基于VMD-MSVM的同步调相机载荷分配故障诊断方法
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TM342

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高等学校学科创新引智计划(111计划)资助项目(B14022)


Load distribution fault diagnosis method of synchronous condenser based on VMD-MSVM
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    摘要:

    由于特高压直流输电工程的大规模建设,其直流送受端采用无功调节的需求日益扩大,因此以同步调相机调节无功在电力系统中得到应用。为解决调相机因载荷分配不平衡引起的振动问题,文中提出一种基于变分模态分解和多核支持向量机(VMD-MSVM)的同步调相机载荷分配故障诊断方法。首先,在调相机轴承座外部布置多个测点采集振动信号;其次,针对该信号非线性且成分复杂的特性,利用VMD将其分解为多层模态函数;然后,根据能量选择反映故障特征的模态,计算其能量熵,并构成特征向量;最后,选取合适的核函数构建MSVM,将特征向量输入MSVM进行故障诊断。结果表明,文中方法适用于调相机载荷分配故障的诊断,且具有更优的故障识别效果。

    Abstract:

    Due to the large-scale construction of ultra high voltage direct current (UHVDC) transmission projects, the demand for reactive power regulation at the receiving end of UHVDC is increasing day by day, so the synchronous condensers is applied in the power system. In order to solve the vibration problem caused by unbalanced load distribution of the synchronous condensers, a fault diagnosis method based on variational mode decomposition and multiple-kernel support vector machine (VMD-MSVM) for load distribution of the synchronous condensers is proposed. Firstly, several measuring points are arranged outside the bearing seat of the synchronous condenser to collect vibration signals. Secondly, according to the characteristics of the signal nonlinear and complicated composition, the vibration signal is decomposed into multiple intrinsic mode function by using VMD modal function. Thirdly, intrinsic mode function is selectd to reflect the characteristics of failure mode, and calculate the energy entropy to constitute feature vector. Finally, the appropriate kernel function is selectd to build a multi-kernel support vector machine, and the feature vectors are input to multi-kernel support vector machine for fault diagnosis. The experimental results show that this method is suitable for the fault diagnosis of load distribution and has better fault identification effect.

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张玉良,马宏忠,蒋梦瑶.基于VMD-MSVM的同步调相机载荷分配故障诊断方法[J].电力工程技术,2022,41(1):185-191

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历史
  • 收稿日期:2021-08-06
  • 最后修改日期:2021-10-27
  • 录用日期:2021-05-16
  • 在线发布日期: 2022-01-27
  • 出版日期: 2022-01-28