Transformer state evaluation method based on echo state network and deep residual neural network

Patent metadata

Field	Value
Publication number	US-12555170-B2
Application number	US-202218045468-A
Country	US
Kind code	B2
Filing date	Oct 11, 2022
Priority date	Oct 12, 2021
Publication date	Feb 17, 2026
Grant date	Feb 17, 2026

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.

First claim

What is claimed is:

1 . A transformer health state evaluation method based on a leaky-integrator echo state network and a deep residual neural network, comprising the following steps:
step 1: collecting monitoring information in each substation, comprising monitoring information of an oil test and contents of dissolved gas and furan in oil in each substation; step 2: performing data filtering, data cleaning and data normalization on the collected monitoring information to obtain an input matrix; step 3: 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; step 4: 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 step 5: performing health state evaluation and network weight update based on actual test data.

2 . The transformer health state evaluation method according to claim 1 , wherein the monitoring information collected in step 1 is specifically records of test ledgers of a transformer and an electric power company during operation, wherein each group of data comprises contents of nine key states: a breakdown voltage (BDV), water, acidity, hydrogen, methane, ethane, ethylene, acetylene, and furan, and a health state of the corresponding transformer.

3 . The transformer health state evaluation method according to claim 1 , wherein step 2 specifically comprises:
performing moving average filtering on the data collected in step 1 to eliminate noise in the data, wherein an expression of a moving average filter is as follows:

Y
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(
t
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=

1

T
w

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∫

t
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T
w

t

y
⁡
(
t
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dt

wherein Y(t) represents an output of the filter, y(t) represents an input of the filter, t represents a length of input data, and T w represents a length of a moving window, and a value of T w determines filtering performance;
then performing data cleaning, comprising error correction, duplicate deletion, specification unification, logic correction, structure conversion, data compression, incomplete/null value supplementation, and data/variable discarding to ensure data consistency; and
finally normalizing processed data by a range transformation method according to the following formula:

y
i

=

x
i

-

min
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(
x
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max
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x
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min
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(
x
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wherein max(x) represents a maximum value of sample data, min(x) represents a minimum value of the sample data, x i represents an ith piece of data in a sample, and y i represents an ith piece of normalized data, x is an integer greater than or equal to 1.

4 . The transformer health state evaluation method according to claim 1 , wherein step 3 specifically comprises:
performing model establishment and algorithm training to obtain a model of the leaky-integrator echo state network, specifically comprising: establishing the leaky-integrator echo state network, wherein a state equation is as follows:
x ( t+ 1)=(1−γ) x ( t )+γƒ( W in u ( t+ 1)+ W res x ( t )+ W back y ( t ))

wherein W in represents an input weight matrix, W res represents a weight matrix of a reservoir state, W back represents a weight matrix of an output to the reservoir state, γ represents a leakage rate, t represents time, x(t) represents a previous state of a storage pool, ƒ(⋅) represents an activation function of a neuron, u(t+1) represents an input layer, and x(t+1) represents a next state of the storage pool, x is an integer greater than or equal to 1; and an output equation of the network is as follows:
y ( t )= g ( W out [x ( n ); u ( n )])
wherein W out represents an output weight matrix of the network, and g(⋅) represents an activation function of an output layer; training the established leaky-integrator echo state network, wherein in a training process, a least-square method is used to dynamically adjust a weight of the leaky-integrator echo state network, and an L1 norm constraint is added to an objective function of the least-square method according to the following formula:

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Assignees

• Univ Wuhan

Inventors

• He Yigang
• Xing Zhikai
• Wang Xiao
• He Liulu
• Wang Chuankun

Classifications

• G01D21/02

  Measuring two or more variables by means not covered by a single other subclass · 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

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06Q50/06 Primary

  Energy or water supply · CPC title