Method and system for determining transformer fault data set

Abstract

The present invention discloses a method and system for determining a transformer fault dataset. The method includes: obtaining sample transformer fault data; modeling the sample transformer fault data into a dynamic pulse width signal model based on an annihilation filtering algorithm; determining sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data; generating target pulse data based on the sample pulse data and a pre-trained pulse parameter generation model; reconstructing target transformer fault data based on the target pulse data and the dynamic pulse width signal model; and determining a transformer fault dataset based on the sample transformer fault data and the target transformer fault data. This method solves the problem of insufficient transformer fault data and improves the efficiency of determining transformer fault datasets.

Description

Technical Field

[0001] The present invention relates to the technical field of data expansion, and in particular to a method and system for determining a transformer fault data set. Background Art

[0002] The research on transformer fault data expansion technology is of great significance in the intelligent operation and maintenance of modern power systems. It mainly serves to improve the efficiency of transformer condition monitoring and fault diagnosis systems.

[0003] With the development of digital power grids and the advancement of the Internet of Things and big data technologies, the amount of real-time monitoring data of transformers has increased significantly. However, due to the sporadic nature and diversity of fault sample data, it is often difficult to accumulate enough cases to train high-precision fault diagnosis models. Summary of the Invention

[0004] The present invention provides a method and system for determining a transformer fault data set to solve the problem of insufficient transformer fault data.

[0005] According to one aspect of the present invention, a method for determining a transformer fault data set is provided, the method comprising:

[0006] Acquire sample transformer fault data, model the sample transformer fault data into a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data;

[0007] generating target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstructing target transformer fault data based on the target pulse data and the dynamic pulse width signal model;

[0008] A transformer fault data set is determined based on the sample transformer fault data and the target transformer fault data.

[0009] According to another aspect of the present invention, a system for determining a transformer fault data set is provided, the system comprising:

[0010] a sample pulse data determination module, configured to obtain sample transformer fault data, model the sample transformer fault data as a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data;

[0011] a data reconstruction module, configured to generate target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstruct target transformer fault data based on the target pulse data;

[0012] The data set determining module is configured to determine a transformer fault data set based on the sample transformer fault data and the target transformer fault data.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method for determining a transformer fault data set according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a transformer fault data set according to any embodiment of the present invention when executed.

[0018] The technical solution of the embodiments of the present invention obtains sample transformer fault data, models the sample transformer fault data as a dynamic pulse width signal model based on an annihilation filtering algorithm, and determines sample pulse data based on the model parameters of the dynamic pulse width signal model. The sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data. The sample transformer fault data is modeled as a dynamic pulse width signal model of a spatial trend curve to facilitate signal processing, feature extraction, and reconstruction. Target pulse data is generated based on the sample pulse data and a pre-trained pulse parameter generation model, and target transformer fault data is reconstructed based on the target pulse data and the dynamic pulse width signal model. New target transformer fault data is rapidly generated using the sample pulse data and the pulse parameter generation model. A transformer fault dataset is determined based on the sample transformer fault data and the target transformer fault data. Rapidly generating a transformer fault dataset based on the sample transformer fault data and the target transformer fault data solves the problem of insufficient transformer fault data and achieves the beneficial effects of improving the efficiency of determining and expanding transformer fault datasets.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flowchart of a method for determining a transformer fault data set according to the first embodiment of the present invention;

[0022] Figure 2a is a flowchart of a method for determining a transformer fault data set provided in accordance with a second embodiment of the present invention;

[0023] Figure 2b is a sample schematic diagram of a sample transformer fault data curve according to an optional example of a method for determining a transformer fault data set provided in embodiment 2 of the present invention;

[0024] Figure 3 2 is a schematic structural diagram of a system for determining a transformer fault data set according to a third embodiment of the present invention;

[0025] Figure 4 It is a structural diagram of an electronic device for implementing the method for determining a transformer fault data set according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Example 1

[0029] Figure 1 A flowchart of a method for determining a transformer fault data set is provided for the first embodiment of the present invention. This embodiment is applicable to data expansion situations. The method can be executed by a system for determining a transformer fault data set. The system for determining a transformer fault data set can be implemented in the form of hardware and / or software. The system for determining a transformer fault data set can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] S110. Obtain sample transformer fault data, model the sample transformer fault data into a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data.

