Transformer fault diagnosis method, system and equipment based on attention variation self-coding and storage medium

Abstract

The invention discloses a transformer fault diagnosis method, system and device based on attention variation self-coding and a storage medium. The method comprises the steps that historical concentration data of all characteristic gases of a transformer, historical oil temperature data of the transformer and historical load data of the transformer are acquired; performing normalization processing on the historical data; constructing a self-attention variation self-encoding model; extracting fault features to obtain a fault feature training set, and constructing a probabilistic neural network fault classification model; performing normalization processing on the data monitored in real time, taking the processed real-time data as input data of a self-attention variation self-encoding model, dynamically predicting the concentration of the dissolved gas to obtain a prediction deviation of the concentration of the dissolved gas, and if the prediction deviation is greater than a preset threshold value, taking the prediction deviation as a fault feature; and performing fault diagnosis through a probabilistic neural network fault classification model by using the obtained fault features, and outputting a fault type. The dissolved gas prediction accuracy and the explanation capability of the model can be improved, and the fault can be accurately diagnosed.

Description

Technical Field

[0001] The present invention relates to electric power automation technology, and in particular to a transformer fault diagnosis method, system, device and storage medium based on attention variational autoencoding. Background Art

[0002] The best method for early diagnosis of transformer faults is mainly the dissolved gas analysis method in oil. The concentration of characteristic gases dissolved in transformer oil can capture the precursor of transformer equipment failure. In practice, the dissolved gas analysis method in transformer oil is often used to monitor and judge the operation of the transformer during oil sample collection, and to promptly discover hidden defects or faults of the transformer. The principle is that oil-filled power transformers will age and deteriorate under the action of electricity or heat during long-term operation, and a small amount of gas (generally called characteristic gas) will be dissolved in the transformer oil. Generally speaking, the amount of dissolved gas is a gradual and steady accumulation process, but when a fault or abnormality occurs, the gas content value will change greatly.

[0003] Taking into account the gas production principle of transformer characteristic gas and the influence of transformer oil temperature and load on characteristic gas, the big data analysis algorithm is used to explore the mutual correlation between characteristic gases dissolved in transformer oil and the correlation between characteristic gases and transformer equipment load and top oil temperature. The factors with a large correlation with the target gas to be predicted are used as input variables, and the changes in oil-soluble gas concentration in the future are accurately predicted. The latent faults of transformer equipment are effectively identified, and the advanced control of the equipment is realized.

[0004] In order to predict the concentration of dissolved gas in transformer oil, some data-driven prediction methods have emerged. These methods use historical data to build models to predict future data. However, there are the following problems: (1) The intrinsic correlation between input variables is not considered, and variable parameters are directly stacked as model input, resulting in input redundancy and affecting the generalization ability of the model; (2) The association model between input variables and external influencing factors is not established, resulting in poor model interpretability; (3) The prediction target only considers the output reconstruction accuracy, ignoring the data distribution of low-dimensional latent variable characteristics.

[0005] As the transformer operation time increases, the data of characteristic gases also increases explosively. How to mine valuable information from massive monitoring data has become an urgent problem to be solved. Traditional manual experience makes it difficult to quickly and accurately identify the fault category. Accurate fault diagnosis is of great significance for quickly solving fault problems and ensuring the safe and stable operation of transformers. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a transformer fault diagnosis method, system, device and storage medium based on attention variational autoencoder, which can improve the accuracy of dissolved gas prediction and the interpretation ability of the model, and accurately diagnose faults.

[0007] Technical solution: A transformer fault diagnosis method based on attention variational autoencoding of the present invention comprises:

[0008] Select characteristic gases from the dissolved gases in transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer;

[0009] Normalize historical concentration data, historical oil temperature data and historical load data;

[0010] An attention variational autoencoder model is constructed and trained using normalized historical data. The correlation between the concentrations of dissolved gases in transformers is obtained through the attention variational autoencoder model, and its low-dimensional latent variable characteristics are extracted. The loss terms of the attention variational autoencoder model include the reconstruction loss of the concentration of dissolved gases in transformers, the data distribution consistency loss, and the attention prediction loss. The model introduces an attention mechanism to make the low-dimensional latent variable characteristics of the model correlated with external influencing factors.

