Transformer fault diagnosis method, device and system and storage medium

Abstract

The invention discloses a transformer fault diagnosis method, device and system and a storage medium, and relates to the technical field of electric digital data processing. The method comprises the following steps: acquiring vibration signals of a transformer iron core and a winding; processing the vibration signal through a Grubrum angle field to obtain a feature image; processing the feature image through a multi-attention mechanism to obtain a global feature matrix; performing feature node cascading on the global feature matrix based on a width learning system, and constructing a CFBLS model; and inputting the global feature matrix into a CFBLS model, constructing a CBAM-CFBLS transformer fault diagnosis model, and carrying out transformer fault diagnosis through the trained CBAM-CFBLS transformer fault diagnosis model. According to the transformer fault diagnosis method provided by the embodiment of the invention, the model is small, the real-time performance is high, the diagnosis precision is high, the signal processing efficiency and precision are improved, and the feature representation capability is enhanced.

Description

Technical Field

[0001] The present application belongs to the technical field of electrical digital data processing, and in particular, relates to a transformer fault diagnosis method, device, system and storage medium. Background Art

[0002] As one of the important power transmission and transformation equipment, power transformers bear the important responsibility of power conversion. Ensuring the normal operation of transformers is of great significance to the reliable operation of the entire power system. With the long-term operation of transformers and environmental impacts, there will be potential mechanical failures inside the transformers, among which the windings and cores are the structures with the highest failure frequency. If not handled in time, it will cause power outages, fires and other accidents.

[0003] Traditional power transformer fault diagnosis usually relies on manual experience detection and certain fixed reference rules, which has the problems of low diagnostic efficiency, low diagnostic accuracy and waste of a lot of human and material resources. With the rapid development of deep learning technology, it has been widely used in power transformer fault diagnosis, effectively solving the above problems. However, deep neural networks require a deep network structure to ensure their detection accuracy, which leads to a large network model and inconvenient deployment. In addition, as the data continues to increase, the deep learning model needs to be re-learned and trained, resulting in a long network detection time, which cannot meet the real-time monitoring requirements of power transformers.

[0004] Therefore, there is an urgent need for a method that can diagnose transformer faults in real time and accurately. Summary of the invention

[0005] The purpose of this application is to provide a transformer fault diagnosis method, device, system and storage medium, which realize transformer fault diagnosis based on Gram angle field and CBAM-CFBLS model, and have the advantages of small model, high real-time performance and high diagnostic accuracy.

[0006] In order to achieve the above objectives, the solution of this application is:

[0007] In a first aspect, an embodiment of the present application provides a transformer fault diagnosis method, comprising:

[0008] Obtain vibration signals of transformer core and winding;

[0009] The vibration signal is processed by using the Gram angle field to obtain a characteristic image, wherein the characteristic image includes a Gram angle sum field characteristic image and a Gram angle difference field characteristic image;

[0010] The Gram angle sum field feature image and the Gram angle difference field feature image are processed through a multi-attention mechanism to obtain a global feature matrix;

[0011] Based on the width learning system, the feature nodes of the global feature matrix are cascaded to build the CFBLS model;

[0012] A data set is constructed based on feature images, and the data set is divided into a training set and a test set. The global feature matrix is ​​input into the CFBLS model, and the parameters of the CFBLS model are adjusted through the training set to construct a CBAM-CFBLS transformer fault diagnosis model. The CBAM-CFBLS transformer fault diagnosis model is tested through the test set until the fault diagnosis accuracy reaches a preset threshold.

[0013] Transformer fault diagnosis is performed using the trained CBAM-CFBLS transformer fault diagnosis model.

[0014] According to the above method of the embodiment of the present application, the following additional technical features may also be provided:

[0015] Furthermore, the vibration signal is processed by the Gram angle field to obtain a characteristic image, including:

[0016] By the sequence X=(X 1 ,X 2 ,...,X n ) represents the vibration signal. The vibration signal is normalized and the sequence X is normalized to the interval [0,1] to obtain the normalized value. The formula is as follows:

[0017]

[0018] Where i = 1, 2, ..., n, x i represents the i-th group of vibration signals, max(X) represents the minimum value in the vibration signal, and min(X) represents the minimum value in the vibration signal.

[0019] Furthermore, the method includes mapping the sequence X into a time series in polar coordinates, and the formula is as follows:

[0020]

[0021] Among them, t i represents the time node, N represents the regularization constant factor in polar coordinates, θ i represents the level angle of point i, r represents the polar coordinate radius, Represents a normalized time series.

[0022] Furthermore, the method includes removing noise from the vibration signal through two inner products with penalty terms, as shown below:

[0023]

[0024]

[0025] Here, <·> represents the inner product operation.

