Deep parallel fault diagnosis method and system for dissolved gas in transformer oil

Patent metadata

Field	Value
Publication number	US-11656298-B2
Application number	US-202117160415-A
Country	US
Kind code	B2
Filing date	Jan 28, 2021
Priority date	Mar 2, 2020
Publication date	May 23, 2023
Grant date	May 23, 2023

Abstract

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.

First claim

What is claimed is:

1. A deep parallel fault diagnosis method for dissolved gas in transformer oil, comprising following steps executing by a processor:
step 1: performing an operation of a plurality of transformers of a power system of a power grid and obtaining a plurality of groups of monitoring data of dissolved gas in each transformer oil of the plurality of transformers of the power system of the power grid, analyzing a dissolved gas content in each of the groups of monitoring data, obtaining a corresponding fault type label, performing a normalizing processing on each of the groups of monitoring data, and forming a target data set by combining the normalized groups of monitoring data with the corresponding fault type label;
step 2: dividing the target data set into a first training set and a first verification set, training a long short-term memory (LSTM) diagnosis model with the first training set, and verifying the trained LSTM diagnosis model with the first verification set;
step 3: performing image processing on each group of data in the target data set to obtain an image data set, dividing the image data set into a second training set and a second verification set, training a convolutional neural network (CNN) diagnosis model with the second training set, and verifying the trained CNN diagnosis model with the second verification set;
step 4: performing a deep parallel fusion on outputs of softmax layers of the trained LSTM diagnosis model and the trained CNN diagnosis model, respectively, and outputting a final diagnosis result according to a maximum confidence principle; and
step 5: modifying the operation of the plurality of transformers of the power system of the power grid based on the final diagnosis result.

2. The method according to claim 1 , wherein the groups of monitoring data of the dissolved gas in each transformer oil are data i ={a i,1 , a i,2 , . . . , a i,j , . . . , a i,N , s i } i∈[1,K], where K is K groups of monitoring data of the dissolved gas, a i,j is a content of the j(j∈[1,N])-th gas parameter in the i-th group of monitoring data of the dissolved gas, s i is a transformer state corresponding to the i-th group of monitoring data of the dissolved gas, and N is the number of gas types, and
data obtained after normalization is data i ′={b i,1 , b i,2 , . . . , b i,j , . . . , b i,N , s i } i∈[1,K], where K is K groups of monitoring data of the dissolved gas, b i,j is a normalized value of the content of the j(j∈[1,N])-th gas parameter in the i-th group of monitoring data of the dissolved gas, s i is the transformer state corresponding to the i-th group of monitoring data of the dissolved gas, and N is the number of gas types.

3. The method according to claim 2 , wherein the LSTM diagnosis model comprises:
an input layer;
an LSTM layer;
a fully connected layer;
the softmax layer; and
a classification output layer,
wherein the LSTM layer comprises a plurality of hidden units, and a state activation function of the hidden units is tanh, and a gate activation function of the hidden units is sigmoid, and
the softmax layer takes an output of the fully connected layer in the LSTM diagnosis model as an input vector and obtains a diagnosis support degree of the LSTM diagnosis model for a fault label by

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where x 1 represents the output of the fully connected layer in the LSTM diagnosis model, θ i , i=1, 2, . . . , N is a weight matrix of the softmax layer in the LSTM diagnosis model, and Softmax is an activation function.

4. The method according to claim 3 , wherein performing image processing on each group of data in the target data set to obtain the image data set in the step 3 comprises:
performing image processing on each group of data in the target data set, presenting differences of the data in image with color to obtain an image data set A and presenting differences of the data in image with height to obtain an image data set B, respectively,
wherein the image data set A is expressed as data i ″={c i ,s i } i∈[1,K], and the image data set B is expressed as data i ″′={d i ,s i } i∈[1,K], where K is K groups of monitoring data of the dissolved gas, c i is an image of a parameter conversion of the i-th group of monitoring data of the dissolved gas in the image data set A, d i is an image of a parameter conversion of the i-th group of monitoring data of the dissolved gas in the image data set B, and s i is the transformer state corresponding to the i-th group of monitoring data of the dissolved gas.

5. The method according to claim 4 , wherein the softmax layer in the CNN diagnosis model takes an output of a fully connected layer in the CNN diagnosis model as an input vector, and obtains a diagnosis support degree for a fault label by

Assignees

• Univ Wuhan

Inventors

• He Yigang
• Wu Xiaoxin
• Duan Jiajun
• Xiong Yuanxin
• Zhang Hui

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/096

  Transfer learning · CPC title

• Y04S10/50

  Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications · CPC title