[0011] Extract fault features from the fault database to obtain a fault feature training set, build an association network between fault features and fault types through a probabilistic neural network, and obtain a probabilistic neural network fault classification model;

[0012] The real-time monitored transformer load, oil temperature data and dissolved gas concentration are normalized, and the normalized real-time data is used as the input data of the attention variational autoencoder model to dynamically predict the dissolved gas concentration and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than the preset threshold, it is used as a fault feature.

[0013] Using the fault features output by the attention variational autoencoder model, fault diagnosis is performed through the probabilistic neural network fault classification model to output the fault type.

[0014] Furthermore, H2, CH4, C2H6, C2H4, C2H2, CO, and CO2 are selected as characteristic gases from the dissolved gases in the transformer oil, and the historical concentration data of each characteristic gas of the transformer in a fixed time period before the forecast date is extracted from the power OMS system, and the historical oil temperature and historical load data of the transformer equipment in a fixed time period before the forecast date are extracted from the power OMS system.

[0015] Furthermore, the normalization process includes:

[0016] The data centering and variance normalization method is used. The specific process is as follows:

[0017]

[0018] Among them, x o , They represent the original value and normalized value of the i-th input parameter respectively; m(x) is the mean of the parameter, and s(x) is the standard deviation of the parameter.

[0019] Furthermore, the construction process of the attention variational autoencoder model is as follows:

[0020] Construct a variational autoencoder network, in which the encoder adopts a multi-layer perceptron structure and inputs a transformer with multiple dissolved gas concentration samples x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T After multiple layers of nonlinear mapping, the output features obey the normal distribution N(μ,σ), and the output z O_mean and z o_var They correspond to the mean and covariance of the normal distribution respectively;

[0021] Use the reparameterization technique to sample N(μ,σ) to obtain a low-dimensional representation z of the latent space o , whose expression is:

[0022] z o =μ+σ·ε

[0023] Where: ε is a random variable that obeys the standard normal distribution, that is, ε~N(0,I);

[0024] The decoder structure is similar to the encoder. The low-dimensional representation z of the input encoder is used to reconstruct the original transformer gas concentration through multi-layer nonlinear mapping. The encoder and decoder are jointly optimized through the loss function. For the input x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T , the loss function of the variational autoencoder network It can be described as:

[0025]

[0026] Where: is the reconstruction loss term of the variational autoencoder network; is the approximate probability distribution q φ The Kullback-Leibler divergence loss term between (z|x) and the prior probability distribution p(z).

[0027] Furthermore, the attention variational autoencoder model input is the low-dimensional latent variable z extracted by the encoder y , zy By z y_mean and z y_var After re-parameterization, the model output is the external influencing factors, which include transformer load and oil temperature.

[0028] Furthermore, the loss term of the attention variational autoencoder model includes the reconstruction loss of the transformer dissolved gas concentration, the data distribution consistency loss and the attention prediction loss, wherein the construction process of the loss term is as follows:

[0029] Low-dimensional latent variables z0 and z y The probability distribution of follows a mutually independent multivariate Gaussian distribution. Therefore, the probability expression of the decoder is as follows:

[0030]

[0031] Where: De1 is the decoder, De2 is the decoder based on the attention mechanism; θ and These are the network parameters of De1 and De2 respectively;

[0032] Accordingly, the probability expression of the encoder is as follows:

[0033] z~En(x)=q φ (z o ,z y |z)=q φ (z o |x)q φ (z y |x)

[0034] Where: q φ (z y |x) is The approximate posterior probability distribution of En is the encoder; φ is the network parameter of En

[0035] Therefore, the overall loss function of the model is as follows:

[0036]

[0037] Where: the first term is the reconstruction loss of the transformer dissolved gas concentration, which is usually calculated using the mean square error between the predicted concentration and the actual concentration; the second term is the data distribution consistency loss, which represents the potential distribution q of the encoder output φ (z o |x) and the prior distribution p(z o ) between them; the third term is also the data distribution consistency loss, which represents the potential distribution q output by the encoder φ (z y |x) and the prior distribution p(z y), both use Kullback-Leibler divergence as the measurement indicator; the fourth term is the attention prediction loss, which is usually calculated by the mean square error between the predicted transformer load and oil temperature and the actual transformer load and oil temperature; β1 and β2 are hyperparameters for weighing different loss terms.