[0031] The sample transformer fault data can be understood as sample fault parameters of the transformer.

[0032] Specifically, the types of sample fault parameters are divided into a parameter vector space according to a preset order. The parameter amplitude characteristics are normalized by type and divided into a trend vector space. The fault data curve is then characterized based on the parameters and trend vector space. Exemplarily, the sample fault parameters include at least one of winding temperature, dissolved gas in oil, oil temperature, oil pressure, ultrasonic partial discharge, high-frequency partial discharge, core ground current, clamp ground current, transformer operating soundprint, transformer operating current and voltage, oil level, and insulation resistance.

[0033] Optionally, the pulse width signal model is expressed by the following formula:

[0034]

[0035] Where N is the length of the parameter vector space, the basis function f k(n) is the double-pulse waveform, σ(n) is the model mismatch error, and K is the number of pulses in the double-pulse waveform.

[0036] It is worth noting that the number of pulses in the double-pulse waveform is usually pre-selected based on experience or determined by an annihilation filtering algorithm.

[0037] Optionally, the double pulse waveform is expressed by the following formula:

[0038]

[0039] in, Represents the symmetrical curve in the double pulse waveform, Indicates the asymmetric curve in the double pulse waveform, c k Represents symmetrical amplitude data, r k Indicates pulse width data, d k Indicates asymmetric amplitude data, n k Indicates delay data.

[0040] S120 , generating target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstructing target transformer fault data based on the target pulse data and the dynamic pulse width signal model.

[0041] The target pulse data can be understood as new pulse data, and the target transformer fault data can be understood as new transformer fault data.

[0042] Specifically, a pre-trained pulse parameter generation model is used to generate new pulse data conforming to a specific distribution based on the input sample pulse data. The model parameters of the pulse parameter generation model can be adjusted to generate target pulse data with different characteristics to simulate different transformer fault conditions. The target pulse data is converted into a transformer fault signal using a dynamic pulse width signal model. The generated target pulse data is mapped to the input of the dynamic pulse width signal model, and after model conversion, reconstructed target transformer fault data is output. The reconstructed target transformer fault data is verified to ensure that it has similar characteristics and distribution to the sample transformer fault data.

[0043] In this embodiment of the present invention, by generating different target pulse data, various transformer fault conditions can be simulated, providing rich test data for fault diagnosis and early warning systems. This effectively enhances the dataset and improves the generalization capability of the fault diagnosis model. By combining a pre-trained pulse parameter generation model with a dynamic pulse width signal model, transformer fault data can be efficiently generated and reconstructed.

[0044] Optionally, the pulse parameter generation model includes an inference network, a transient space, a generation network and a thermal encoder, the transient space is respectively connected to the inference network and the generation network, the thermal encoder is respectively connected to the inference network and the generation network, and the input of the inference network is the target pulse data.

[0045] Optionally, the inference network includes a first input layer, a first feedforward neuron layer connected to the first input layer, a first intermediate neuron layer connected to the first feedforward neuron layer, a first post neuron layer connected to the first neuron layer, and a first output layer connected to the first post neuron layer; the generation network includes a second input layer, a second feedforward neuron layer connected to the second input layer, a second intermediate neuron layer connected to the second feedforward neuron layer, a second post neuron layer connected to the second neuron layer, and a second output layer connected to the second post neuron layer; the transient space is connected to the first output layer and the second output layer, respectively, and the output layer of the thermal encoder is connected to the first intermediate neuron layer, the first post neuron layer, the second intermediate neuron layer, and the second post neuron layer, respectively.

[0046] It is worth noting that the dimensions of the inference network and the generation network are different.

[0047] Optionally, the target pulse data is generated based on the sample pulse data and a pre-trained pulse parameter generation model, including: inputting the sample pulse data into the inference network to obtain a transient feature vector converted based on the sample pulse data, and sending the transient feature vector to the generation network through the transient space; inputting the sample label corresponding to the sample pulse data into the hot encoder to obtain a hot encoding coefficient corresponding to the sample label encoding, and sending the hot encoding coefficient to the generation network; the generation network generates target pulse data based on the received transient feature vector and the hot encoding coefficient.

