基于自适应筛选EMD和CFDC的变压器绕组状态检测

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (19) : 106-111.

论文

杨毅1，王丰华2，段若晨2，杜胜磊1，刘石1，杨贤1

作者信息 +

Detection of Transformer Winding Condition Based on the Self-adaptive Sifting EMD and CFDC

YANG Yi1，WANG Fenghua2，DUAN Ruochen2, DU Shenglei1，LIU Shi1，YANG Xian1

Author information +

文章历史 +

摘要

为更加准确地检测变压器的绕组状态，本文提出了自适应筛选EMD算法来对变压器突发短路下的非平稳和强时变振动信号进行分解，进而根据得到的Hilbert边际谱定义了中心频率分布系数（central frequency distribution coefficient，CFDC）来对变压器绕组状态进行检测。仿真分析和某大型变压器实测振动信号的计算结果表明，增加了自适应筛选因子的改进EMD算法能够有效地抑制模态混叠现象，提高了振动信号分解的准确性，所定义的CFDC及其变化可以清晰地反映出变压器绕组状态改变的演变过程，便于及时有效检测绕组状态，确保变压器的安全可靠运行。

Abstract

To detect the mechanical condition of transformer winding more accurately, the self-adaptive sifting EMD (SASEMD) is proposed to analyze the unstable and time-varying vibration signals of power transformer under sudden short-circuit. The central frequency distribution coefficient (CFDC) based on obtained Hilbert marginal spectrum is defined to detect the winding condition. According to the simulation analysis and the calculated results of the measured vibration signals of some large-scaled transformer, it is seen that the improved EMD with self-adaptive sifting factor can effectively restrain the aliasing effect and improve the accurateness of signal decomposition. The defined CFDC can clearly reflect the variation degree of winding deformation, which is helpful to effectively detection the winding condition for the secure and reliable operation of power transformer.

导出引用

杨毅1，王丰华2，段若晨2，杜胜磊1，刘石1，杨贤1. 基于自适应筛选EMD和CFDC的变压器绕组状态检测[J]. 振动与冲击, 2017, 36(19): 106-111

YANG Yi1，WANG Fenghua2，DUAN Ruochen2, DU Shenglei1，LIU Shi1，YANG Xian1. Detection of Transformer Winding Condition Based on the Self-adaptive Sifting EMD and CFDC[J]. Journal of Vibration and Shock, 2017, 36(19): 106-111

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