[0038] Furthermore, the expression of the prediction deviation is as follows:

[0039]

[0040] Where: x and are the actual value and the predicted value respectively;

[0041] Based on the same inventive concept, a transformer fault diagnosis system based on attention variational autoencoding of the present invention comprises:

[0042] A data acquisition module is used to select characteristic gases from the dissolved gases in the transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer;

[0043] Data preprocessing module, used to normalize historical concentration data, historical oil temperature data and historical load data;

[0044] An attention variational autoencoder model construction module is used to construct an attention variational autoencoder model and train the model using normalized historical data. The attention variational autoencoder model is used to obtain the correlation between the concentrations of dissolved gases in transformers and extract their low-dimensional latent variable features. The loss terms of the attention variational autoencoder model include the reconstruction loss of the concentration of dissolved gases in transformers, the data distribution consistency loss, and the attention prediction loss. The model introduces an attention mechanism to make the low-dimensional latent variable features of the model correlated with external influencing factors.

[0045] A probabilistic neural network fault classification model building module is used to extract fault features from the fault database to obtain a fault feature training set, and to build an association network between fault features and fault types through a probabilistic neural network to obtain a probabilistic neural network fault classification model;

[0046] The fault feature extraction module is used to normalize the real-time monitored transformer load, oil temperature data and dissolved gas concentration, and use the normalized real-time data as the input data of the attention variational autoencoder model to dynamically predict the dissolved gas concentration and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than a preset threshold, it is used as a fault feature and output;

[0047] The fault diagnosis module is used to utilize the fault features output by the attention variational autoencoder model to perform fault diagnosis through the probabilistic neural network fault classification model and output the fault type.

[0048] Based on the same inventive concept, a transformer fault diagnosis device based on attention variational autoencoding of the present invention includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned transformer fault diagnosis method based on attention variational autoencoding.

[0049] Based on the same inventive concept, a computer-readable storage medium of the present invention stores a computer program thereon, and when the program is executed by a processor, the steps of the above-mentioned transformer fault diagnosis method based on attention variational autoencoding are implemented.

[0050] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are:

[0051] Variational autoencoders are used to automatically learn the correlation between different oil and gas concentrations, extract low-dimensional representations, and eliminate redundancy of input variables.

[0052] The attention mechanism is introduced to establish an interpretable association model between the input and external influencing factors (transformer load and oil temperature), and to enhance the model's ability to capture key features in the input data, so as to improve the model's representation ability and prediction accuracy.

[0053] The loss term of the attention variational autoencoder model combines different losses to improve the generalization ability and prediction interpretability of the model, accurately predict the changing trend of dissolved gas concentration, and provide high-confidence fault features for transformer fault diagnosis.

[0054] By integrating the two functions of prediction and diagnosis into one, a complete transformer fault diagnosis system has been built, which can realize the intelligentization from data collection to fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a transformer fault diagnosis method based on attention variational autoencoding disclosed in an embodiment of the present invention;

[0056] Figure 2 It is a structural diagram of an attention-based variational autoencoder model disclosed in an embodiment of the present invention;

[0057] Figure 3 It is a structural schematic diagram of a transformer fault diagnosis system based on attention variational autoencoding disclosed in an embodiment of the present invention;

[0058] Figure 4It is a structural schematic diagram of a transformer fault diagnosis device based on attention variational autoencoding disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It will be understood by those skilled in the art that the purposes and advantages that can be achieved with the present invention are not limited to the specific description of the above beneficial effects, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description.

[0060] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design of the technical solution. Professional and technical personnel can use different methods to implement the functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0061] Reference to "embodiments" in the present invention 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 invention. The appearance of the phrase in various places 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 herein may be combined with other embodiments.

