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    Transformer fault diagnosis method and system using induced ordered weighted evidence reasoning
    1.
    发明授权
    Transformer fault diagnosis method and system using induced ordered weighted evidence reasoning 有权

    公开(公告)号：US11921169B2

    公开(公告)日：2024-03-05

    申请号：US17496779

    申请日：2021-10-08

    申请人： WUHAN UNIVERSITY

    发明人： Yigang He ,  Jiajun Duan ,  Xiaoxin Wu ,  Liulu He

    IPC分类号： G01R31/62 ,  G06N7/01

    CPC分类号： G01R31/62 ,  G06N7/01

    摘要： 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.

    Deep parallel fault diagnosis method and system for dissolved gas in transformer oil
    4.
    发明授权
    Deep parallel fault diagnosis method and system for dissolved gas in transformer oil 有权

    公开(公告)号：US11656298B2

    公开(公告)日：2023-05-23

    申请号：US17160415

    申请日：2021-01-28

    申请人： WUHAN UNIVERSITY

    发明人： Yigang He ,  Xiaoxin Wu ,  Jiajun Duan ,  Yuanxin Xiong ,  Hui Zhang

    IPC分类号： G01R31/62 ,  G01N33/00 ,  G06N3/045 ,  G06N3/044

    CPC分类号： G01R31/62 ,  G01N33/0004 ,  G06N3/045 ,  G06N3/044

    摘要： The disclosure provides a deep parallel fault diagnosis method and system for dissolved gas in transformer oil, which relate to the field of power transformer fault diagnosis. The deep parallel fault diagnosis method includes: collecting monitoring information of dissolved gas in each transformer substation and performing a normalizing processing on the data; using the dissolved gas in the oil to build feature parameters as the input of the LSTM diagnosis model, and performing image processing on the data as the input of the CNN diagnosis model; building the LSTM diagnosis model and the CNN diagnosis model, respectively, and using the data set to train and verify the diagnosis models according to the proportion; and using the DS evidence theory calculation to perform a deep parallel fusion of the outputs of the softmax layers of the two deep learning models.

    Transformer failure identification and location diagnosis method based on multi-stage transfer learning
    5.
    发明授权
    Transformer failure identification and location diagnosis method based on multi-stage transfer learning 有权

    公开(公告)号：US11619682B2

    公开(公告)日：2023-04-04

    申请号：US17105616

    申请日：2020-11-26

    申请人： WUHAN UNIVERSITY

    发明人： Yigang He ,  Jiajun Duan ,  Xiaoxin Wu ,  Liulu He ,  Hui Zhang

    IPC分类号： G01R31/62 ,  G06N3/04 ,  G06N3/08 ,  G01R31/00

    摘要： 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.

    Method and system for power equipment diagnosis based on windowed feature and Hilbert visualization
    6.
    发明授权
    Method and system for power equipment diagnosis based on windowed feature and Hilbert visualization 有权

    公开(公告)号：US11520676B2

    公开(公告)日：2022-12-06

    申请号：US17161680

    申请日：2021-01-29

    申请人： WUHAN UNIVERSITY

    发明人： Yigang He ,  Xiaoxin Wu ,  Jiajun Duan ,  Xiaoyan Liu ,  Lie Li ,  Zhaorong Zeng

    IPC分类号： G06F11/00 ,  G06F11/30 ,  G06F11/07 ,  G06F11/32 ,  G06K9/62 ,  G06N3/08

    摘要： A method and a system for power equipment diagnosis based on windowed feature and Hilbert visualization are provided, which belong to the field of power equipment fault diagnosis. The method includes: obtaining an original data set of monitoring data containing power equipment fault features; introducing windowed feature calculation considering logarithmic constraints to process data to obtain a feature sequence; using Hilbert visualization method for further processing to obtain a Hilbert image data set used to train and verify a convolutional neural network; and finally directly inputting newly obtained test sample data after windowed feature calculation and Hilbert visualization processing into the trained network for fault diagnosis and location. The disclosure uses windowed feature calculation and Hilbert visualization to process the monitoring data of a power equipment to fully extract fault features and effectively improve diagnostic accuracy, and uses the convolutional neural network for diagnosis to improve the intelligence of diagnosis.