CN113505876A - High-voltage circuit breaker fault diagnosis method based on generation type countermeasure network - Google Patents
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Abstract
The invention discloses a high-voltage circuit breaker fault diagnosis method based on a generative countermeasure network, which comprises the following steps: collecting actual fault data and carrying out data preprocessing; constructing a generative confrontation network model; inputting random noise into a generation model, and training to obtain generation data; inputting the actual fault data and the generated data into a discrimination model for discrimination; inputting data to train a generative confrontation network until a set discrimination threshold is reached; and fusing the actual fault data and the generated data to form a fault database. According to the technical scheme, the fault data are generated by using the generation type countermeasure network, the fault database is enriched, and meanwhile, the generated fault data are identified, whether the data are abnormal or missing is judged, so that the high quality of the database data is ensured, the requirement of high-voltage circuit breaker fault identification based on machine learning on the high-quality data is met, and the accuracy of a diagnosis result is greatly improved.
Description
Technical Field
The invention relates to the technical field of breaker diagnosis, in particular to a high-voltage breaker fault diagnosis method based on a generative countermeasure network.
Background
It is well known that circuit breakers are important devices in high voltage networks, mainly for controlling and protecting the devices. With the increase of the service time, certain parts of the circuit breaker can be continuously aged or damaged, such as the abrasion of a mechanical mechanism, the abrasion of a contact, the failure of a secondary opening and closing mechanism and the like. These problems can cause the circuit breaker to malfunction and even affect the safe operation of the entire grid. Therefore, it is important to periodically check the performance of the circuit breaker and evaluate its operating state.
The high-voltage circuit breaker is monitored in real time and diagnosed in fault, the running state of the circuit breaker can be grasped in real time, a reasonable maintenance plan is formulated according to the condition, and the method has important significance for guaranteeing safe running of a power grid. According to the IEEE recommendations for selecting breaker monitoring objects, the most common state quantities are: the circuit breaker comprises a circuit breaker moving contact stroke, a circuit breaker contact and ambient temperature, an opening and closing state, an opening and closing coil current, an energy storage motor current and circuit breaker mechanical vibration. The fault diagnosis of the common circuit breaker can be completed by collecting, feature extracting and classifying the parameters. In actual operation, however, fault data is extremely difficult to obtain. The circuit breaker simulation fault experiment can obtain high-quality data, but the experiment cost is higher; the simulation software can acquire fault simulation data, but the actual value and the simulation value have obvious errors, so that the acquisition of accurate data for realizing diagnosis becomes important.
Chinese patent document CN108828441A discloses a "fault diagnosis method for high-voltage circuit breaker". Adopts the following steps: 1) collecting the current of a switching-on and switching-off coil of a high-voltage circuit breaker to form a data set; 2) coding is carried out aiming at different state types of the related circuit breaker, data are randomly extracted according to a proportion to be respectively used as a pre-training sample, a parameter fine-tuning sample and a test sample, and normalization processing is carried out; 3) establishing a DBN classification network model, respectively using unlabeled pre-training data samples to perform network pre-training, and using labeled samples to perform parameter fine-tuning; 4) and (4) carrying out fault diagnosis on the high-voltage circuit breaker by using the trained DBN. According to the technical scheme, the fault simulation data can be obtained, but obvious errors exist between the fault simulation data and an actual fault value, and the accuracy of a diagnosis result is difficult to guarantee.
Disclosure of Invention
The invention mainly solves the technical problems that the fault simulation data acquired by the original technical scheme have obvious errors with the actual fault value and the fault diagnosis is realized due to lack of accurate data, and provides a high-voltage circuit breaker fault diagnosis method based on a generative countermeasure network.
The technical problem of the invention is mainly solved by the following technical scheme: the invention comprises the following steps:
s1, collecting actual fault data and carrying out data preprocessing;
s2, constructing a generative confrontation network model;
s3, inputting random noise into the generation model, and training to obtain generation data;
s4, inputting the actual fault data and the generated data into a discrimination model for discrimination;
s5, inputting data to train a generative confrontation network until a set discrimination threshold is reached;
s6 merges the actual fault data and the generated data to form a fault database.
Preferably, the step S1 of preprocessing the data includes integrating and deleting missing and repeated data, performing normalization processing and feature selection on the data set, and implementing classification of fault data, so as to accelerate the convergence speed of the sample generation model and reduce the data dimension.
