Abstract: The disclosure discloses a single-ended fault positioning method and system for a HVDC power transmission line based on a hybrid deep network. The method comprises the following: collecting rectification side bus output voltage and current signals of a HVDC power transmission system under different fault types, fault distances and transition resistances as an original data set; eliminating electromagnetic coupling of the bipolar direct-current line by using phase-mode transformation, extracting IMF components of fault voltage and current signals under different fault scenes by using variational mode decomposition, and calculating TEO of the IMF components to obtain a fault data set after feature engineering; normalizing the fault data set, and dividing the fault data set into a training set and a test set; and successively inputting the training set and the test set into a hybrid network of a convolutional neural network and a long short-term memory network for training and testing.

Abstract: A transformer failure diagnosis method and system based on an integrated deep belief network are provided. The disclosure relates to the fields of electronic circuit engineering and computer vision. The method includes the following: obtaining a plurality of vibration signals of transformers of various types exhibiting different failure types, retrieving a feature of each of the vibration signals, and establishing training data through the retrieved features; training a plurality of deep belief networks exhibiting different learning rates through the training data and obtaining a failure diagnosis correct rate of each of the deep belief networks; and keeping target deep belief networks corresponding to the failure diagnosis correct rates that satisfy requirements, building an integrated deep belief network through each of the target deep belief networks, and performing a failure diagnosis on the transformers through the integrated deep belief network.

Abstract: A method and a system for comprehensively evaluating reliability of a multi-chip parallel IGBT module are provided. The method includes: establishing a gate-emitter voltage reliability model of the multi-chip parallel IGBT module, performing a chip fatigue failure test, and selecting a gate-emitter voltage as a failure characteristic quantity; establishing a transconductance reliability model of the multi-chip parallel IGBT module, performing a bonding wire shedding failure test, and selecting a transmission characteristic curve of the module as a failure characteristic quantity; using a Pearson correlation coefficient to characterize a degree of health of the IGBT module, and respectively calculating degrees of health PPMCCC and PPMCCB in different degrees of chip fatigue and bonding wire shedding failure states; and comprehensively evaluating the reliability of the multi-chip parallel IGBT module according to PPMCCC and PPMCCB.

Abstract: A circuit health state prediction method and system based on an integrated deep neural network are provided and relates to a technique for predicting a power electronic circuit failure. The invention serves to identify and diagnose a health state of a simulation circuit based on historical data by using an integrated deep neural network, and the method includes: carrying out parameter aging simulation experiments for different devices; extracting a series of time domain features of output signals through a temporal transformation method, and establishing health indices of the devices based on an improved angular similarity; predicting a health state of the simulation circuit in degeneration by using CAE and LSTM-RNN; and predicting validity of the circuit health state prediction method by referring to relevant evaluation indices. The invention is capable of effectively predicting the health state of the simulation circuit and is highly accurate and easy to implement.

Abstract: A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

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: The disclosure discloses a power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification, including 1) a winding equivalent circuit is established, and a transfer function thereof is calculated; 2) a sine wave excitation source is set at one end of the power transformer winding to obtain the amplitude-frequency characteristic curve of each winding node; 3) circuits under various fault statuses are subjected to scanning frequency response analysis to extract amplitude-frequency characteristics; 4) a feature matrix is established based on the obtained amplitude-frequency characteristics; 5) scanning frequency response analysis is performed on the diagnosed power transformer to form a feature matrix; 6) the feature matrix is converted into an image, simulation and historical detection data are used as a training set, and a deep convolutional neural network is input for training; 7) diagnosed transformer is subjected to fault classification and

Abstract: A method and a system for predicting remaining useful life of an analog circuit are provided. A simulation model of the analog circuit is built, and an output voltage is selected as a degradation variable. Different degradation cycles are set to extract degradation features of the output voltage. Key features that can reflect a degradation trend of a circuit component are selected. Multi-feature fusion and similarity model are adopted to construct a health indicator curve to characterize a degradation process of a full life cycle of different circuit components. A prediction model is established based on a temporal convolutional network and an attention mechanism, and preferably selected features and a constructed health indicator database are used as an input of a TCN-attention network to predict the remaining useful life of the circuit component.

Abstract: A method and a system for comprehensively evaluating reliability of a multi-chip parallel IGBT module are provided. The method includes: establishing a gate-emitter voltage reliability model of the multi-chip parallel IGBT module, performing a chip fatigue failure test, and selecting a gate-emitter voltage as a failure characteristic quantity; establishing a transconductance reliability model of the multi-chip parallel IGBT module, performing a bonding wire shedding failure test, and selecting a transmission characteristic curve of the module as a failure characteristic quantity; using a Pearson correlation coefficient to characterize a degree of health of the IGBT module, and respectively calculating degrees of health PPMCCC and PPMCCB in different degrees of chip fatigue and bonding wire shedding failure states; and comprehensively evaluating the reliability of the multi-chip parallel IGBT module according to PPMCCC and PPMCCB.