[0026] Furthermore, the method includes generating a Gram angle and field two-dimensional feature image by calculating the cosine function of the sum of each two level angles and summing them up, and the formula is as follows:

[0027]

[0028] By calculating the sine function of the difference between every two level angles and summing them up, a two-dimensional characteristic image of the Gram angle difference field is generated. The formula is as follows:

[0029]

[0030] Where I represents the unit row vector, represents the normalized time series, express The transpose of .

[0031] Furthermore, the Gram angle sum field feature image and the Gram angle difference field feature image are processed through a multi-attention mechanism to obtain a global feature matrix, including:

[0032] The Gram angle sum field feature image and the Gram angle difference field feature image are used as the input of the multi-attention mechanism. The multi-attention mechanism includes the channel attention mechanism and the spatial attention mechanism. The formula of the channel attention mechanism is as follows:

[0033]

[0034] Where i = 1, 2, ..., n, Z i ' represents the features of the i-th group of feature maps output by the channel attention mechanism, M c represents the attention weight in the channel dimension, It represents the feature map obtained after GAF transformation of the input i-th group of vibration signal samples. represents element-wise multiplication;

[0035] The features output by the channel attention mechanism are used as the input of the spatial attention mechanism. The formula is as follows:

[0036]

[0037] Among them, M s Represents the attention weight in the spatial dimension; Z i " represents the features of the i-th group of feature maps output by the spatial attention mechanism;

[0038] The formula of the global feature matrix is ​​as follows:

[0039]

[0040] Furthermore, based on the width learning system, the feature nodes of the global feature matrix are cascaded to construct the CFBLS model, including:

[0041] The global feature matrix is ​​input into the width learning system. The width learning system converts the global feature matrix into n groups of random feature nodes through linear mapping, and uses the output of the previous group of feature nodes as the input of the next group of feature nodes to obtain the i-th group of feature nodes of the CFBLS model. The formula is as follows:

[0042]

[0043] Where i = 1, 2, ..., n, represents the feature layer activation function, W ei and β ei Respectively represent the weight and bias of the randomly generated i-th feature node;

[0044] Each group of feature nodes of the CFBLS model is connected in series to obtain a complete feature mapping layer Z p , the formula is as follows:

[0045] Z p =[Z 1 ,Z 2 ,...,Z p ]

[0046] For the complete feature map node Z p The enhanced nodes are obtained by performing nonlinear transformation. The formula is as follows:

[0047] H j =ξ(ZW hk +β hk )

[0048] Where j = 1, 2, ..., q, ξ(·) represents the linear activation function of the enhancement node, W hk and β hk They represent the weight and bias of the randomly generated k-th enhancement node respectively;

[0049] Each enhancement node is connected in series to obtain a complete enhancement layer H q , the formula is as follows:

[0050] H q =[H 1 H 2 ,...,H q ]

[0051] The feature map layer Zp and enhancement layer H q After being connected in series to the output layer, a complete CFBLS model is constructed. The formula of the output matrix is ​​as follows:

[0052] Y=[Z p |H q ]W=AW

[0053] Where A=[Z p |H q ], represents the hidden layer of all feature mapping layers and enhancement layers, and W represents the weight connecting the hidden layer and the output layer;

[0054] The objective function for calculating the optimal output weight is as follows:

[0055]

[0056] Among them, λ represents the regularization parameter;

[0057] The optimal output weight W is calculated by the Tikhonov regularization method best , the formula is as follows:

[0058] W best =A + Y=(λI+A T A) -1 A T Y

[0059] Among them, A + represents the pseudo-inverse matrix of A, and I represents the identity matrix;

[0060] When λ→0, the pseudo-inverse matrix A + The formula is as follows:

[0061]

[0062] In a second aspect, an embodiment of the present application provides a transformer fault diagnosis device, comprising:

[0063] A signal acquisition module is configured to acquire vibration signals of the transformer core and windings;

[0064] A feature image module is configured to process the vibration signal through a Gram angle field to obtain a feature image, wherein the feature image includes a Gram angle sum field feature image and a Gram angle difference field feature image;

[0065] A feature matrix module is configured to process the Gram angle sum field feature image and the Gram angle difference field feature image through a multi-attention mechanism to obtain a global feature matrix;

[0066] A model building module is configured to perform feature node cascading on a global feature matrix based on a width learning system to build a CFBLS model;

[0067] The model training module is configured to construct a data set based on the feature image, and divide the data set into a training set and a test set; input the global feature matrix into the CFBLS model, adjust the parameters of the CFBLS model through the training set, construct a CBAM-CFBLS transformer fault diagnosis model, and test the CBAM-CFBLS transformer fault diagnosis model through the test set until the fault diagnosis accuracy reaches a preset threshold;

[0068] The fault diagnosis module is configured to perform transformer fault diagnosis through the trained CBAM-CFBLS transformer fault diagnosis model.

