Abstract: A method and system for predicting an insulated gate bipolar transistor (IGBT) lifetime based on compound failure mode coupling are provided. First, a simultaneous failure probability model of a bonding wire and a solder layer is calculated. Next, expectancy of the simultaneous failure probability model is calculated and recorded as a lifetime under a coupling effect. A coupling function relation is established. A lifetime of the solder layer and a lifetime of the bonding wire are predicted. An IGBT lifetime prediction model not taking the coupling effect into account is established. An IGBT lifetime prediction model taking the coupling effect into account is established. In the disclosure, the lifetime of the IGBT module under the coupling effect of the solder layer and the bonding wire may be accurately predicted.

Abstract: A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

Abstract: A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Abstract: A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Abstract: A reliability evaluation method and system for a microgrid inverter IGBT based on segmented long short-term memory (LSTM) is disclosed, including steps as follows. An electrothermal coupling model is constructed to obtain real-time junction temperature data. The original LSTM algorithm is improved to obtain a segmented LSTM prediction network for the aging characteristics of the IGBT. The monitoring value of the IGBT aging parameter is used to perform segmented LSTM prediction to obtain the predicted aging process, and the threshold values of different aging stages are categorized. An aging correction is performed on the aging parameter of the electrothermal coupling model to ensure the accuracy of the junction temperature data. Rainflow-counting algorithm is used to calculate real-time thermal stress load distribution of the IGBT. The fatigue damage theory and the Lesit life prediction model are combined to calculate the real-time cumulative damage and predicted life of the IGBT.

Abstract: A power transformer fault diagnosis method based on a stacked time series network, includes: collecting gas-in-oil data of a transformer in each substation; performing z-score normalization on the collected data to obtain a normalized matrix; dividing the normalized matrix into a training set and a test set in proportion; constructing a stacked time series network based on Xgboost and a bidirectional gated neural network, and inputting the training set and the test set to perform network training; and normalizing real-time collected data to obtain trainable data to predict a fault and update network parameters. The gas-in-oil data is predicted by using Xgboost and a gated neural network, obtains prediction data of a power transformer from two time series networks by using a meta learner, and obtains a fault diagnosis result of the transformer by using a Softmax layer. The neural network has accurate fault diagnosis performance and stable robustness.

Abstract: A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.

Abstract: A wind power prediction method and system for optimizing a deep Transformer network by whale optimization algorithm are disclosed. The sequence data of wind power and related influence factors are taken as sample data which is divided into a training set and a test set, where the data is trained and predicted by a Transformer network model established according to values of the initialized hyper-parameters, and an average absolute error of wind power prediction is taken as a fitness value of each whale group. A local optimal position is determined according to the initial fitness value of individual whale group, and the current optimal position is updated by utilizing whale group optimization, and the best prediction effect is obtained by comparing the local optimal solution with the global optimal solution. An optimal hyper-parameter combination is obtained after multiple iterations of the whale optimization algorithm, and the wind power is predicted.

Abstract: A method and a system for diagnosing a fault of a three-phase three-level rectifier are relate to the technical field of fault diagnosis of power electronic equipment, and provided to implement identification and location of an open-circuit fault of a power switching device thereof. A deviation between an expected value and an actual value of a phase-to-phase voltage is adopted as a diagnosis variable. The diagnosis variable is calculated by adopting a screening technique, thereby reducing calculation error to ensure accuracy of diagnosis. Only existing voltage current signals in a control system of the rectifier are required to calculate the diagnosis variable, so no additional hardware is required and low-cost fault diagnosis can be implemented. Different voltage thresholds are adopted for different fault characteristic sections, and the voltage thresholds are updated in real time according to a direct current side voltage, which improves diagnosis speed while ensuring higher robustness.

Abstract: A method for monitoring an online state of a bonding wire of an Insulated Gate Bipolar Translator (IGBT) module comprises the following steps: Step 1, constructing a full bridge inverter circuit and an online measuring circuit and connecting two input ends of the online measuring circuit to a collecting electrode and an emitting electrode of an IGBT power module of the full bridge inverter circuit to realize a connection of the full bridge inverter circuit and the online measuring circuit; Step 2, establishing a three-dimensional data model of a healthy IGBT; Step 3, establishing a three-dimensional data model of the IGBT with a broken bonding wire; Step 4, optimizing a least squares support vector machine by adopting a genetic algorithm; and Step 5, estimating states of the three-dimensional data models obtained in the Step 2 and the Step 3 by utilizing the optimized least squares support vector machine.

