基于相空间重构与GSA-LVQ的有载调压变压器分接开关机械故障诊断

赵书涛; 李小双; 李大双; 徐晓会; 李云鹏; 李波

赵书涛,李小双,李大双,徐晓会,李云鹏,李波.基于相空间重构与GSA-LVQ的有载调压变压器分接开关机械故障诊断[J].电测与仪表,2023,60(10):136-141.
Zhao Shutao,Li Xiaoshuang,Li Dashuang,Xu Xiaohui,Li Yunpeng,Li Bo.Mechanical fault diagnosis of on-load voltage regulatingtap-changer based on CEEMD-phase space reconstruction and GSA-LVQ[J].Electrical Measurement & Instrumentation,2023,60(10):136-141.

基于相空间重构与GSA-LVQ的有载调压变压器分接开关机械故障诊断

Mechanical fault diagnosis of on-load voltage regulatingtap-changer based on CEEMD-phase space reconstruction and GSA-LVQ

DOI：10.19753/j.issn1001-1390.2023.10.022

中文关键词: 有载调压变压器分接开关（OLTC）互补集合经验模态分解（CEEMD）相空间重构万有引力搜索法（GSA） LVQ神经网络振动信号机械故障诊断

英文关键词:on-load voltage-regulating transformer tap-changer(OLTC), complementary set empirical mode decomposition(CEEMD), phase space reconstruction, universal gravity search(GSA), learning vector quantization neural network(LVQ), vibration signal, mechanical fault diagnosis

基金项目:北京市自然科学基金项目资助；河北省自然科学基金项目资助

作者中文名	作者英文名	单位
赵书涛	Zhao Shutao	华北电力大学（保定）
李小双	Li Xiaoshuang	华北电力大学（保定）
李大双	Li Dashuang	华北电力大学（保定）
徐晓会	Xu Xiaohui	华北电力大学（保定）
李云鹏	Li Yunpeng	华北电力大学（保定）
李波	Li Bo	华北电力大学（保定）

摘要点击次数: 997

中文摘要:

针对有载调压变压器分接开关机械故障诊断准确率不高以及潜在机械故障不能及时被发现的问题，提出了一种基于互补集合经验模态分解（CEEMD）、相空间重构结合万有引力搜索法（GSA）改进学习矢量量化神经网络（LVQ）的有载分接开关机械故障诊断新方法。采用CEEMD对振动信号进行时频域分解，然后通过C-C算法确定延迟时间和嵌入维数，对反映不同频率特征的固有模态函数（IMF）进行相空间重构，并提取反映混沌特征的两个特征量李雅普诺夫指数和关联维数构成特征向量。利用GSA优化LVQ，解决网络对初始连接权值敏感的问题，增强网络对有载分接开关机械故障分类识别性能。通过对有载分接开关机械状态的实验分析，证明了相空间重构结合GSA-LVQ算法的可行性和有效性。

英文摘要:

Aiming at the problem that the on-load tap-changer mechanical fault diagnosis is not accurate and potential mechanical faults cannot be found in time, this paper presents a novel method of on-load tap-changer mechanical fault diagnosis based on complementary set empirical mode decomposition (CEEMD), phase space reconstruction combined with gravitational search method (GSA) improved learning vector quantization neural network (LVQ). Firstly, the CEEMD is used to decompose the vibration signal into time-frequency domain. Then, the delay time and embedding dimension are determined through the C-C algorithm. The phase space of the inherent modal function (IMF) reflecting the characteristics of different frequencies is reconstructed, and the two feature quantities Lyapunov exponent reflecting the chaotic features and correlation dimension are extracted to form the feature vector. Finally, GSA is used to optimize the LVQ to solve the problem that the network is sensitive to the initial connection weight, thus enhancing the network performance in classifying and identifying the mechanical faults of the on-load tap-changer. The experimental analysis of the mechanical state of the on-load tap-changer proves the feasibility and effectiveness of combining phase space reconstruction with GSA-LVQ algorithm.

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