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Fault Diagnosis of On-Load Tap Changer Based on Optimized Empirical Modal Decomposition Algorithm

机译:基于优化的经验模态分解算法的负载分接开关的故障诊断

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In order to effectively monitor the operation of the on-load tape-changer (OLTC) online, an optimized empirical mode decomposition algorithm based on improved mask signal is proposed to analyze the vibration signals on the surface of the switched on load changers. An improved mask signal is added to the collected original signal, which can effectively eliminate the modal aliasing in the process of Empirical Mode Decomposition. Then the maximum power characteristic matrix is obtained according to the decomposed intrinsic mode function (IMF), further monitoring condition of the on-load tap switch. The results show that the maximum power characteristic matrix of OLTC in different operating states has obvious difference. When different typical faults occur in OLTC, the interval maximum power characteristic matrix index can effectively discriminate the degree of vibration signal difference.
机译:为了有效地监测在线负载磁带变换器(OLTC)的操作,提出了一种基于改进的掩模信号的优化的经验模式分解算法,以分析在装载变换器上开关表面上的振动信号。将改进的掩模信号添加到收集的原始信号中,可以有效地消除经验模式分解过程中的模态叠种。然后根据分解的内在模式函数(IMF)获得最大功率特性矩阵,进一步监视载荷抽头开关的监控条件。结果表明,不同操作状态下OLTC的最大功率特性矩阵具有明显的差异。当在OLTC中发生不同的典型故障时,间隔最大功率特性矩阵指数可以有效地区分振动信号差的程度。

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