Fault Diagnosis of Active Neutral Point Clamped Three-Level Inverter Based on Energy Spectrum Entropy and Wavelet Neural Network
Li Bing1,2, Cui Jiebing1,2, He Yigang1,2, Shi Luqiang1, Liu Xiaohui1
1. College of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009 China; 2. National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei 230009 China
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.
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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2020, Vol. 35 
