	US 12,436,205 B2
	Method for diagnosing transformer fault based on deep coupled dense convolutional neural network
Yigang He, Wuhan (CN); Zihao Li, Wuhan (CN); Jianfeng Wang, Wuhan (CN); Xiaoyu Liu, Wuhan (CN); Shiqian Ma, Wuhan (CN); and Qingwu Gong, Wuhan (CN)
Assigned to WUHAN UNIVERSITY, Wuhan (CN); and State Grid Tianjin Electric Power Company, Tianjin (CN)
Filed by WUHAN UNIVERSITY, Wuhan (CN); and State Grid Tianjin Electric Power Company, Tianjin (CN)
Filed on Nov. 29, 2022, as Appl. No. 18/059,965.
Prior Publication US 2023/0393219 A1, Dec. 7, 2023
Int. Cl. G01R 31/62 (2020.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06F 17/18 (2006.01); G06N 3/09 (2023.01)

CPC G01R 31/62 (2020.01) [G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06F 17/18 (2013.01); G06N 3/09 (2023.01)]

5 Claims

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_mrepresents an input value of a network at an m^thlayer, namely, an output value of a network at an (m−1)^thlayer, and F_mrepresents a calculation function of the m^thlayer;

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;

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.