[0062] Example 1

[0063] See also Figure 1 , Figure 1 : is a flow chart of a transformer fault diagnosis method based on attention variational autoencoding disclosed in an embodiment of the present invention. Figure 1 The transformer fault diagnosis method based on attention variational autoencoder described is applied to power systems, such as transformer fault diagnosis, etc., which is not limited in the embodiments of the present invention. Figure 1 As shown, the transformer fault diagnosis method based on attention variational autoencoding may include the following operations:

[0064] S1. Select characteristic gases from the dissolved gases in transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer.

[0065] In this embodiment, H2 (hydrogen), CH4 (methane), C2H6 (ethane), C2H4 (ethylene), C2H2 (acetylene), CO (carbon monoxide), and CO2 (carbon dioxide) are selected from the dissolved gases in the transformer oil as characteristic gases, and the historical concentration data of each characteristic gas of the transformer 30 days before the prediction date is extracted from the power OMS system (power grid dispatching and operation management system), and the historical oil temperature and historical load data of the transformer equipment 30 days before the prediction date are extracted from the power OMS system.

[0066] S2. Normalize the historical concentration data, historical oil temperature data and historical load data.

[0067] The collected historical data needs to be preprocessed by data normalization to eliminate the influence of different parameter measurement dimensions on the clustering results. The z-score standardization is adopted, that is, the data centering and variance normalization method. The specific process is as follows:

[0068]

[0069]

[0070] Among them, x i , They represent the original value and normalized value of the i-th input parameter respectively; m(x) is the mean of the parameter, and s(x) is the standard deviation of the parameter.

[0071] S3. Construct an attention variational autoencoder model and use normalized historical data to train the model. The attention variational autoencoder model is used to obtain the correlation between the dissolved gas concentrations of the transformer and extract its low-dimensional latent variable characteristics. The loss terms of the attention variational autoencoder model include the reconstruction loss of the dissolved gas concentration of the transformer, the data distribution consistency loss and the attention prediction loss. The model introduces the attention mechanism to make the low-dimensional latent variable characteristics of the model correlated with external influencing factors (transformer load and oil temperature).

[0072] In this embodiment, the construction process of the attention variational autoencoder model is as follows:

[0073] Construct a variational autoencoder network, in which the encoder adopts a multi-layer perceptron structure, and inputs a transformer with multiple dissolved gas concentration samples x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T , after multiple layers of nonlinear mapping, the output features obey the normal distribution N(μ,σ), and the output z O_mean and z o_var They correspond to the mean and covariance of the normal distribution respectively;

[0074] Use the reparameterization technique to sample N(μ,σ) to obtain a low-dimensional representation z of the latent space o , whose expression is:

[0075] z o =μ+σ·ε

[0076] Where: ε is a random variable that obeys the standard normal distribution, that is, ε~N(0,I);

[0077] The decoder structure is similar to the encoder. The low-dimensional representation z of the input encoder is used to reconstruct the original transformer gas concentration through multi-layer nonlinear mapping. The encoder and decoder are jointly optimized through the loss function. For the input x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T , the loss function of the variational autoencoder network It can be described as:

[0078]

[0079] Where: is the reconstruction loss term of the variational autoencoder network; is the approximate probability distribution q φ The Kullback-Leibler divergence loss term between (z|x) and the prior probability distribution p(z).

[0080] In this embodiment, the attention variational autoencoder model input is the low-dimensional latent variable z extracted by the encoder y , z y By z y_mean and z y_var It is obtained by re-parameterization; through the decoding network based on the attention mechanism, the model output is external influencing factors such as transformer load and oil temperature.