[0048] Specifically, the sample pulse data is input into the inference network, which processes the sample pulse data, extracts its transient features, and outputs a transient feature vector. The transient feature vector is sent to the generator network via the transient space. The sample label corresponding to the sample pulse data is input into the hot encoder. The hot encoder converts the sample label into a hot encoding coefficient and sends the hot encoding coefficient to the generator network. The generator network receives the transient feature vector from the inference network and the hot encoding coefficient from the hot encoder. Based on the transient feature vector and the hot encoding coefficient, the generator network generates the target pulse data.

[0049] In this embodiment of the present invention, the pulse parameter generation model can quickly and accurately generate target pulse data with specific labels or features. By adjusting the hot encoding coefficient, pulse data with different features or labels can be generated, providing a rich dataset for transformer fault diagnosis.

[0050] S130: Determine a transformer fault data set based on the sample transformer fault data and the target transformer fault data.

[0051] The transformer fault data set may be understood as a collection of a preset number of transformer fault data.

[0052] Specifically, the sample transformer fault data and the newly generated target transformer fault data are integrated, and the integrated data are preprocessed, including data cleaning, data standardization and normalization, and a transformer fault data set is determined based on a preset number of preprocessed transformer fault data.

[0053] Optionally, determining the transformer fault data set based on the sample transformer fault data and the target transformer fault data includes: reducing the sample transformer fault data based on a random downsampling method to obtain processed sample transformer fault data, and constructing a transformer fault data set based on the processed sample transformer fault data and the target transformer fault data.

[0054] Specifically, the original sample transformer fault data is randomly downsampled. A portion of samples is randomly selected from the majority class (e.g., non-fault or common fault) and deleted to reduce their number. This is to make the number of samples in different classes more balanced, which helps to improve the performance of the fault diagnosis model. After random downsampling, processed sample transformer fault data is obtained. The newly generated target transformer fault data is integrated with the processed sample data. The integrated data is preprocessed, and a transformer fault dataset is constructed based on the processed sample transformer fault data and the target transformer fault data. Furthermore, each sample in the transformer fault dataset can be labeled, including relevant information such as fault type and fault severity. Furthermore, the data in the transformer fault dataset is divided into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used for model selection and parameter adjustment, and the test set is used to evaluate model performance. The divided dataset is stored in an appropriate storage medium, and a dataset management and maintenance mechanism is established. The transformer fault dataset is used to train and test the fault diagnosis model.

[0055] In an embodiment of the present invention, the original sample transformer fault data is pared down to reduce data imbalance and improve efficiency, thereby enriching the diversity of the data set and including more samples of different fault conditions.

[0056] The technical solution of the embodiments of the present invention obtains sample transformer fault data, models the sample transformer fault data as a dynamic pulse width signal model based on an annihilation filtering algorithm, and determines sample pulse data based on the model parameters of the dynamic pulse width signal model. The sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data. The sample transformer fault data is modeled as a dynamic pulse width signal model of a spatial trend curve to facilitate signal processing, feature extraction, and reconstruction. Target pulse data is generated based on the sample pulse data and a pre-trained pulse parameter generation model, and target transformer fault data is reconstructed based on the target pulse data and the dynamic pulse width signal model. New target transformer fault data is rapidly generated using the sample pulse data and the pulse parameter generation model. A transformer fault dataset is determined based on the sample transformer fault data and the target transformer fault data. Rapidly generating a transformer fault dataset based on the sample transformer fault data and the target transformer fault data solves the problem of insufficient transformer fault data and achieves the beneficial effects of improving the efficiency of determining and expanding transformer fault datasets.

[0057] Example 2

[0058] FIG2 is a flow chart of a method for determining a transformer fault data set provided by a second embodiment of the present invention. This embodiment is a further refinement of the method for generating target pulse data based on the sample pulse data and a pre-trained pulse parameter generation model in the above embodiment. Optionally, before generating target pulse data based on the sample pulse data and the pre-trained pulse parameter generation model, the method further includes: training the constructed initial pulse parameter generation model using a pre-defined training objective function, calculating the loss function using a back-propagation algorithm during the training process, and updating the model parameters using an optimizer until a preset number of training times is reached; after the training is completed, evaluating the trained model to obtain a model evaluation index, and determining the target pulse parameter generation model based on at least one of the model evaluation indicators.