[0069] In a third aspect, an embodiment of the present application provides a transformer fault diagnosis system, the system including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the transformer fault diagnosis method provided in the first aspect of the embodiment of the present application.

[0070] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the transformer fault diagnosis method as described in the first aspect of the embodiment of the present application.

[0071] Compared with the prior art, the transformer fault diagnosis method provided by the embodiment of the present application has the following beneficial technical effects:

[0072] The embodiment of the present application realizes the conversion from the original vibration signal to the two-dimensional feature image through the application of Gram Angular Field (GAF) technology, which significantly improves the efficiency and accuracy of signal processing. The GASF and GADF feature images not only intuitively show the dynamic changes of the signal, but also remove the redundant information and noise in the time domain through the conversion of the angle domain, thereby extracting purer and more effective features.

[0073] The embodiment of the present application introduces a multi-attention mechanism to automatically identify and strengthen key feature areas and channels, significantly enhancing the feature representation capability, so that the model can more accurately capture subtle changes in vibration signals that are closely related to faults, and maintain high sensitivity and accuracy even under complex and changeable working conditions.

[0074] The embodiment of the present application constructs a CFBLS model by improving the connection mode of BLS feature nodes, thereby reducing the size of the model. Through linear mapping and cascading of random feature nodes, CFBLS can construct feature mapping layers and enhancement layers with rich representation capabilities in a short time. At the same time, the application of ridge regression method and Tikhonov regularization further ensures the stability and generalization ability of model training. This efficient model training and optimization process enables the method to quickly adapt to different data sets and diagnostic tasks.

[0075] The embodiment of the present application combines a variety of advanced technologies to form a comprehensive and powerful transformer fault diagnosis solution. Its generalization ability is not only reflected in the accurate identification of different fault types, but also in its adaptability to different working conditions and complex environments. Whether it is for old equipment or new transformers, this method can provide reliable and accurate fault diagnosis results. In addition, this method also has a wide range of applications and can be extended to fault diagnosis of other types of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic diagram showing a flow chart of a transformer fault diagnosis method according to an embodiment of the present application is shown;

[0077] Figure 2 A structural block diagram of a transformer fault diagnosis device according to an embodiment of the present application is shown;

[0078] Figure 3 A structural block diagram of a computer device according to an embodiment of the present application is shown;

[0079] Figure 4 The transformer normal signal GAF conversion process of the embodiment of the present application is shown;

[0080] Figure 5 The core loose vibration signal GAF conversion process of the embodiment of the present application is shown;

[0081] Figure 6 The winding stacking vibration signal GAF transformation process of the embodiment of the present application is shown;

[0082] Figure 7 The winding deformation vibration signal GAF transformation process of the embodiment of the present application is shown;

[0083] Figure 8 The winding loose vibration signal GAF conversion process of the embodiment of the present application is shown;

[0084] Fig. 9 The vibration signal GAF transformation process of the embodiment of the present application when the core loosens and the winding overlaps occurs simultaneously is shown;

[0085] Fig.10The vibration signal GAF transformation process of the embodiment of the present application when the core loosens and the winding deforms simultaneously occurs;

[0086] Fig.11 The CBAM-CFBLS model structure of the embodiment of the present application is given;

[0087] Fig.12 The following is a diagram showing the diagnostic results of an embodiment of the present application in which the test set is fed into the CBAM-CFBLS model. DETAILED DESCRIPTION

[0088] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some structures related to the present application are shown in the accompanying drawings, rather than all structures. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0089] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0090] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0091] Gramian Angular Field (GAF) is an effective method to convert one-dimensional time series data into two-dimensional image representation. Gramian Angular Field treats each data point in the one-dimensional time series data as a point in the vector space, calculates the cosine value of the angle between these points, and then maps these cosine values ​​to the pixels of the two-dimensional image, thereby generating an image that can reflect the dynamic and periodic characteristics of the time series. This method uses the concept of Gramian matrix in linear algebra and reflects the similarity of data at different times by calculating the inner product between vectors.

[0092] Gramian Angular Summation Field (GASF) is a technique for converting one-dimensional time series data into two-dimensional images. It is an implementation of Gramian Angular Field (GAF). GASF generates a two-dimensional image that reflects the dynamic and periodic characteristics of the time series by mapping the one-dimensional time series data onto the unit circle and calculating the cosine sum of the angles between these points. This method preserves the time dependence and correlation in the time series, allowing the converted image to be used for further analysis and processing.

[0093] Gramian Angular Difference Field (GADF) is a technique for converting one-dimensional time series data into a two-dimensional image representation. The GADF method is based on the concept of the Gramian matrix. By calculating the angle difference between different moments in the time series data, a two-dimensional matrix that can reflect the trend of data changes is generated, and then this matrix is ​​used as the pixel value of the image to generate a two-dimensional image. This method utilizes the temporal characteristics of time series data and the geometric meaning of the angle difference to convert one-dimensional data into a two-dimensional image for further analysis and processing.