[0081] In this embodiment, the loss term of the attention variational autoencoder model includes the reconstruction loss of the transformer dissolved gas concentration, the data distribution consistency loss and the attention prediction loss, wherein the construction process of the loss term is as follows:

[0082] The network structure of the attention variational autoencoder model is as follows Figure 2 As shown. Low-dimensional latent variable z o and z y The probability distribution of follows a mutually independent multivariate Gaussian distribution. Therefore, the probability expression of the decoder is as follows:

[0083]

[0084] Where: De1 is the decoder, De2 is the decoder based on the attention mechanism; θ and These are the network parameters of De1 and De2 respectively;

[0085] Accordingly, the probability expression of the encoder is as follows:

[0086] z~En(x)=q φ (z o ,z y |x)=q φ (z o |x)q φ (z y |x)

[0087] Where: q φ (z y |x) is The approximate posterior probability distribution of En is the encoder; φ is the network parameter of En

[0088] Therefore, the overall loss function of the model is as follows:

[0089]

[0090] Where: the first term is the reconstruction loss of the transformer dissolved gas concentration, which is usually calculated using the mean square error between the predicted concentration and the actual concentration; the second term is the data distribution consistency loss, which represents the potential distribution q of the encoder output φ (z o |x) and the prior distribution p(z o ) between them; the third term is also the data distribution consistency loss, which represents the potential distribution q output by the encoder φ (z y |x) and the prior distribution p(z y ), both use Kullback-Leibler divergence as the measurement indicator; the fourth term is the attention prediction loss, which is usually calculated by the mean square error between the predicted transformer load and oil temperature and the actual transformer load and oil temperature; β1 and β2 are hyperparameters for weighing different loss terms.

[0091] S4. Extract fault features from the fault database to obtain a fault feature training set, build an association network between the fault features and the fault types through a probabilistic neural network, and obtain a probabilistic neural network fault classification model.

[0092] S5. Normalize the real-time monitored transformer load, oil temperature data and dissolved gas concentration, use the normalized real-time data as input data of the attention variational autoencoder model, dynamically predict the dissolved gas concentration, and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than the preset threshold, it is used as a fault feature and output.

[0093] Among them, the prediction deviation of dissolved gas concentration is the deviation between the predicted value and the actual value. The expression of the prediction deviation is as follows:

[0094]

[0095] Where: x and are the actual value and the predicted value respectively.

[0096] S6. Using the fault features output by the attention variational autoencoder model, the probabilistic neural network fault classification model is used to perform fault diagnosis and output the fault type.

[0097] Among them, the fault categories include: high temperature overheating, medium temperature overheating, low temperature overheating, partial discharge, low energy discharge, and high energy discharge.

[0098] The method provided by the present invention can effectively improve the interpretability and generalization ability of the prediction model, accurately predict the changing trend of oil-soluble gas concentration, provide high-reliability fault characteristics based on prediction deviation, and realize accurate diagnosis of transformer faults, providing a reliable basis for transformer health management and maintenance decisions.

[0099] Example 2

[0100] See also Figure 3 , Figure 3 : is a structural diagram of a transformer fault diagnosis system based on attention variational autoencoding disclosed in an embodiment of the present invention. The system can realize transformer fault diagnosis, specifically including:

[0101] A data acquisition module is used to select characteristic gases from the dissolved gases in the transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer;

[0102] Data preprocessing module, used to normalize historical concentration data, historical oil temperature data and historical load data;

[0103] The attention variational autoencoder model construction module is used to construct the attention variational autoencoder model and train the model using normalized historical data. The attention variational autoencoder model is used to obtain the correlation between the concentrations of dissolved gases in transformers and extract their low-dimensional latent variable features. The loss terms of the attention variational autoencoder model include the reconstruction loss of the concentration of dissolved gases in transformers, the data distribution consistency loss, and the attention prediction loss. The model introduces the attention mechanism to make the low-dimensional latent variable features of the model correlated with external influencing factors.

[0104] A probabilistic neural network fault classification model building module is used to extract fault features from the fault database to obtain a fault feature training set, and to build an association network between fault features and fault types through a probabilistic neural network to obtain a probabilistic neural network fault classification model;

[0105] The fault feature extraction module is used to normalize the real-time monitored transformer load, oil temperature data and dissolved gas concentration, and use the normalized real-time data as the input data of the attention variational autoencoder model to dynamically predict the dissolved gas concentration and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than a preset threshold, it is used as a fault feature and output;

[0106] The fault diagnosis module is used to utilize the fault features output by the attention variational autoencoder model to perform fault diagnosis through the probabilistic neural network fault classification model and output the fault type.