[0059] like Figure 2a As shown, the method includes:

[0060] S210. Obtain sample transformer fault data, model the sample transformer fault data as a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data.

[0061] S220. The constructed initial pulse parameter generation model is trained using a predefined training objective function. During the training process, the loss function is calculated using a back-propagation algorithm, and the model parameters are updated using an optimizer until a preset number of training times is reached.

[0062] Specifically, the sample transformer fault data is divided into a training set and a test set, and a training objective function is defined, which is used to measure the difference between the pulse parameters generated by the model and the expected pulse parameters. The training objective function is used to calculate the error between the model prediction value and the true value. Before training, the parameters of the model are initialized. The initial pulse parameter generation model is trained based on the training set. In each training process, the error between the pulse parameters output by the model and the expected pulse parameters is calculated through the training objective function (loss function). The model parameters are updated based on the error value. The backpropagation algorithm is used to calculate the gradient of the loss function with respect to the model parameters. After calculating the gradient, the optimizer is used to update the parameters of the model until the preset number of training times is reached.

[0063] Optionally, the training objective function is expressed as follows:

[0064] J CVAE =E Z～Q [lnP(X|Z,Y)]-D KL [Q(Z|X,Y)||P(Z)];

[0065]

[0066] Among them, E Z～Q [] represents sampling of the transient feature vector Z according to the distribution Q and calculating the expected value under the sampling, X represents the sample pulse data, Z represents the transient feature vector, Y represents the hot encoding coefficient of the sample label, P(Z) is the prior probability distribution of the transient feature vector Z, P(X|Z) represents the posterior probability distribution of the output target pulse data when the transient feature vector Z is input, D KL Represents the KL divergence formula.

[0067] Exemplarily, the dimension of the inference network input layer of the CVAE after training is set to K, the output of the hot encoder is 7, the number of intermediate neuron layer nodes of the generation network and the inference network is set to 156, the number of feedforward (post) neuron layer nodes is set to 24, the number of post (feedforward) neuron nodes is 48, and the dimension of the transient feature vector is 18.

[0068] S230. After the training is completed, the trained model is evaluated to obtain a model evaluation index, and a target pulse parameter generation model is determined based on at least one of the model evaluation indicators.

[0069] Specifically, after the training is completed, the trained model is evaluated based on the test set to obtain a model evaluation index, and the model with the best model evaluation index is determined as the target pulse parameter generation model.

[0070] S240 , generating target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstructing target transformer fault data based on the target pulse data and the dynamic pulse width signal model.

[0071] S250: Determine a transformer fault data set based on the sample transformer fault data and the target transformer fault data.

[0072] The technical solution of an embodiment of the present invention trains an initial pulse parameter generation model using a predefined training objective function. During the training process, a loss function is calculated using a backpropagation algorithm, and model parameters are updated using an optimizer until a preset number of training cycles is reached. After training is complete, the trained model is evaluated to obtain model evaluation metrics. A target pulse parameter generation model is determined based on at least one of these model evaluation metrics. The best-performing model is selected as the target pulse parameter generation model. This model can be used in subsequent pulse parameter generation or transformer fault diagnosis tasks, thereby improving the efficiency of transformer fault diagnosis.

[0073] As an optional example of an embodiment of the present invention, the method for determining a transformer fault data set in this embodiment specifically includes the following steps:

[0074] Step 1: First, use the annihilation filtering algorithm to model the sample transformer fault data into a dynamic pulse width signal model, and determine the sample pulse data based on the model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data and delay data.

[0075] Step 2: Train the pulse parameter generation model.

[0076] Step 3: Generate target pulse data based on the sample pulse data and the pre-trained pulse parameter generation model.

[0077] Step 4: Reconstruct target transformer fault data based on the target pulse data and the dynamic pulse width signal model.

[0078] Step 5: The sample transformer fault data is reduced by a random downsampling method to obtain processed sample transformer fault data, and a transformer fault data set is constructed based on the processed sample transformer fault data and the target transformer fault data.

[0079] The specific step one is:

[0080] Figure 2b is a sample schematic diagram of a sample transformer fault data curve according to an optional example of a method for determining a transformer fault data set provided in the second embodiment of the present invention, such as Figure 2b As shown in the figure, the fault data curve of the sample transformer is constructed. Its main contents include: dividing the fault parameter types into parameter vector space in order, dividing the parameter amplitude characteristics into trend vector space after normalizing them according to the type, and representing the fault data curve according to the parameter and trend vector space.

