周东旭;王丰华;党晓婧;张欣;刘顺桂;

• 1:上海交通大学电气工程系
• 2:深圳供电局有限公司电力科学研究院

摘要(Abstract)：

运行中的变压器声信号由其机械结构振动经空气传播形成,易受周围环境影响,且所含干扰信号具有较大的不确定性,给基于声信号的变压器状态监测带来不利影响。为有效获取反映变压器运行状态的声信号,从特高压交流变压器声信号的声学特征出发,依据声信号的集合经验模态分解(ensembled empirical mode decomposition,EEMD)结果,提出了基于稀疏表示理论的特高压交流变压器声信号盲分离方法,即通过构建K-奇异值分解(K-singularvalue decomposition,K-SVD)字典和应用正交匹配追踪算法,得到了成分相对单一的特高压交流变压器声信号,有效抑制了特高压交流变压器声信号中的干扰成分。对某1000kV变电站主变声信号的计算结果表明:基于EEMD算法和稀疏表示理论能有效地对特高压交流变压器声信号中包含的环境噪声和人说话声等干扰成分进行盲分离,具有自适应性强、计算效率高和重构误差低的优点,可为基于声信号的特高压交流变压器状态监测提供重要数据支持。

关键词(KeyWords)： 特高压变压器;EEMD算法;K-SVD字典;盲分离;声信号;稀疏表示理论

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划项目(2017YFB0902700)~~

作者(Author): 周东旭;王丰华;党晓婧;张欣;刘顺桂;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.2248

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