Abstract: 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.

Abstract: A method and a system of identifying and estimating a complex analog circuit failure, belonging to the field of power electronic circuit failure prediction. The method includes the following steps: building a degradation simulation model of an analog circuit to be diagnosed, performing a parameter aging simulation experiment on different devices; extracting a time domain feature of each of output signals by using a time-series transformation method, building a health index of each of the devices based on angle similarity; identifying whether the analog circuit to be diagnosed is degraded and a starting point of degradation by combining a time moving window and a convolutional neural network; multiplexing part of hidden layers of the convolutional neural network and a long short term memory-recurrent neural network to estimate a health state of a degraded analog circuit; and evaluating prediction accuracy.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 win

Abstract: A fault diagnosis method for a series hybrid electric vehicle AC/DC 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 neural network.

Abstract: 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 win