[0081] The dynamic pulse width signal model is used to model the fault data curve of the sample transformer fault data. The main content is: the sample transformer fault data curve is modeled as the sum of multiple dynamic pulse width signals (dynamic-pulse-width) and model mismatch errors, that is,

[0082]

[0083] Where N is the length of the parameter vector space, the basis function f k (n) is the double pulse waveform, σ(n) is the model mismatch error, K is the number of pulses in the double pulse waveform, which is usually selected based on experience or determined by the annihilation filter algorithm, and the basis function f k (n) is a double pulse waveform, which can be divided into:

[0084]

[0085] in, Represents the symmetrical curve in the double pulse waveform, Indicates the asymmetric curve in the double pulse waveform, c k Represents symmetrical amplitude data, r k Indicates pulse width data, d k Indicates asymmetric amplitude data, n k Indicates delay data.

[0086] Due to the expansibility of the period of the double-pulse waveform, ignoring the matching error, the Fourier coefficients of the pulse width signal model of the fault data can be expressed as:

[0087]

[0088] Among them, v k is the amplitude sequence coefficient required for subsequent reconstruction, u k For the subsequent reconstruction of the filter roots required, the parameters of the model can also be expressed as:

[0089] Among them, the main contents of the annihilation filtering algorithm are:

[0090] After obtaining discrete samples of the fault data curve, the fast Fourier transform is calculated on the samples to obtain the spectrum;

[0091] Constructing such an annihilation filter containing signal parameter information is as follows:

[0092]

[0093] Where A[k], k∈[0, K] are the coefficients of the filter, A[0]=1, and R+1 is the number of frequency domain poles.

[0094] Construct the Toeplitz matrix of the estimated annihilation filter coefficient and combine the K sample spectral coefficients to obtain the pulse width r in the dynamic perception coefficient by calculating the root of the filter k and delay n k .

[0095] Construct the Vandermonde matrix and combine the sample M spectral coefficients to obtain the symmetrical amplitude c in the dynamic perception coefficient by calculating the amplitude sequence k , asymmetric amplitude d k .

[0096] The Toeplitz matrix is:

[0097]

[0098] Where G[M] is the Fourier coefficient of the discrete sample of the fault data curve, M=2K.

[0099] The pulse width r in the dynamic perception coefficient is obtained by calculating the root of the filter: k and delay n k .

[0100]

[0101] A'=(T T' T') -1 T T G;

[0102]

[0103] Among them, u k is the root of filter A, which can be calculated by the least squares method.

[0104] (4.3) Construct the Vandermonde matrix and combine the sample M spectral coefficients to obtain the symmetric amplitude c in the dynamic perception coefficient by calculating the amplitude sequence k , asymmetric amplitude d k ,include:

[0105] Construct the Vandermonde matrix U:

[0106]

[0107] The symmetrical amplitude c in the target pulse data is obtained by calculating the amplitude sequence using the following formula k , asymmetric amplitude d k .

[0108] V=(U T U) -1 U T G;

[0109]

[0110] The specific step two is:

[0111] Construct a pulse parameter generation model: inference network, transient space, generation network, and hot encoder.

[0112] The inference network includes a first input layer, a first feedforward neuron layer connected to the first input layer, a first intermediate neuron layer connected to the first feedforward neuron layer, a first post-neuron layer connected to the first neuron layer, and a first output layer connected to the first post-neuron layer;

[0113] The generation network includes a second input layer, a second feedforward neuron layer connected to the second input layer, a second intermediate neuron layer connected to the second feedforward neuron layer, a second post-neuron layer connected to the second neuron layer, and a second output layer connected to the second post-neuron layer;

[0114] The transient space is connected to the first output layer and the second output layer respectively, and the output layer of the thermal encoder is connected to the first intermediate neuron layer, the first post neuron layer, the second intermediate neuron layer and the second post neuron layer respectively.

[0115] In this embodiment of the present invention, the prior probability distribution of the transient feature vector Z is assumed to follow a Gaussian distribution. Specifically, the inference network learns the distribution parameters of the transient feature vector Z: mean μ and standard deviation σ. The inference network can be represented as Q(Z|X), which is the posterior distribution of the transient feature vector Z represented by the known input sample X.