[0107] In an optional embodiment, a transformer fault diagnosis method based on attention variational autoencoder includes: a) obtaining historical concentration data of each characteristic gas of the transformer, historical oil temperature data of the transformer, and historical load data; b) normalizing the historical concentration data, historical oil temperature data, and historical load data; c) constructing an attention variational autoencoder model; d) extracting fault features from a fault database to obtain a fault feature training set, constructing an association network between fault features and fault types through a probabilistic neural network, and obtaining a probabilistic neural network fault classification model; e) normalizing the real-time monitored transformer load, oil temperature data, and dissolved gas concentration, using the normalized real-time data as input data of the attention variational autoencoder model, dynamically predicting the dissolved gas concentration, and obtaining a prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than a preset threshold, it is used as a fault feature; f) using the acquired fault features to perform fault diagnosis through a probabilistic neural network fault classification model and output the fault type.

[0108] Example 3

[0109] See also Figure 4 , Figure 4 : is a schematic diagram of a transformer fault diagnosis device based on attention variational autoencoding disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a power system, such as transformer fault diagnosis, etc., which is not limited in the embodiments of the present invention.

[0110] like Figure 4As shown, the device may include a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiment and can achieve the technical effect consistent with the above method.

[0111] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0112] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0113] Example 4

[0114] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the above embodiment are implemented and the technical effect consistent with the above method can be achieved.

[0115] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0116] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0117] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0118] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0119] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0120] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A transformer fault diagnosis method based on attention variational autoencoder, characterized in that: include: Select characteristic gases from the dissolved gases in transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer; Normalize historical concentration data, historical oil temperature data and historical load data; An attention variational autoencoder model is constructed and trained using normalized historical data. The correlation between the concentrations of dissolved gases in transformers is obtained through the attention variational autoencoder model, and its low-dimensional latent variable characteristics are extracted. The loss terms of the attention variational autoencoder model include the reconstruction loss of the concentration of dissolved gases in transformers, the data distribution consistency loss, and the attention prediction loss. The model introduces an attention mechanism to make the low-dimensional latent variable characteristics of the model correlated with external influencing factors. Extract fault features from the fault database to obtain a fault feature training set, build an association network between fault features and fault types through a probabilistic neural network, and obtain a probabilistic neural network fault classification model; The real-time monitored transformer load, oil temperature data and dissolved gas concentration are normalized, and the normalized real-time data is used as the input data of the attention variational autoencoder model to dynamically predict the dissolved gas concentration and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than the preset threshold, it is used as a fault feature and output; Using the fault features output by the attention variational autoencoder model, fault diagnosis is performed through the probabilistic neural network fault classification model to output the fault type.

2. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: H2, CH4, C2H6, C2H4, C2H2, C0, and C02 are selected as characteristic gases from the dissolved gases in the transformer oil, and the historical concentration data of each characteristic gas of the transformer in a fixed period before the forecast date is extracted from the power OMS system. The historical oil temperature and historical load data of the transformer equipment in a fixed period before the forecast date are extracted from the power OMS system.

3. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: The normalization process includes: The z-score standardization is adopted, and the specific process is as follows: Among them, x i , They represent the original value and normalized value of the i-th input parameter respectively; m(x) is the mean of the parameter, and s(x) is the standard deviation of the parameter.

4. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: The construction process of the attention variational autoencoder model is as follows: Construct a variational autoencoder network, in which the encoder adopts a multi-layer perceptron structure and inputs a transformer with multiple dissolved gas concentration samples x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T , after multiple layers of nonlinear mapping, the output features obey the normal distribution N(μ,σ), and the output z O_mean and z o_var They correspond to the mean and covariance of the normal distribution respectively; Use the reparameterization technique to sample N(μ,σ) to obtain a low-dimensional representation z of the latent space o , whose expression is: z o =μ+σ·e Where: ε is a random variable that obeys the standard normal distribution, that is, ε~N(0,I); The decoder structure is similar to the encoder. The low-dimensional representation z of the input encoder is used to reconstruct the original transformer gas concentration through multi-layer nonlinear mapping. The encoder and decoder are jointly optimized through the loss function. For the input x = [H2, CH4, C2H6, C2H4, C2H2, CO, CO2] T , the loss function of the variational autoencoder network It can be described as: Where: is the reconstruction loss term of the variational autoencoder network; is the approximate probability distribution q φ The Kullback-Leibler divergence loss term between (z|x) and the prior probability distribution p(z).

5. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: The attention variational autoencoder model input is the low-dimensional latent variable z extracted by the encoder y , z y By z y_mean and z y_var The model output obtained after re-parameterization is the external influencing factors, which include transformer load and oil temperature.

6. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: The loss term of the attention variational autoencoder model includes the reconstruction loss of the transformer dissolved gas concentration, the data distribution consistency loss and the attention prediction loss, wherein the construction process of the loss term is as follows: Low-dimensional latent variable z o and z y The probability distribution of follows a mutually independent multivariate Gaussian distribution. Therefore, the probability expression of the decoder is as follows: Where: De1 is the decoder, De2 is the decoder based on the attention mechanism; θ and These are the network parameters of De1 and De2 respectively; Accordingly, the probability expression of the encoder is as follows: z~En(x)=q φ (z o ,z y |x)=q φ (z o |x)q φ (z y |x) Where: q φ (z y |x) is The approximate posterior probability distribution of En is the encoder; φ is the network parameter of En Therefore, the overall loss function of the model is as follows: Where: the first term is the reconstruction loss of the transformer dissolved gas concentration, which is usually calculated using the mean square error between the predicted concentration and the actual concentration; the second term is the data distribution consistency loss, which represents the potential distribution q of the encoder output φ (z o |x) and the prior distribution p(z o ) between them; the third term is also the data distribution consistency loss, which represents the potential distribution q output by the encoder φ (z y |x) and the prior distribution p(z y ), both use Kullback-Leibler divergence as the measurement indicator; the fourth term is the attention prediction loss, which is usually calculated by the mean square error between the predicted transformer load and oil temperature and the actual transformer load and oil temperature; β1 and β2 are hyperparameters for weighing different loss terms.

7. The transformer fault diagnosis method based on attention variational autoencoder according to claim 1 is characterized in that: The expression of the prediction deviation is as follows: Where: x and are the actual value and the predicted value respectively.

8. A transformer fault diagnosis system based on attention variational autoencoder, characterized in that: include: A data acquisition module is used to select characteristic gases from the dissolved gases in the transformer oil, obtain historical concentration data of each characteristic gas of the transformer, and obtain historical oil temperature data and historical load data of the transformer; Data preprocessing module, used to normalize historical concentration data, historical oil temperature data and historical load data; An attention variational autoencoder model construction module is used to construct an attention variational autoencoder model and train the model using normalized historical data. The attention variational autoencoder model is used to obtain the correlation between the concentrations of dissolved gases in transformers and extract their low-dimensional latent variable features. The loss terms of the attention variational autoencoder model include the reconstruction loss of the concentration of dissolved gases in transformers, the data distribution consistency loss, and the attention prediction loss. The model introduces an attention mechanism to make the low-dimensional latent variable features of the model correlated with external influencing factors. A probabilistic neural network fault classification model building module is used to extract fault features from the fault database to obtain a fault feature training set, and to build an association network between fault features and fault types through a probabilistic neural network to obtain a probabilistic neural network fault classification model; The fault feature extraction module is used to normalize the real-time monitored transformer load, oil temperature data and dissolved gas concentration, and use the normalized real-time data as the input data of the attention variational autoencoder model to dynamically predict the dissolved gas concentration and obtain the prediction deviation of the dissolved gas concentration. If the prediction deviation is greater than a preset threshold, it is used as a fault feature and output; The fault diagnosis module is used to utilize the fault features output by the attention variational autoencoder model to perform fault diagnosis through the probabilistic neural network fault classification model and output the fault type.

9. A transformer fault diagnosis device based on attention variational autoencoder, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the transformer fault diagnosis method based on attention variational autoencoding as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the transformer fault diagnosis method based on attention variational autoencoding as claimed in any one of claims 1 to 7.

Images