Method for diagnosing transformer fault based on deep coupled dense convolutional neural network

Patent metadata

Field	Value
Publication number	US-12436205-B2
Application number	US-202218059965-A
Country	US
Kind code	B2
Filing date	Nov 29, 2022
Priority date	Jun 7, 2022
Publication date	Oct 7, 2025
Grant date	Oct 7, 2025

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.

First claim

What is claimed is:

1. A method for diagnosing a transformer fault based on a deep coupled dense convolutional neural network, comprising the following steps:
step 1: obtaining datasets of dissolved gas in oil of a transformer in normal and fault states, normalizing the datasets of the dissolved gas in the oil, and setting a label;
step 2: expanding the obtained datasets of the dissolved gas in the oil in step 1 by using an adaptive synthetic oversampling method, to form a new dataset;
step 3: performing, in a form of a two-dimensional matrix, feature reconstruction on characteristic gas dissolved in the oil;
step 4: building a transformer fault diagnosis model based on a deep coupled dense convolutional neural network; and
step 5: dividing the new expanded dataset in step 2 into a training set and a test set, taking the two-dimensional matrix in step 3 as an input of the deep coupled dense convolutional neural network and the set label in step 1 as an output to train the deep coupled dense convolutional neural network, and calculating an accuracy rate based on the test set to obtain a trained transformer fault diagnosis model;
wherein: the transformer fault diagnosis model in step 4 comprises a quantity of coupled dense modules; the coupled dense modules comprise a quantity of convolutional layers; the number of coupled dense modules is 1 to 4; the number of convolutional layers is 2 to 6;
the coupled dense modules are configured to train a sample and measurement accuracy rate of test sets; the convolutional layers are configured to train the dataset and measurement accuracy rate of test sets;
the transformer fault diagnosis model is configured to fuse values calculated by two previous convolutional layers in a depth direction as the input value of a next convolutional layer:
x m =F m ([ x m−2 ,x m−1 ])

wherein x m represents an input value of a network at an m th layer, namely, an output value of a network at an (m−1) th layer, and F m represents a calculation function of the m th layer;
the calculation function mainly comprises five basic calculation processes: convolution calculation, standardization, activation functions, pooling, and discarding;
a simplified formula of the convolution calculation is as follows:
y=ΣWX+b

wherein x represents an input value, w represents a weight, b represents an offset, and y represents an output;
the standardization is configured to making data conform to a standard normal distribution with an average value of 0 and a standard deviation of 1; the activation functions mainly comprise a Relu function, a Tanh function, and a softmax function; and the convolutional layer uses the relu function, a fully connected layer uses the tanh function, and an output layer uses the softmax function;

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wherein x represents an input value of the layer, and f(x) represents an output value of the layer; and
the pooling is configured to reducing an amount of characteristic data in a convolutional neural network, and mainly comprises maximum pooling and average pooling; and a discarding layer is mainly used to discard some neurons, so as to effectively prevent overfitting of the convolutional neural network.

2. The method according to claim 1 , wherein step 1 comprises:
obtaining the dataset of the dissolved gas in the oil according to the following formula:
sample i={x i,1 ,x i,2 , . . . ,x i,j ,y i }i∈[ 1 ,N]

wherein samplei represents data of the dissolved gas in the oil in an i th sample, and there are N data samples in total; x i,j represents a content of j th characteristic gas in the i th sample; and y i represents a state of the transformer in the i th sample;
performing normalization according to the following formula:

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setting the label for the state of the transformer in a form of a numerical serial number.

3. The method according to claim 1 , wherein the adaptive synthetic oversampling method in step 2 comprises:
(1) assuming that a quantity of samples to be synthesized for a minority-class sample is G, and for each minority-class sample samplei, finding K adjacent samples by using a Euclidean distance formula in n-dimensional space:

d
=

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Assignees

• Univ Wuhan
• State Grid Tianjin Electric Power Co

Inventors

• He Yigang
• Li Zihao
• Wang Jianfeng
• Liu Xiaoyu
• Ma Shiqian
• Gong Qingwu

Classifications

• G06F17/18 Primary

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0464 Primary

  Convolutional networks [CNN, ConvNet] · CPC title

• G01R31/62 Primary

  Testing of transformers · CPC title