[0116] μ=E μ (X);

[0117] σ=E σ (X);

[0118] Z=[μ,σ];

[0119] When the generative network obtains the prior probability distribution P(Z) of the transient feature vector Z, it restores the reconstructed sample of the input sample X. The process of generating the network can be expressed as:

[0120]

[0121] is the prior probability distribution of the output, P(X|Z) is the posterior probability distribution of the output target pulse data represented by the known input transient eigenvector Z, and P(Z) is the prior probability distribution of the transient eigenvector Z.

[0122] The training objective function of the initial pulse parameter generation model is obtained as follows:

[0123] J CVAE =E Z～Q [ln P(X|Z,Y)]-D KL [Q(Z|X,Y)||P(Z)];

[0124]

[0125] Among them, E Z～Q [] represents sampling of the transient feature vector Z according to the distribution Q and calculating the expected value under the sampling, X represents the sample pulse data, Z represents the transient feature vector, Y represents the hot encoding coefficient of the sample label, P(Z) is the prior probability distribution of the transient feature vector Z, P(X|Z) represents the posterior probability distribution of the output target pulse data when the transient feature vector Z is input, D KL Represents the KL divergence formula.

[0126] The specific step five is: once all target pulse data are estimated, they can be substituted into the model The target transformer fault data is reconstructed using an expression. A random undersampling algorithm is used to reduce the data in the first normal sample. Finally, a new dataset is generated by merging the generated fault samples with the corrected normal samples. The random undersampling method arranges the normal samples according to their overall trend based on parameter type, randomly reduces the values of the corresponding samples, and finally recombines them to generate new normal samples.

[0127] The technical solution of the embodiment of the present invention models the sample transformer fault data as a spatial trend curve, which is defined as a signal processing feature extraction and reconstruction problem to quickly generate target transformer fault data. By finding the optimal and simplest feature representation to achieve a target transformer fault data generation result with high computational efficiency, the overall transformer fault data samples are balanced to improve the disadvantage of uneven data set.

[0128] Example 3

[0129] Figure 3 This is a schematic diagram of a system for determining a transformer fault data set according to the third embodiment of the present invention. Figure 3As shown, the system includes: a sample pulse data determination module 310 , a data reconstruction module 320 and a data set determination module 330 .

[0130] Among them, the sample pulse data determination module 310 is used to obtain sample transformer fault data, model the sample transformer fault data as a dynamic pulse width signal model based on the annihilation filtering algorithm, and determine the sample pulse data based on the model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data and time delay data; the data reconstruction module 320 is used to generate target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstruct target transformer fault data based on the target pulse data; the data set determination module 330 is used to determine the transformer fault data set based on the sample transformer fault data and the target transformer fault data.

[0131] The technical solution of the embodiment of the present invention uses a sample pulse data determination module to obtain sample transformer fault data. The sample transformer fault data is modeled as a dynamic pulse width signal model based on an annihilation filtering algorithm. Sample pulse data is determined based on the model parameters of the dynamic pulse width signal model. The sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data. The sample transformer fault data is modeled as a dynamic pulse width signal model with a spatial trend curve to facilitate signal processing, feature extraction, and reconstruction. A data reconstruction module generates target pulse data based on the sample pulse data and a pre-trained pulse parameter generation model. The target transformer fault data is reconstructed based on the target pulse data and the dynamic pulse width signal model. New target transformer fault data is rapidly generated using the sample pulse data and the pulse parameter generation model. A data set determination module determines a transformer fault data set based on the sample transformer fault data and the target transformer fault data. Rapidly generating a transformer fault set based on the sample transformer fault data and the target transformer fault data solves the problem of insufficient transformer fault data and achieves the beneficial effects of improving the efficiency of determining and expanding transformer fault data sets.

[0132] Optionally, the sample pulse data determination module is specifically configured to:

[0133] The pulse width signal model is expressed by the following formula:

[0134]

[0135] Where N is the length of the parameter vector space, the basis function f k (n) is the double-pulse waveform, σ(n) is the model mismatch error, and K is the number of pulses in the double-pulse waveform.

