要从事电气设备故障诊断与状态监测等方面的研究工作。
更新日期/Last Update: 2020-07-25