Preferably, the generative countermeasure network model of step S2 includes a generative model for generating data and a discriminant model for determining whether the generated data is true or false and generating a data type, and the generative model and the discriminant model are mutually game-learned and are mutually exchanged and compared with the data of the fault database.
Preferably, the generated model is formed by a depth transposition convolution network, and the fault sample generation model is trained through the sampled real fault samples to learn the distribution of the real fault samples.
Preferably, the discrimination model is composed of a deep neural network, and the discrimination model includes a classifier for determining authenticity of data. And training an evaluation model by using the real fault sample to select the generated sample, wherein the generated fault sample used for training the fault diagnosis model is close to the distribution of the real sample so as to evaluate the quality of the generated sample.
Preferably, in step S4, the difference between the real fault data and the generated fault data is identified and distinguished by using a two-stage authenticity classifier, the output value is a binary number 0 or 1, if the output value is 0, the data is false data, if the output value is 1, the data is true data, and the generation model and the discriminant model are trained by using a large amount of data, so that the output value of the discriminant model after training is closer to 1.
Preferably, the first layer of the generated model is a fully-connected layer with a size of 6144, and the subsequent four layers are three-dimensional transposed convolutional layers, wherein the size of a convolutional kernel is 2 × 5 × 5, namely, time × width × height, and the transposed convolutional layer operation is used in the generated model. The use of transposed convolutional layer operations is considered to be a "reverse" propagation process of conventional convolutional operations.
Preferably, the discriminant model uses a convolutional neural network, the first four layers of the discriminant model are three-dimensional convolutional layers for extracting spatio-temporal features of data, followed by fully-connected layers, and the convolutional layers have a convolutional kernel size of 2 × 5 × 5, i.e., time × width × height.
Preferably, in step S3, the Wasserstein distance calculation formula is defined as the loss function of the Wasserstein distance optimization composite network model to stabilize the training process
Wherein, T1For distribution of true fault data obeys, T2Is the distribution, II (T), to which the generated data obeys1，T2) Is T1And T2A set of all joint distributions combined, γ being one of the joint distributions, (x, y) being a set of samples in γ, E(x，y)～γ[‖x-y‖]Is the expected value of the sample distance.
Preferably, in step S5, if it is determined that the true probability of the set of input data is greater than the set threshold, the set of input data is determined to be valid. The training aims to enable the discrimination truth rate of the generated confrontation network model to reach a set threshold value, alternately optimize and learn the generated model and the discrimination model, and finally achieve the zero-sum game.
The invention has the beneficial effects that: the fault data are generated by using the generative countermeasure network, so that a fault database is enriched, and the generated fault data are identified at the same time to judge whether the data are abnormal or missing, so that the high quality of the database data is ensured, the requirement of machine learning-based high-voltage circuit breaker fault identification on the high-quality data is met, and the accuracy of a diagnosis result is greatly improved.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings. Example (b): the method for diagnosing the fault of the high-voltage circuit breaker based on the generative countermeasure network in the embodiment is shown in fig. 1, and comprises the following steps:
s1, collecting actual fault data and preprocessing the data, integrating and deleting missing and repeated data, and performing normalization processing and feature selection on a data set to realize classification of the fault data, so that the convergence speed of a sample generation model is increased, and the data dimensionality is reduced.
S2, constructing a generative confrontation network model, wherein the generative confrontation network model comprises a generative model for generating data and a discriminant model for judging whether the generated data is true or false and generating data categories, and the generative model and the discriminant model are mutually game-learned and are mutually exchanged and compared with the data of the fault database. The generation model is composed of a depth transposition convolution network, and the distribution of real fault samples is learned by training the fault sample generation model through the sampled real fault samples. The first layer of the generated model is a fully connected layer of 6144 size, the subsequent four layers are three-dimensional transposed convolutional layers, where the convolutional kernel size is 2 × 5 × 5, i.e., time × width × height, and the transposed convolutional layers are used in the generated model. The use of transposed convolutional layer operations is considered to be a "reverse" propagation process of conventional convolutional operations.
The discrimination model is composed of a deep neural network, and the discrimination model comprises a classifier for judging the authenticity of data. And training an evaluation model by using the real fault sample to select the generated sample, wherein the generated fault sample used for training the fault diagnosis model is close to the distribution of the real sample so as to evaluate the quality of the generated sample. The discriminative model uses a convolutional neural network, the first four layers of which are three-dimensional convolutional layers for extracting spatio-temporal features of data, followed by fully-connected layers, the convolutional layers having convolutional kernel sizes of 2 × 5 × 5, i.e., time × width × height.