[0136] Optionally, the sample pulse data determination module is specifically configured to:

[0137] The double pulse waveform is expressed as follows:

[0138]

[0139] in, Represents the symmetrical curve in the double pulse waveform, Indicates the asymmetric curve in the double pulse waveform, c k Represents symmetrical amplitude data, r k Indicates pulse width data, d k Indicates asymmetric amplitude data, n k Indicates delay data.

[0140] Optionally, the pulse parameter generation model includes an inference network, a transient space, a generation network and a thermal encoder, the transient space is respectively connected to the inference network and the generation network, the thermal encoder is respectively connected to the inference network and the generation network, and the input of the inference network is the target pulse data.

[0141] Optionally, the inference network includes a first input layer, a first feedforward neuron layer connected to the first input layer, a first intermediate neuron layer connected to the first feedforward neuron layer, a first post-neuron layer connected to the first neuron layer, and a first output layer connected to the first post-neuron layer;

[0142] The generation network includes a second input layer, a second feedforward neuron layer connected to the second input layer, a second intermediate neuron layer connected to the second feedforward neuron layer, a second post-neuron layer connected to the second neuron layer, and a second output layer connected to the second post-neuron layer;

[0143] The transient space is connected to the first output layer and the second output layer respectively, and the output layer of the thermal encoder is connected to the first intermediate neuron layer, the first post neuron layer, the second intermediate neuron layer and the second post neuron layer respectively.

[0144] Optionally, the data reconstruction module includes:

[0145] a feature vector acquisition unit, configured to input the sample pulse data into the inference network to obtain a transient feature vector converted based on the sample pulse data, and send the transient feature vector to the generation network through a transient space;

[0146] an encoding unit, configured to input a sample label corresponding to the sample pulse data into the hot encoder to obtain a hot encoding coefficient corresponding to the sample label encoding, and send the hot encoding coefficient to the generating network;

[0147] A data generating unit is used for the generation network to generate target pulse data based on the received transient feature vector and the hot encoding coefficient.

[0148] Optionally, the system further includes a training module and a model determination module.

[0149] The training module is configured to train the constructed initial pulse parameter generation model using a predefined training objective function before generating target pulse data based on the sample pulse data and the pre-trained pulse parameter generation model. During the training process, the loss function is calculated using a back-propagation algorithm, and the model parameters are updated using an optimizer until a preset number of training times is reached.

[0150] The model determination module is used to evaluate the trained model after training to obtain a model evaluation index, and determine a target pulse parameter generation model based on at least one of the model evaluation indicators.

[0151] Optionally, the training objective function is expressed as follows:

[0152] J CVAE =E Z～Q [ln P(X|Z,Y)]-D KL [Q(Z|X,Y)||P(Z)];

[0153]

[0154] Among them, E Z～Q [] represents sampling of the transient feature vector Z according to the distribution Q and calculating the expected value under the sampling, X represents the sample pulse data, Z represents the transient feature vector, Y represents the hot encoding coefficient of the sample label, P(Z) is the prior probability distribution of the transient feature vector Z, P(X|Z) represents the posterior probability distribution of the output target pulse data when the transient feature vector Z is input, D KL Represents the KL divergence formula.

[0155] Optionally, the data set determination module is specifically configured to:

[0156] The sample transformer fault data is reduced based on a random downsampling method to obtain processed sample transformer fault data, and a transformer fault data set is constructed based on the processed sample transformer fault data and the target transformer fault data.

[0157] The system for determining a transformer fault data set provided by the embodiment of the present invention can execute the method for determining a transformer fault data set provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0158] Example 4

[0159] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0160] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0161] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0162] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining a transformer fault dataset.

[0163] In some embodiments, the method for determining a transformer fault dataset can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining a transformer fault dataset described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining a transformer fault dataset in any other suitable manner (e.g., via firmware).

[0164] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0166] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0169] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0170] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0171] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for determining a transformer fault data set, characterized in that: include: Acquire sample transformer fault data, model the sample transformer fault data into a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data; generating target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstructing target transformer fault data based on the target pulse data and the dynamic pulse width signal model; determining a transformer fault data set based on the sample transformer fault data and the target transformer fault data; The pulse width signal model is expressed by the following formula: ； in, is the length of the parameter vector space, the basis function It is a double pulse waveform. is the model mismatch error, is the number of pulses in the double-pulse waveform; The double pulse waveform is expressed as follows: ； ； ； in, Represents the symmetrical curve in the double pulse waveform, Represents the asymmetric curve in the double pulse waveform, represents symmetrical amplitude data, Indicates pulse width data, represents asymmetric amplitude data, Indicates delay data.

