Mechanical Condition Detection of On-Load Tap-Changer in Converter Transformer Based on Narrowband Noise Assisted Multivariate Empirical Mode Decomposition Algorithm
Duan Ruochen, Wang Fenghua, Zhou Lidan, Yao Gang
Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai 200240 China
Abstract：In order to effectively detect the mechanical condition of on-load tap-changer (OLTC) in converter transformer, the narrowband noise assisted multivariate empirical mode decomposition (NNA-MEMD) method was proposed. This method can analyze the multichannel vibration signals during contacts switch. In detail, several narrowband noises were added to the multichannel vibration signals. Meanwhile the intrinsic mode functions (IMF) of each channel were uniformly calculated under the high dimensional hypersphere. The method will not only reduce the aliasing effect of EMD, but also improve the accurateness and reduce the complexity of signal decomposition. The power matrices of IMF based on interval maximum power feature under different conditions were also calculated to describe the mechanical characteristics. The results under normal and typical fault conditions of R type OLTC model show that, the power matrices of different conditions vary significantly, so that the matrix similarity index can measure the difference degree of vibration under typical faults. In addition, the index of contact looseness is lower than that of the insulated panel looseness. It indicates that the contact looseness fault has greater influence on switch-over process of OLTC.
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2017, Vol. 32 
