基于三维熵距和熵空间的航空电弧故障检测与分类技术

doi:10.19595/j.cnki.1000-6753.tces.191717

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Abstract In view of the limitations of common time-frequency domain analysis methods in aviation arc fault detection and to avoid the contingency of single feature, this paper put forward the method of using three dimensional entropy to achieve the feature fusion of three kinds of information entropy. PCA fault detection technology was used to realize dimensionality reduction of the feature matrix for reducing the noise and redundancy of feature fusion. The characteristics of wavelet energy entropy, power spectrum entropy, sample entropy and the three dimensional entropy distance of three kinds of information entropy were analyzed respectively before and after arc faults. The monitoring statistics ${{T}^{2}}$, SPE and their respective control limits under each test load were acquired by the PCA fault detection technology. The detection of aviation AC arc faults was realized by comparing the values of ${{T}^{2}}$ and SPE before and after arc faults with their respective control limits. The analysis results show that this method combines the advantages of each characteristic quantity and does not need to set the threshold manually, which can identify the occurrence of arc faults reliably and accurately. Finally, by the three-dimensional entropy space, the classification methods of several typical arc fault types are given. The proposed method provides a reference for realizing the arc fault detection and identifying the type of arc fault.

Key words： Aviation arc fault three-dimensional entropy distance feature fusion PCA detection technology entropy space fault classification

Received: 06 December 2019

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TM501

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	Cui Ruihua
	Li Ze
	Tong Deshuan

Cite this article:

Cui Ruihua,Li Ze,Tong Deshuan. Arc Fault Detection and Classification Based on Three-Dimensional Entropy Distance and Entropy Space in Aviation Power System[J]. Transactions of China Electrotechnical Society, 2021, 36(4): 869-880.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191717 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I4/869

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