2. The method according to claim 1, characterized in that The pulse parameter generation model includes an inference network, a transient space, a generation network and a thermal encoder, the transient space is respectively connected to the inference network and the generation network, the thermal encoder is respectively connected to the inference network and the generation network, and the input of the inference network is the target pulse data.

3. The method according to claim 2, characterized in that The inference network includes a first input layer, a first feedforward neuron layer connected to the first input layer, a first intermediate neuron layer connected to the first feedforward neuron layer, a first post-neuron layer connected to the first feedforward neuron layer, and a first output layer connected to the first post-neuron layer; The generation network includes a second input layer, a second feedforward neuron layer connected to the second input layer, a second intermediate neuron layer connected to the second feedforward neuron layer, a second post-neuron layer connected to the second feedforward neuron layer, and a second output layer connected to the second post-neuron layer; The transient space is connected to the first output layer and the second output layer respectively, and the output layer of the thermal encoder is connected to the first intermediate neuron layer, the first post neuron layer, the second intermediate neuron layer and the second post neuron layer respectively.

4. The method according to claim 3, characterized in that Generating target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model includes: Inputting the sample pulse data into the inference network to obtain a transient feature vector converted based on the sample pulse data, and sending the transient feature vector to the generation network through a transient space; Inputting the sample label corresponding to the sample pulse data into the hot encoder to obtain a hot encoding coefficient corresponding to the sample label encoding, and sending the hot encoding coefficient to the generating network; The generation network generates target pulse data based on the received transient feature vector and the hot encoding coefficient.

5. The method according to claim 1, wherein Before generating target pulse data according to the sample pulse data and the pre-trained pulse parameter generation model, the method further includes: The constructed initial pulse parameter generation model is trained using a predefined training objective function. During the training process, the loss function is calculated using the backpropagation algorithm, and the model parameters are updated using the optimizer until the preset number of training times is reached. After the training is completed, the trained model is evaluated to obtain a model evaluation index, and a target pulse parameter generation model is determined based on at least one of the model evaluation indicators.

6. The method according to claim 5, characterized in that The training objective function is expressed as follows: ； ； in, Represents the transient eigenvector Sampling is performed according to distribution Q, and the expected value under the sampling is calculated. represents sample pulse data, represents the transient eigenvector, represents the hot encoding coefficient of the sample label, is the transient eigenvector The prior probability distribution of It represents the posterior probability distribution of the output target pulse data when the transient eigenvector Z is input. express Divergence formula.

7. The method according to claim 1, characterized in that The determining of a transformer fault data set based on the sample transformer fault data and the target transformer fault data includes: The sample transformer fault data is reduced based on a random downsampling method to obtain processed sample transformer fault data, and a transformer fault data set is constructed based on the processed sample transformer fault data and the target transformer fault data.

8. A system for determining a transformer fault data set, characterized in that: include: a sample pulse data determination module, configured to obtain sample transformer fault data, model the sample transformer fault data as a dynamic pulse width signal model based on an annihilation filtering algorithm, and determine sample pulse data based on model parameters of the dynamic pulse width signal model, wherein the sample pulse data includes symmetrical amplitude data, pulse width data, asymmetrical amplitude data, and time delay data; a data reconstruction module, configured to generate target pulse data according to the sample pulse data and a pre-trained pulse parameter generation model, and reconstruct target transformer fault data based on the target pulse data; a data set determining module, configured to determine a transformer fault data set based on the sample transformer fault data and the target transformer fault data; The sample pulse data determination module is specifically used to: The pulse width signal model is expressed by the following formula: ； in, is the length of the parameter vector space, the basis function It is a double pulse waveform. is the model mismatch error, is the number of pulses in the double-pulse waveform; The sample pulse data determination module is specifically used to: The double pulse waveform is expressed as follows: ； ； ； in, Represents the symmetrical curve in the double pulse waveform, Represents the asymmetric curve in the double pulse waveform, represents symmetrical amplitude data, Indicates pulse width data, represents asymmetric amplitude data, Indicates delay data.

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