Abstract: A method for diagnosing an open-circuit fault of a switching transistor of a single-phase half-bridge five-level inverter is provided. It includes the following steps. A semi-physical experiment platform with a DSP controller and an RT-LAB real-time simulator as its core constructed, and an output side voltage is selected as a fault signal variable. Empirical mode decomposition is used to extract a fault feature vector, and then a HHT time-frequency diagram of the fault feature vector is extracted, a voltage signal is converted into spectrum data, and time-frequency diagram fuzzy sets corresponding to different fault types are obtained. Fusion of the time-frequency diagram fuzzy sets of the same fault type is performed to obtain a fusion image that contains more fault features. The fusion images corresponding to all fault types are inputted into the deep convolutional neural network for training and testing, and a fault diagnosis result is obtained.

Abstract: The disclosure discloses a single-ended fault positioning method and system for a HVDC power transmission line based on a hybrid deep network. The method comprises the following: collecting rectification side bus output voltage and current signals of a HVDC power transmission system under different fault types, fault distances and transition resistances as an original data set; eliminating electromagnetic coupling of the bipolar direct-current line by using phase-mode transformation, extracting IMF components of fault voltage and current signals under different fault scenes by using variational mode decomposition, and calculating TEO of the IMF components to obtain a fault data set after feature engineering; normalizing the fault data set, and dividing the fault data set into a training set and a test set; and successively inputting the training set and the test set into a hybrid network of a convolutional neural network and a long short-term memory network for training and testing.

Abstract: Provided are a failure diagnosis method and apparatus for open circuit failure of a power tube of a three-phase rectifier based on a current signal, relating to a failure diagnosis technique for power electronic equipment and capable of quickly and accurately diagnosing on an open circuit failure of the power tube of the three-phase rectifier without adding a hardware component. The failure diagnosis method only requires a sampled current existing in the control system of the rectifier and some intermediate computing signals and is therefore simple and requires little computing resource. A distorted current after the open circuit failure occurs in the power tube of the rectifier and a positive/negative half cycle where the current is present when the failure occurs serve as diagnostic variables. By analyzing the sampled current, a quick diagnosis on the power tube having the open circuit failure is provided. Thus, the invention is highly applicable.

Abstract: A circuit health state prediction method and system based on an integrated deep neural network are provided and relates to a technique for predicting a power electronic circuit failure. The invention serves to identify and diagnose a health state of a simulation circuit based on historical data by using an integrated deep neural network, and the method includes: carrying out parameter aging simulation experiments for different devices; extracting a series of time domain features of output signals through a temporal transformation method, and establishing health indices of the devices based on an improved angular similarity; predicting a health state of the simulation circuit in degeneration by using CAE and LSTM-RNN; and predicting validity of the circuit health state prediction method by referring to relevant evaluation indices. The invention is capable of effectively predicting the health state of the simulation circuit and is highly accurate and easy to implement.

Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.

Abstract: Provided are a failure diagnosis method and apparatus for open circuit failure of a power tube of a three-phase rectifier based on a current signal, relating to a failure diagnosis technique for power electronic equipment and capable of quickly and accurately diagnosing on an open circuit failure of the power tube of the three-phase rectifier without adding a hardware component. The failure diagnosis method only requires a sampled current existing in the control system of the rectifier and some intermediate computing signals and is therefore simple and requires little computing resource. A distorted current after the open circuit failure occurs in the power tube of the rectifier and a positive/negative half cycle where the current is present when the failure occurs serve as diagnostic variables. By analyzing the sampled current, a quick diagnosis on the power tube having the open circuit failure is provided. Thus, the invention is highly applicable.

Abstract: The disclosure provides an adaptive inversion method of Internet-of-things environmental parameters based on an RFID multi-feature fusion sensing model, including the following steps. Space-medium-interference is proposed as an overall concept, from the multipath propagation mechanism of electromagnetic waves, the electromagnetic wave transmission mechanism is considered. Combining with the joint characteristics of the generalized time domain, frequency domain, energy domain, and spatial domain, a global signal transfer function of RFID sensing is analyzed and derived to complete extraction of RFID sensing main features.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

Abstract: The disclosure discloses an analog circuit fault feature extraction method and system based on an optimal wavelet basis function, and belongs to the field of electronic circuit engineering and computer vision, and the method comprises the steps of obtaining output signals of an analog circuit during different faults; sequentially applying wavelet transformation methods based on different wavelet basis functions to extract features of output signals; for each feature, calculating the center position of each fault, the distance from each fault data point to the center position, the farthest position of the fault data point and the average position of the fault data points; and determining an optimal wavelet basis function for analog circuit fault feature extraction according to a score discriminating method.