Patents by Inventor Liulu HE

Liulu HE has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11921169
    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.
    Type: Grant
    Filed: October 8, 2021
    Date of Patent: March 5, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Jiajun Duan, Xiaoxin Wu, Liulu He
  • Patent number: 11913854
    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.
    Type: Grant
    Filed: December 17, 2020
    Date of Patent: February 27, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Jiajun Duan, Xiaoxin Wu, Liulu He, Hui Zhang, Guolong Shi
  • Patent number: 11914936
    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.
    Type: Grant
    Filed: January 28, 2021
    Date of Patent: February 27, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Wenjie Wu, Hui Zhang, Chaolong Zhang, Liulu He
  • Patent number: 11874640
    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.
    Type: Grant
    Filed: October 8, 2021
    Date of Patent: January 16, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Lei Wang, Yingying Zhao, Ming Xiang, Lie Li, Liulu He
  • Patent number: 11874341
    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.
    Type: Grant
    Filed: July 29, 2019
    Date of Patent: January 16, 2024
    Assignee: HEFEI UNIVERSITY OF TECHNOLOGY
    Inventors: Yigang He, Kaiwei Li, Liulu He, Zhigang Li
  • Patent number: 11875500
    Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: January 16, 2024
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Liufei Shen, Liulu He, Hui Zhang, Jiajun Duan
  • Patent number: 11705899
    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.
    Type: Grant
    Filed: December 11, 2019
    Date of Patent: July 18, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Lie Li, Liulu He, Chenyuan Wang
  • Patent number: 11694011
    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.
    Type: Grant
    Filed: February 18, 2021
    Date of Patent: July 4, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Ming Xiang, Hui Zhang, Bolun Du, Liulu He
  • Patent number: 11662385
    Abstract: A method and a system of lithium battery state of charge (SOC) estimation based on second-order difference particle filtering belonging to the technical field of battery management are provided. The method includes the following steps: building a second-order RC battery model of a lithium battery; performing model parameterization by using a least squares algorithm with a forgetting factor; and generating an importance density function through a second-order central difference Kalman filtering (SCDKF) algorithm, improving a particle filtering algorithm to obtain a second-order difference particle filtering (SCDPF) algorithm, and performing SOC estimation on a lithium battery by using the SCDPF. The estimation method provided by the disclosure is accurate and has greater estimation accuracy than an unscented particle filtering algorithm (UPF), an unscented Kalman filtering algorithm (UKF), and an extended Kalman filtering algorithm (EKF). An SOC value of the lithium battery may thus be accurately estimated.
    Type: Grant
    Filed: October 25, 2021
    Date of Patent: May 30, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Yuan Chen, Zhong Li, Liulu He
  • Patent number: 11656266
    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.
    Type: Grant
    Filed: December 17, 2020
    Date of Patent: May 23, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Weibo Yuan, Guolong Shi, Liulu He, Chaolong Zhang, Bolun Du
  • Publication number: 20230152364
    Abstract: A method and a system for diagnosing an open circuit (OC) fault of an insulated gate bipolar transistor (IGBT) of a T-type three-level (T23L) inverter under multiple power factors based on instantaneous current distortion are provided. Similar characteristics of current distortion may be caused by an OC fault of a T23L inverter, making it is difficult to locate the fault. The method for diagnosing an OC fault of a grid-connected T23L inverter, can diagnose the OC fault hierarchically; four switch transistors in a phase can be divided into two groups according to the similarity analysis of current distortion under different power factors; group-based fault diagnosis is realized by half cycles in which a zero domain occurs; and then, a specific switching signal is injected to realize equipment-based OC fault diagnosis. The OC fault diagnosis of a T23L inverter is realized without additional hardware circuits.
