基于能量谱熵及小波神经网络的有源中性点钳位三电平逆变器故障诊断

doi:10.19595/j.cnki.1000-6753.tces.190429

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Abstract The power switching device is the core component of the inverter. The method of fault characteristic parameter extraction and classification is the technical basis for fault diagnosis and prediction, which is important for improving the reliability of the inverter. Taking the IGBT open circuit fault diagnosis of active neutral-point clamped (ANPC) three-level inverter as an example, a fault diagnosis method based on energy spectrum entropy and wavelet neural network is proposed. The upper, middle and lower arm voltages of ANPC three-level inverter are used as the measurement signals. The characteristics of bridge arm voltage signals are extracted by wavelet packet energy spectrum entropy, and the eigenvectors are optimized by kernel principal component analysis. Secondly, the adaptive moment wavelet neural network (A-WNN) is used to build a fault dictionary, Finally, the feasibility of the algorithm is verified by setting up an experimental platform. The experimental results show that A-WNN has the advantages of fast fault identification and high precision, which is suitable for real-time fault diagnosis of ANPC three-level inverter.

Key words： Three-level entropy of energy spectrum wavelet neural network fault dictionary

Received: 16 April 2019

PACS:

TM464

Fund:国家自然科学基金（51777050）、装备预先研究重点项目（41402040301）、国家自然科学基金重点项目（51637004）和国家重点研发计划“重大科学仪器设备开发”项目（2016YFF0102200）资助

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	Li Bing
	Cui Jiebing
	He Yigang
	Shi Luqiang
	Liu Xiaohui

Cite this article:

Li Bing,Cui Jiebing,He Yigang等. Fault Diagnosis of Active Neutral Point Clamped Three-Level Inverter Based on Energy Spectrum Entropy and Wavelet Neural Network[J]. Transactions of China Electrotechnical Society, 2020, 35(10): 2216-2225.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190429 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I10/2216

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