Abstract: A DC/DC converter fault diagnosis method based on an improved sparrow search algorithm, includes: establishing an simulation module of the converter, selecting a leakage inductance current of a transformer as a diagnosis signal, and collecting diagnosis signal samples under OC faults of different power switching devices of the converter as a sample set; improving a global search ability of a sparrow search algorithm by using a Levy flight strategy; dividing the sample set into a training set and a test set, preliminarily establishing an architecture of a deep belief network, and initializing network parameters; optimizing a quantity of hidden-layer units of the deep belief network by using an improved sparrow search algorithm, to obtain a best quantity of hidden-layer units of the deep belief network; and training an optimized deep belief network obtained based on the improved sparrow search algorithm, and obtaining a fault diagnosis result based on a trained network.

Abstract: A DC/DC converter fault diagnosis method based on an improved sparrow search algorithm, includes: establishing an simulation module of the converter, selecting a leakage inductance current of a transformer as a diagnosis signal, and collecting diagnosis signal samples under OC faults of different power switching devices of the converter as a sample set; improving a global search ability of a sparrow search algorithm by using a Levy flight strategy; dividing the sample set into a training set and a test set, preliminarily establishing an architecture of a deep belief network, and initializing network parameters; optimizing a quantity of hidden-layer units of the deep belief network by using an improved sparrow search algorithm, to obtain a best quantity of hidden-layer units of the deep belief network; and training an optimized deep belief network obtained based on the improved sparrow search algorithm, and obtaining a fault diagnosis result based on a trained network.

Abstract: A method for diagnosing transformer fault based on a deep coupled dense convolutional neural network, includes: obtaining datasets of dissolved gas in oil of a transformer in normal and fault states; expanding the datasets by using an adaptive synthetic oversampling method; performing, in a form of a two-dimensional matrix, feature reconstruction on characteristic gas dissolved in the oil; building a transformer fault diagnosis model based on a deep coupled dense convolutional neural network; and dividing an expanded dataset into a training set and a test set, and taking the two-dimensional matrix as an input of the deep coupled dense convolutional neural network and a set label as an output to train the network to obtain a fault diagnosis model. The present disclosure can resolve a problem that a fault diagnosis accuracy rate of the transformer is low due to insufficient and unbalanced fault samples in the dissolved gas in the oil.

Abstract: The present disclosure provides certain tetrahydro-1H-cyclopenta[cd]indene compounds that are Hypoxia Inducible Factor 2? (HIF-2?) inhibitors and are therefore useful for the treatment of diseases treatable by inhibition of HIF-2?. Also provided are pharmaceutical compositions containing such compounds and processes for preparing such compounds.

Abstract: The present disclosure provides certain tetrahydro-1H-cyclopenta[cd]indene compounds that are Hypoxia Inducible Factor 2? (HIF-2?) inhibitors and are therefore useful for the treatment of diseases treatable by inhibition of HIF-2?. Also provided are pharmaceutical compositions containing such compounds and processes for preparing such compounds.

Abstract: A serial IGBT voltage equalization method and system based on an auxiliary voltage source is disclosed. The method includes the following steps. (1) Detect a port dynamic voltage of each serial IGBT. (2) Perform dynamic overvoltage diagnosis respectively on the port dynamic voltage of each IGBT. (3) Supply emergency high level signal to the gate of the IGBT when there is dynamic overvoltage. (4) Stop supplying emergency high level signal to the gate of the IGBT, supply a constant voltage at the gate of the IGBT through the auxiliary voltage source. The invention provides a constant voltage through the auxiliary voltage source, prolongs the off time of the faulty IGBT, and turns off other IGBTs simultaneously, thereby achieving the purpose of serial IGBT voltage equalization.

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: Disclosed herein are processes for preparing certain intermediates useful in the synthesis of 3-fluoro-5-(((1S,2aR)-1,3,3,4,4-pentafluoro-2a-hydroxy-2,2a,3,4-tetrahydro-1H-cyclopenta[cd]inden-7-yl)oxy)benzonitrile or a pharmaceutically acceptable salt thereof.

Abstract: The present disclosure provides certain fused tricyclic ring derivatives that are Src Homology-2 phosphatase (SHP2) inhibitors and are therefore useful for the treatment of diseases treatable by inhibition of SHP2. Also provided are pharmaceutical compositions containing such compounds and processes for preparing such compounds.