    Type: Application
    Filed: November 14, 2022
    Publication date: May 18, 2023
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Weiwei ZHANG, Xiao WANG, Xiaoyu LIU, Liulu HE, Mingyun CHEN
  • Publication number: 20230112749
    Abstract: A transformer health state evaluation method based on a leaky-integrator echo state network includes the following steps: collecting monitoring information in each substation; performing data filtering, data cleaning and data normalization on the collected monitoring information to obtain an input matrix; inputting the input matrix into a leaky-integrator echo state network to generate trainable artificial data, and dividing the artificial data into a training set and a test set in proportion; constructing a deep residual neural network based on a squeeze-and-excitation network, and inputting the training set and the test set for network training; and performing health state evaluation and network weight update based on actual test data. Considering that a deep learning-based neural network needs a large amount of data, the present disclosure uses the leaky-integrator echo state network to generate the artificial training data.
    Type: Application
    Filed: October 11, 2022
    Publication date: April 13, 2023
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Zhikai XING, Xiao WANG, Liulu HE, Chuankun WANG
  • Patent number: 11619682
    Abstract: A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.
    Type: Grant
    Filed: November 26, 2020
    Date of Patent: April 4, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Jiajun Duan, Xiaoxin Wu, Liulu He, Hui Zhang
  • Patent number: 11586913
    Abstract: A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result.
    Type: Grant
    Filed: December 24, 2019
    Date of Patent: February 21, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Jiajun Duan, Hui Zhang, Liulu He
  • Patent number: 11581967
    Abstract: The disclosure provides a wireless channel scenario identification method and system. The method includes: simulating different wireless channel scenarios to obtain a channel scenario baseband signal y(t)pq; extracting a feature parameter of y(t)pq, extracting an autocorrelation function Ah(t)pq and performing a Fourier transform thereon to obtain a power spectral density function S(t)pq; normalizing S(t)pq to obtain a normalized channel scenario power spectral density function S(t)pq; designing a deep learning network and inputting S(t)pq and a category label pair to train the deep learning network; and for a system with a channel scenario to be identified, collecting a passband signal at its receiving end, obtaining the normalized scenario power spectral density function ?(t)pq, and using ?(t)pq as an input of the trained classifier, the output of the classifier being a label sequence of the channel scenario, and the channel scenario is effectively determined.
    Type: Grant
    Filed: February 1, 2021
    Date of Patent: February 14, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Shuguang Ning, Liulu He, Mingyun Chen
  • Patent number: 11581130
    Abstract: The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results.
    Type: Grant
    Filed: January 13, 2020
    Date of Patent: February 14, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Jiajun Duan, Liulu He
  • Patent number: 11579201
    Abstract: A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage.
    Type: Grant
    Filed: November 9, 2020
    Date of Patent: February 14, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Yuan Chen, Zhong Li, Guolong Shi, Liulu He, Chaolong Zhang
  • Patent number: 11549985
    Abstract: A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.
    Type: Grant
    Filed: November 12, 2019
    Date of Patent: January 10, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Yaru Zhang, Liulu He
  • Patent number: 11543305
    Abstract: A method for estimating the junction temperature on-line on an insulated gate bipolar transistor (IGBT) power module, including the following steps. Estimate the junction temperature by the temperature sensitive electrical parameter method, set the space thermal model of the extended state, and apply the Kalman filter to the junction temperature estimation. The temperature sensitive electrical parameter method estimates the junction temperature of the IGBT power module in real time, selects the IGBT conduction voltage drop VCE(ON) as the temperature sensitive electrical parameter, and provides a VCE(ON) on-line measuring circuit. The power loss of the diode and IGBT and the estimated value of junction temperature obtained by the temperature sensitive electrical parameter method are taken as the input of the Kalman filter, and measurement noise and process noise are considered to obtain an optimal estimated value of junction temperature.
    Type: Grant
    Filed: November 20, 2019
    Date of Patent: January 3, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Kaiwei Li, Weibo Yuan, Liulu He, Yuzheng Guo, Hui Zhang
  • Patent number: 11544917
    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.
    Type: Grant
    Filed: November 6, 2019
    Date of Patent: January 3, 2023
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Bolun Du, Yaru Zhang, Jiajun Duan, Liulu He, Kaipei Liu