Wuhan University

Kaipei Liu

Yigang He

Hui Zhang

Liulu He

Yaru Zhang

Tao Wang

Jiajun Duan

Bolun Du

Bing Li

Yuzheng Guo

Lei Xu

Yongguang Cheng

Jie Xie

Jintao Zhou

Yuanzhe Ge

Qizhen Li

A fault diagnosis method for power electronic circuits based on optimizing a deep belief network, including steps. (1) Use RT-LAB hardware-in-the-loop simulator to set up fault experiments and collect DC-link output voltage signals in different fault types. (2) Use empirical mode decomposition to extract the intrinsic function components of the output voltage signal and its envelope spectrum and calculate various statistical features to construct the original fault feature data set. (3) Based on the feature selection method of extreme learning machine, remove the redundancy and interference features, as fault sensitive feature data set. (4) Divide the fault sensitive feature set into training samples and test samples, and primitively determine the structure of the deep belief network. (5) Use the crow search algorithm to optimize the deep belief network. (6) Obtain the fault diagnosis result.

Power electronic circuit fault diagnosis method based on optimizing deep belief network

This disclosure provides a PSR-PWM technique for high power active front-end inverters to damp a specific inter-harmonic that may cause relative sub-synchronous resonance in power system. Due to the strong interaction between wind power converters, photovoltaic converters, FACTS devices and HVDC transmission, low-frequency oscillations occur from a few Hz to dozens of Hz, or even high-frequency oscillations ranging from about 300-2000 Hz. Meanwhile, low-frequency oscillations ranging from 0.6 Hz to 7 Hz occur in the power supply systems of many electric locomotives. Even in the case of large-scale train outage, low-frequency oscillation will lead to abnormal locomotive dispatching system; in addition, the power grid voltage disturbance and flicker caused by a large number of high-power are furnaces and other nonlinear loads in the industrial field with a passband inter-harmonic frequency ranging from 0.05 Hz-90 Hz and so on are detected.

Generalized pulse width modulation technique for specific inter-harmonics control of the inverters

A fault diagnosis method for a series hybrid electric vehicle AC/DC (Alternating Current/Direct Current) converter, implementing identifying and diagnosing of an open circuit fault of a power electronic components in an AC/DC converter, and including the following steps: first, establishing a simulation model for a series hybrid electric vehicle AC/DC converter, and selecting a DC bus output current as a fault characteristic; then classifying fault types according to a quantity and locations of faulty power electronic components; next, decomposing the fault characteristic, that is, the DC bus output current by means of fast Fourier transform to different frequency bands, and selecting harmonic ratios of the different frequency bands as fault diagnosing eigenvectors; and finally, identifying the fault types by using a genetic algorithm-based BP (Back Propagation) neural network.

Fault diagnosis method for series hybrid electric vehicle AC/DC converter

A transformer winding fault diagnosis method based on wireless identification sensing includes the following steps: collecting transformer winding vibration signals in a normal state and when a fault occurs; denoising the transformer winding vibration signals by using singular entropy, and randomly dividing the denoised transformer winding vibration signals in the normal state into two groups, where one group is training data, and the other group is original measurement data; using the denoised transformer winding vibration signals obtained when the fault occurs as original threshold data; reconstructing the original threshold data to obtain reconstructed threshold data, and obtaining a transformer winding residual error threshold when the fault occurs; and reconstructing the original measurement data to obtain reconstructed measurement data, obtaining corresponding residual error data, and comparing the residual error data with the residual error threshold, to implement fault diagnosis on the transformer winding. The present invention implements remote, wireless, and unmanned fault diagnosis on a transformer winding. The diagnosis efficiency is high, the workload of a maintenance person is reduced, and the running efficiency of a transformer is increased.

Transformer winding fault diagnosis method based on wireless identification sensing

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Hubei, China

Kaipei Liu