S3, inputting random noise into the generation model, and training to obtain generation data. Using Wasserstein distance to optimize the loss function of the composite network model to stabilize the training process, and defining Wasserstein distance calculation formula as
Wherein, T1For distribution of true fault data obeys, T2Is the distribution, II (T), to which the generated data obeys1，T2) Is T1And T2A set of all joint distributions combined, γ being one of the joint distributions, (x, y) being a set of samples in γ, E(x，y)～γ[‖x-y‖]Is the expected value of the sample distance.
S4 inputs the actual failure data and the generated data to the discrimination model to discriminate the authenticity. And (3) identifying and distinguishing the difference between the real fault data and the generated fault data by using a true-false two-classifier, wherein the output value is binary number 0 or 1, if the output value is 0, the data is false data, if the output value is 1, the data is true data, and a large amount of data is used for training the generation model and the discrimination model, so that the output value of the trained discrimination model is closer to 1.
And S5, training the generative confrontation network by the input data until reaching a set discrimination threshold, and if the true probability of the set of input data is judged to be greater than the set threshold, judging the set of input data to be valid. The training aims to enable the discrimination truth rate of the generated confrontation network model to reach a set threshold value, alternately optimize and learn the generated model and the discrimination model, and finally achieve the zero-sum game.
S6 merges the actual fault data and the generated data to form a fault database.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although terms such as generative confrontation network models are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (10)
1. A high-voltage circuit breaker fault diagnosis method based on a generative countermeasure network is characterized by comprising the following steps:
s1, collecting actual fault data and carrying out data preprocessing;
s2, constructing a generative confrontation network model;
s3, inputting random noise into the generation model, and training to obtain generation data;
s4, inputting the actual fault data and the generated data into a discrimination model for discrimination;
s5, inputting data to train a generative confrontation network until a set discrimination threshold is reached;
s6 merges the actual fault data and the generated data to form a fault database.
2. The method as claimed in claim 1, wherein the step S1 of preprocessing data includes performing normalization and feature selection on the data set by deleting missing and repeated data, so as to classify fault data, so as to accelerate convergence speed of the sample generation model and reduce data dimensionality.
3. The method as claimed in claim 1, wherein the generative countermeasure network model of step S2 includes a generative model for generating data and a discriminant model for determining whether the generated data is true or false and generating data category, and the generative model and the discriminant model are game-learned and are in mutual exchange comparison with data of the fault database.
4. The method as claimed in claim 3, wherein the generative model is composed of a depth transposition convolution network, and the distribution of real fault samples is learned by training the fault sample generative model with the sampled real fault samples.
5. The method as claimed in claim 1, wherein the discriminant model is composed of a deep neural network, and the discriminant model includes a classifier for determining authenticity of data.
6. The method as claimed in claim 1, wherein the step S4 is to use a true-false classifier to distinguish the difference between real fault data and generated fault data, the output value is binary 0 or 1, if the output value is 0, the data is false data, if the output value is 1, the data is true data, and the large amount of data is used to train the generation model and the discriminant model, so that the output value of the discriminant model after training is closer to 1.
7. The method as claimed in claim 4, wherein the first layer of the generated model is a 6144 full-connected layer, the four subsequent layers are three-dimensional transposed convolutional layers, the convolutional kernel size is 2 × 5 × 5, i.e. time × width × height, and the transposed convolutional layer operation is used in the generated model.
8. The method as claimed in claim 5, wherein the discriminant model uses convolutional neural network, the first four layers of the discriminant model are three-dimensional convolutional layers for extracting spatio-temporal features of data, and then fully-connected layers, and the convolutional layers have convolutional kernel size of 2 x 5, i.e. time x width x height.
9. The method as claimed in claim 1, wherein the step S3 uses a loss function of a Wasserstein distance optimization composite network model to stabilize the training process, and the Wasserstein distance calculation formula is defined as
Wherein, T1For distribution of true fault data obeys, T2Is the distribution, II (T), to which the generated data obeys1，T2) Is T1And T2A set of all joint distributions combined, γ being one of the joint distributions, (x, y) being a set of samples in γ, E(x，y)～γ[‖x-y‖]Is the expected value of the sample distance.
10. The method as claimed in claim 1, wherein the step S5 is performed if the true probability of the set of input data is greater than a predetermined threshold, then the set of input data is determined to be valid.
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Application publication date: 20211015 |