利用窄带噪声辅助多元经验模态分解算法检测换流变压器用有载分接开关机械状态

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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.

Key words： Converter transformer on-load tap-changer multivariate empirical mode decompo- sition narrowband noise power matrix similarity

Received: 14 October 2015 Published: 26 May 2017

PACS:

TM403.4

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	Articles by authors
	Duan Ruochen
	Wang Fenghua
	Zhou Lidan
	Yao Gang

Cite this article:

Duan Ruochen,Wang Fenghua,Zhou Lidan等. Mechanical Condition Detection of On-Load Tap-Changer in Converter Transformer Based on Narrowband Noise Assisted Multivariate Empirical Mode Decomposition Algorithm[J]. Transactions of China Electrotechnical Society, 2017, 32(10): 182-189.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I10/182

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