[0094] Broad Learning System (BLS) is a neural network learning framework that aims to improve learning ability and efficiency by expanding the width of the network rather than its depth. It is an improvement and development of the Random Vector Function Link Network (RVFLNN) and the Single-layer Feedforward Neural Network (SLFN). BLS emphasizes feature extraction and linear transformation, and its basic structure includes an input layer, a mapping node (feature node) layer, an enhancement node layer, and an output layer. Unlike deep learning, BLS does not increase the number of network layers, but increases the number of nodes in the same layer to achieve efficient incremental learning.

[0095] The Convolutional Block Attention Module (CBAM) in the multi-attention mechanism is an attention mechanism designed to enhance the modeling and representation capabilities of convolutional neural networks (CNNs) for image features. CBAM enables the model to dynamically adjust the weights of feature maps to adapt to different tasks and scenarios by introducing two mechanisms: channel attention and spatial attention. CBAM is a lightweight attention module that can be seamlessly integrated into existing convolutional neural network architectures without requiring extensive modifications to the underlying network structure. It enhances the network's focus on important features by sequentially applying channel attention and spatial attention, thereby improving the performance of the model.

[0096] The embodiment of the present application introduces a multi-attention mechanism (Convolutional Block Attention Module, CBAM), namely a channel attention mechanism and a spatial attention mechanism, before a broad learning system (BLS) feature node to obtain global features and improve the feature expression effect of BLS. By cascading feature nodes, CFBLS is constructed, the acquired global features are input into the CFBLS feature node, and the model is trained and adjusted with a training set to establish a CBAM-CFBLS (Convolutional Block Attention Module-enhanced Broad Learning System) transformer fault diagnosis model.

[0097] like Figure 1 As shown, the embodiment of the present application provides a transformer fault diagnosis method, comprising the following steps:

[0098] Step 101, obtaining vibration signals of the transformer core and windings.

[0099] During the operation of the transformer, the core and winding will vibrate due to various factors such as electromagnetic force and mechanical force. These vibration signals contain rich equipment status information. By collecting and analyzing these signals, the transformer status can be monitored and fault diagnosis can be performed.

[0100] In order to comprehensively and accurately obtain the vibration signals of the transformer core and windings, it is necessary to select appropriate vibration measurement points on the surface of the transformer body. At each selected vibration measurement point, a vibration sensor needs to be installed to collect vibration signals. Vibration sensors usually include acceleration sensors, velocity sensors and other types. Appropriate sensors can be selected according to actual needs. The installation of the sensor should ensure stability and reliability to reduce the impact of external interference on the signal.

[0101] The embodiment of the present application uses an acceleration sensor for field measurement to collect transformer vibration signals. Specifically, the transformer vibration signal base frequency is 100Hz, the transformer vibration signal sampling frequency is 10Khz, and a set of vibration signal data is obtained every 10s, that is, 1000 data points constitute 1 vibration signal sample, and the collected transformer vibration signals include normal transformer vibration signals, core loose vibration signals, winding overlap vibration signals, winding deformation vibration signals, winding loose vibration signals, vibration signals of core loose and winding overlap at the same time, and vibration signals of core loose and winding deformation at the same time, a total of 7 vibration signals, 300 vibration signal samples are collected for each vibration signal, and a total of 2100 vibration signal samples.

[0102] Step 102, the vibration signal is processed by using the Gram angle field to obtain a feature image, wherein the feature image includes a Gram angle sum field feature image and a Gram angle difference field feature image.

[0103] Processing vibration signals through Gramian Angular Field (GAF) is a method of converting time series data into image representation, which is particularly suitable for processing one-dimensional time series data such as vibration signals. This method can retain the dynamic characteristics and correlation of the time series and convert it into a two-dimensional image, which is convenient for subsequent feature extraction and pattern recognition.

[0104] The embodiment of the present application adopts GAF to convert the transformer vibration signal into a two-dimensional image, normalizes the collected transformer vibration signal, eliminates the noise in the transformer vibration signal through the inner product with a penalty term, and then completes the conversion of the transformer vibration signal into two feature images of GASF and GADF by GAF.

[0105] Specifically, through the sequence X=(X 1 ,X 2 ,...,X n ) represents the vibration signal. The vibration signal is normalized and the sequence X is normalized to the interval [0,1] to obtain the normalized value. The formula is as follows:

[0106] Where i = 1, 2, ..., n, x i represents the i-th group of vibration signals, max(X) represents the minimum value in the vibration signal, and min(X) represents the minimum value in the vibration signal.

[0107] The transformer vibration signal sequence X is mapped to a time series in polar coordinates by generating polar coordinates using the time node as the arc cosine of the radius and scaling value. The formula is as follows:

[0108]

[0109] Among them, t i represents the time node, N represents the regularization constant factor in polar coordinates, θ i represents the level angle of point i, r represents the polar coordinate radius, Represents a normalized time series.

