| 引用本文: | 尚海昆,李宇才,林 伟.基于EWT-MQE的变压器局部放电特征提取[J].电力系统保护与控制,2022,50(14):161-171.[点击复制] |
| SHANG Haikun,LI Yucai,LIN Wei.Partial discharge feature extraction of a transformer based on EWT-MQE[J].Power System Protection and Control,2022,50(14):161-171[点击复制] |
|
| 摘要: |
| 为了有效提取局部放电信号故障特征，进而对电力变压器故障进行诊断，提出一种基于经验小波变换(Empirical Wavelet Transform, EWT)和多尺度量子熵(Multiscale Quantum Entropy, MQE)的变压器局部放电特征提取方法。首先，该方法利用EWT对局部放电信号进行分解，得到多个不同的固有模态分量(Intrinsic Mode Function, IMF)和残余分量。其次，计算信号分解出的每个IMF的多尺度量子熵序列。然后，对多尺度量子熵序列利用局部切空间排列算法(Local Tangent Space Arrangement, LTSA)进行降维处理。最后，采用层次聚类算法(Hierarchical Agglomerative Clustering, HAC)进行聚类分析，得到不同放电类型的识别结果。通过与不同诊断方法对比，仿真结果及实验数据验证了所提方法的有效性和优越性。 |
| 关键词: 经验小波变换 多尺度量子熵 局部放电 变压器 层次聚类 |
| DOI：DOI: 10.19783/j.cnki.pspc.211255 |
| 投稿时间：2021-09-11修订日期：2022-01-18 |
| 基金项目:国家自然科学基金项目资助(SGLNDK00KJJS15 00008)，东北电力大学博士科研基金项目资助(BSJXM201406)
此局部放电故障诊断对于保障设备绝缘安全具有重要现实意义[8-11]。 |
|
| Partial discharge feature extraction of a transformer based on EWT-MQE |
| SHANG Haikun,LI Yucai,LIN Wei |
| (Key Laboratory of Modern Power System Simulation & Control and Renewable Energy Technology,
Ministry of Education, Northeast Electric Power University, Jilin 132012, China) |
| Abstract: |
| To effectively extract the partial discharge fault feature and diagnose the fault of a power transformer, this paper presents a method based on empirical wavelet transform (EWT) and multiscale quantum entropy (MQE) to diagnose transformer faults. First, EWT is employed for partial discharge signal decomposition to get different IMF components and a residual. Secondly, the MQE sequence of each IMF is generated by signal decomposition. Then the dimension of the MQE sequence is reduced with the local tangent space arrangement algorithm (LTSA). Finally, a hierarchical clustering algorithm (HAC) is used for clustering analysis to get the recognition results of different types of discharge. Compared with different diagnostic methods, the effectiveness and superiority of the proposed method is verified by the simulation result and experimental data.
This work is supported by the National Natural Science Foundation of China (No. SGLNDK00KJJS1500008). |
| Key words: empirical wavelet transformation multi-scale quantum entropy partial discharge transformer hierarchical clustering |
