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

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (19) : 106-111.

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

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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.

Key words

power transformer / winding condition / self-adaptive sifting EMD / central frequency distribution coefficient / vibration signal

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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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