[0110] The noise in the transformer vibration signal is eliminated by the inner product with penalty terms. The formulas of two unique inner products with penalty terms are as follows:

[0111]

[0112] Here, <·> represents the inner product operation.

[0113] The GASF two-dimensional characteristic diagram is generated by calculating the cosine function of the sum of every two level angles and summing them up. The formula is as follows:

[0114]

[0115] By calculating the sine function of the difference between every two level angles and summing them up, a GADF two-dimensional feature map is generated. The formula is as follows:

[0116]

[0117] Where I represents the unit row vector, represents the normalized time series, express The above transformation converts the given time series into two-dimensional feature images of GASF and GADF that are symmetrical along the diagonal, with increased time and pixel points, retaining the time-related features within the time series.

[0118] The 2100 vibration signal samples were transformed through GAF to obtain 1400 GAF images, which included 7 categories and 300 vibration signal samples in each category.

[0119] Step 103, processing the Gram angle sum field feature image and the Gram angle difference field feature image through a multi-attention mechanism to obtain a global feature matrix.

[0120] When processing image features, especially when it comes to features such as Gramian Angular Field (GAF) and Gramian Angular Difference Field (GADF), it is an effective method to use a multi-attention mechanism to extract a global feature matrix. This method can help the model pay more attention to important areas in the image, thereby improving the quality of feature representation.

[0121] The embodiment of the present application introduces CBAM, namely the channel attention mechanism and the spatial attention mechanism, before the BLS feature node to obtain global features, improve the feature expression effect of BLS, and construct CFBLS by cascading feature nodes.

[0122] Specifically, the GASF and GADF feature images are used as CBAM inputs. First, the channel attention mechanism is used to suppress the background noise and redundant information in the remote sensing image in the channel dimension, while enhancing the expression of significant features. The formula is as follows:

[0123]

[0124] Where i = 1, 2, ..., n, Z i ' represents the features of the i-th group of feature maps output by the channel attention mechanism, M c represents the attention weight in the channel dimension, It represents the feature map obtained after GAF transformation of the input i-th group of vibration signal samples. represents element-wise multiplication;

[0125] The features output by the channel attention mechanism are used as the input of the spatial attention mechanism. The formula is as follows:

[0126]

[0127] Among them, M s Represents the attention weight in the spatial dimension; Z i " represents the features of the i-th group of feature maps output by the spatial attention mechanism;

[0128] After the above CBAM processing, the global feature matrix is ​​obtained:

[0129] Step 104 , based on the width learning system, feature nodes are cascaded on the global feature matrix to construct a CFBLS model.

[0130] Based on the Broad Learning System (BLS), the global feature matrix is ​​cascaded with feature nodes to build a Composite Feature Broad Learning System (CFBLS) model, which is a task that extends the traditional BLS framework to handle more complex feature fusion and data representation. The Broad Learning System is a fast learning framework based on a random weight neural network, which enhances the feature representation of input data by constructing enhancement nodes. BLS mainly consists of three parts: an input layer, an enhancement layer (also called a feature mapping layer), and an output layer. The global feature matrix is ​​usually obtained from the original data through feature extraction methods (such as PCA, LDA, deep learning feature extraction, etc.). This matrix contains a global representation of the data sample, each row represents a sample, and each column represents a feature. In CFBLS, the embodiment of the present application regards the global feature matrix as part of the input layer, and cascades and enhances the feature nodes on this basis.

[0131] In the embodiment of the present application, the BLS converts the input The conversion to n groups of random feature nodes is done by taking the output of the previous group of feature nodes as the input of the next group of feature nodes. The formulas for the first and second groups of feature nodes are as follows:

[0132]

[0133] In the formula, represents the feature layer activation function, W e1 and β e1 Respectively represent the weight and bias of the first randomly generated feature node, W e2 and β e2 They respectively represent the weight and bias of the randomly generated second feature node.

[0134] By analogy, CFBLS is constructed. The formula of the i-th group of feature nodes in CFBLS is as follows:

[0135]

[0136] Where i = 1, 2, ..., n, represents the feature layer activation function, W ei and β ei They represent the weight and bias of the randomly generated i-th feature node respectively.

[0137] Step 105, construct a data set based on the feature image, and divide the data set into a training set and a test set; input the global feature matrix into the CFBLS model, adjust the parameters of the CFBLS model through the training set, construct a CBAM-CFBLS transformer fault diagnosis model, and test the CBAM-CFBLS transformer fault diagnosis model through the test set until the fault diagnosis accuracy reaches a preset threshold.

[0138] In the embodiment of the present application, based on the vibration signal samples obtained in step 102, 300 samples of each type are divided into a training set and a test set in a ratio of 7:3.

[0139] like Fig.11 As shown, in order to construct a transformer fault diagnosis model based on feature images and apply the combination of CBAM and CFBLS, the embodiment of the present application connects each group of feature nodes of the CFBLS model in series to obtain a complete feature mapping layer Z p , the formula is as follows:

[0140] Z p =[Z 1 ,Z 2 ,...,Z p ]

[0141] For the complete feature map node Z pPerform nonlinear transformation to obtain enhanced nodes. This step aims to increase the nonlinear ability and feature expression ability of the model. The formula is as follows:

[0142] H j =ξ(ZW hk +β hk )

[0143] Where j = 1, 2, ..., q, ξ(·) represents the linear activation function of the enhancement node, W hk and β hk They represent the weight and bias of the randomly generated k-th enhancement node respectively;

[0144] Each enhancement node is connected in series to obtain a complete enhancement layer H q , the formula is as follows:

[0145] H q =[H 1 H 2 ,...,H q ]

[0146] The feature map layer Z p and enhancement layer H q After being connected in series to the output layer, a complete CFBLS model is constructed. The formula of the output matrix is ​​as follows:

[0147] Y=[Z p |H q ]W=AW

[0148] Where A=[Z p |H q ], represents the hidden layer of all feature mapping layers and enhancement layers, and W represents the weight connecting the hidden layer and the output layer;

[0149] In terms of model training and optimization, the embodiment of the present application optimizes and solves the objective function through the ridge regression method. Ridge regression is a technique for solving the overfitting problem in linear regression. It limits the complexity of the model by adding a regularization term (usually the sum of the squares of the weights) to the loss function, and calculates the objective function of the optimal output weight. The formula is as follows:

[0150]

[0151] Among them, λ represents the regularization parameter;

[0152] The optimal output weight is calculated by the Tikhonov regularization method. Tikhonov regularization (also known as Tikhonov regularization) is a form of ridge regression, which is mathematically equivalent to adding a small positive definite matrix to the coefficient matrix when solving a linear system of equations to improve the stability of the solution and obtain the optimal output weight W.best , the formula is as follows:

[0153] W best =A + Y=(λI+A T A) -1 A T Y

[0154] Among them, A + represents the pseudo-inverse matrix of A, and I represents the identity matrix;

[0155] When λ→0, the pseudo-inverse matrix A + The formula is as follows:

[0156]

[0157] At this point, the embodiment of the present application has obtained the CBAM-CFBLS transformer fault diagnosis model through the above steps. Next, the transformer fault diagnosis model CBAM-CFBLS is tested through a test set to calculate the diagnostic accuracy of the model for the test set.

[0158] The fault information is shown in the following table:

[0159] Table 1

[0160] Fault location Fault type Label normal / 1 Iron Core Loose 2 Winding Overlap 3 Winding Deformation 4 Winding Loose 5 Core, Winding Loose, overlapping 6 Core, Winding Looseness, deformation 7

[0161] like Figures 4 to 10 As shown, they are normal transformer vibration signal, core loose vibration signal, winding overlap vibration signal, winding deformation vibration signal, winding loose vibration signal, vibration signal of core looseness and winding overlap, and vibration signal of core looseness and winding deformation. The seven signals are transformed into GASF and GADF two-dimensional feature images through GAF transformation.

[0162] like Fig.12 As shown in Figure 1, the test results of the transformer fault diagnosis model CASM-CFBLSN using the test set are shown.

[0163] Step 106: perform transformer fault diagnosis using the trained CBAM-CFBLS transformer fault diagnosis model.

[0164] Ensure that the transformer data required for fault diagnosis matches the data format and type used when training the model. Usually, these data will be transformer features represented in the form of images (such as Gram angle field, Gram angle difference field, etc.).

[0165] The input data is subjected to the same preprocessing steps as the training data, including normalization, scaling, etc., to ensure data consistency.

[0166] Load the trained CBAM-CFBLS model into the working environment and input the preprocessed transformer data into the CBAM-CFBLS model. Inside the model, the data will first pass through the CBAM module for feature enhancement, and then pass through the CFBLS module for feature processing and classification.

[0167] The input transformer data is processed and prediction results are output. These results are usually a set of probability values, indicating the likelihood that the input data belongs to each fault category.

[0168] According to the probability value output by the model, the category with the highest probability is selected as the prediction result. This category is the most likely transformer fault type considered by the model.

[0169] Based on the prediction results, corresponding repair or maintenance decisions are made. For example, if the prediction results show that the transformer has a certain fault, it may be necessary to arrange for maintenance personnel to conduct on-site inspection and repair.

[0170] like Figure 2 As shown, the embodiment of the present application provides a transformer fault diagnosis device, including a signal acquisition module 201, a feature image module 202, a feature matrix module 203, a model construction module 204, a model training module 205 and a fault diagnosis module 206, wherein:

[0171] A signal acquisition module 201 is configured to acquire vibration signals of the transformer core and windings;

[0172] The feature image module 202 is configured to process the vibration signal through the Gram angle field to obtain a feature image, wherein the feature image includes a Gram angle sum field feature image and a Gram angle difference field feature image;

[0173] A feature matrix module 203 is configured to process the Gram angle sum field feature image and the Gram angle difference field feature image through a multi-attention mechanism to obtain a global feature matrix;

[0174] A model building module 204 is configured to perform feature node cascading on a global feature matrix based on a width learning system to build a CFBLS model;

[0175] The model training module 205 is configured to construct a data set based on the feature image, and divide the data set into a training set and a test set; input the global feature matrix into the CFBLS model, adjust the parameters of the CFBLS model through the training set, construct a CBAM-CFBLS transformer fault diagnosis model, and test the CBAM-CFBLS transformer fault diagnosis model through the test set until the fault diagnosis accuracy reaches a preset threshold;

[0176] The fault diagnosis module 206 is configured to perform transformer fault diagnosis using the trained CBAM-CFBLS transformer fault diagnosis model.

[0177] The transformer fault diagnosis device of the embodiment of the present application can be a computer device, or a component in the computer device, such as an integrated circuit or a chip. The computer device can be a terminal, or other devices other than a terminal. Exemplarily, the computer device can be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted computer device, a mobile Internet device (Mobile Internet Device, MID), an ultra-mobile personal computer (Ultra-Mobile Personal Computer, UMPC), a netbook or a personal digital assistant (Personal Digital Assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (Personal Computer, PC), etc., which is not specifically limited in the embodiment of the present application.

[0178] The transformer fault diagnosis device provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the transformer fault diagnosis method embodiment are not described here.

[0179] The present application also provides a computer device, such as Figure 3 As shown, the computer device includes a processor 301 and a memory 302. The memory 302 stores programs or instructions that can be run on the processor 301. When the program or instruction is executed by the processor 301, each step of the above-mentioned transformer fault diagnosis method is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0180] It should be noted that the computer device in the embodiment of the present application includes the above-mentioned mobile computer device and non-mobile computer device.

[0181] The memory 302 can be used to store software programs and various data. The memory 302 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 302 may include a volatile memory or a non-volatile memory, or the memory 302 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 302 of the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0182] The processor 301 may include one or more processing units; optionally, the processor 301 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 301.

[0183] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned transformer fault diagnosis method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0184] An embodiment of the present application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned transformer fault diagnosis method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0185] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0186] An embodiment of the present application also provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned transformer fault diagnosis method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0187] It should be noted that, in the present application, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0188] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A transformer fault diagnosis method, characterized in that: The method comprises: Obtain vibration signals of transformer core and winding; Processing the vibration signal through a Gram angle field to obtain a characteristic image, wherein the characteristic image includes a Gram angle sum field characteristic image and a Gram angle difference field characteristic image; The Gram angle sum field feature image and the Gram angle difference field feature image are processed by a multi-attention mechanism to obtain a global feature matrix; Based on the width learning system, the feature nodes of the global feature matrix are cascaded to construct a CFBLS model; A data set is constructed based on the feature image, and the data set is divided into a training set and a test set; the global feature matrix is ​​input into the CFBLS model, the CFBLS model is adjusted through the training set, a CBAM-CFBLS transformer fault diagnosis model is constructed, and the CBAM-CFBLS transformer fault diagnosis model is tested through the test set until the fault diagnosis accuracy reaches a preset threshold; Transformer fault diagnosis is performed using the trained CBAM-CFBLS transformer fault diagnosis model.

2. The transformer fault diagnosis method according to claim 1, characterized in that: The step of processing the vibration signal by using the Gram angle field to obtain a characteristic image includes: By the sequence X=(X1,X2,...,X n ) represents the vibration signal, the vibration signal is normalized, the sequence X is normalized to the interval [0,1], and the normalized value is obtained The formula is as follows: Where i = 1, 2, ..., n, x i represents the i-th group of vibration signals, max(X) represents the minimum value in the vibration signal, and min(X) represents the minimum value in the vibration signal.

3. The transformer fault diagnosis method according to claim 2, characterized in that: The method includes mapping the sequence X into a time series in polar coordinates, and the formula is as follows: Among them, t i represents the time node, N represents the regularization constant factor in polar coordinates, θ i represents the level angle of point i, r represents the polar coordinate radius, Represents a normalized time series.

4. The transformer fault diagnosis method according to claim 3, characterized in that: The method includes eliminating noise in the vibration signal through two inner products with penalty terms, and the formula is as follows: Here, <·> represents the inner product operation.

5. The transformer fault diagnosis method according to claim 3, characterized in that: The method comprises generating a Gram angle and field two-dimensional characteristic image by calculating the cosine function of the sum of each two level angles and summing them up, and the formula is as follows: By calculating the sine function of the difference between every two level angles and summing them up, a two-dimensional characteristic image of the Gram angle difference field is generated. The formula is as follows: Where I represents the unit row vector, represents the normalized time series, express The transpose of .

6. The transformer fault diagnosis method according to claim 1, characterized in that: The multi-attention mechanism is used to process the Gram angle sum field feature image and the Gram angle difference field feature image to obtain a global feature matrix, including: The Gram angle sum field feature image and the Gram angle difference field feature image are used as inputs of a multi-attention mechanism, wherein the multi-attention mechanism includes a channel attention mechanism and a spatial attention mechanism. The formula of the channel attention mechanism is as follows: Where i = 1, 2, ..., n, Z i ' represents the features of the i-th group of feature maps output by the channel attention mechanism, M c represents the attention weight in the channel dimension, It represents the feature map obtained after GAF transformation of the input i-th group of vibration signal samples. represents element-wise multiplication; The features output by the channel attention mechanism are used as the input of the spatial attention mechanism. The formula is as follows: Among them, M s Represents the attention weight in the spatial dimension; Z i " represents the features of the i-th group of feature maps output by the spatial attention mechanism; The formula of the global feature matrix is ​​as follows:

7. The transformer fault diagnosis method according to claim 6, characterized in that: The width-based learning system performs feature node cascading on the global feature matrix to construct a CFBLS model, including: The global feature matrix is ​​input into the width learning system, and the width learning system converts the global feature matrix into n groups of random feature nodes through linear mapping, and uses the output of the previous group of feature nodes as the input of the next group of feature nodes to obtain the i-th group of feature nodes of the CFBLS model. The formula is as follows: Where i = 1, 2, ..., n, represents the feature layer activation function, W ei and β ei Respectively represent the weight and bias of the randomly generated i-th feature node; Each group of feature nodes of the CFBLS model is connected in series to obtain a complete feature mapping layer Z p , the formula is as follows: WITH p =[Z1,Z2,...,Z p ] For the complete feature map node Z p The enhanced nodes are obtained by performing nonlinear transformation. The formula is as follows: H j =ξ(ZW hk +b hk ) Where j = 1, 2, ..., q, ξ(·) represents the linear activation function of the enhancement node, W hk and β hk They represent the weight and bias of the randomly generated k-th enhancement node respectively; Each enhancement node is connected in series to obtain a complete enhancement layer H q , the formula is as follows: H q =[H1 H2,...,H q ] The feature map layer Z p and the reinforcing layer H q After being connected in series to the output layer, a complete CFBLS model is formed. The formula of the output matrix is ​​as follows: <h2 style=";text-align:left;direction:ltr">Y=[Z<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> |H<h2 style=";text-align:left;direction:ltr"> q <h2 style=";text-align:left;direction:ltr"> ]W=AW Where A=[Z p |H q ], represents the hidden layer of all feature mapping layers and enhancement layers, and W represents the weight connecting the hidden layer and the output layer; The objective function for calculating the optimal output weight is as follows: Among them, λ represents the regularization parameter; The optimal output weight W is calculated by the Tikhonov regularization method best , the formula is as follows: W best =A + Y=(λI+A T A) -1 A T Y Among them, A + represents the pseudo-inverse matrix of A, and I represents the identity matrix; When λ→0, the pseudo-inverse matrix A + The formula is as follows:

8. A transformer fault diagnosis device, characterized in that: The device comprises: A signal acquisition module is configured to acquire vibration signals of the transformer core and winding; A feature image module is configured to process the vibration signal through a Gram angle field to obtain a feature image, wherein the feature image includes a Gram angle sum field feature image and a Gram angle difference field feature image; A feature matrix module is configured to process the Gram angle sum field feature image and the Gram angle difference field feature image through a multi-attention mechanism to obtain a global feature matrix; A model building module is configured to perform feature node cascading on the global feature matrix based on a width learning system to build a CFBLS model; A model training module is configured to construct a data set based on the feature image, and divide the data set into a training set and a test set; input the global feature matrix into the CFBLS model, adjust the parameters of the CFBLS model through the training set, construct a CBAM-CFBLS transformer fault diagnosis model, and test the CBAM-CFBLS transformer fault diagnosis model through the test set until the fault diagnosis accuracy reaches a preset threshold; The fault diagnosis module is configured to perform transformer fault diagnosis through the trained CBAM-CFBLS transformer fault diagnosis model.

9. A transformer fault diagnosis system, the system comprising a processor and a memory, wherein a computer program is stored in the memory, characterized in that: The computer program is loaded and executed by the processor to implement the transformer fault diagnosis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein a computer program is stored in the storage medium, characterized in that: When the computer program is executed by a processor, it is used to implement the transformer fault diagnosis method according to any one of claims 1 to 7.

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