CN118040679B - Disturbance and fault identification method for in-phase power supply system - Google Patents

Disturbance and fault identification method for in-phase power supply system Download PDF

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CN118040679B
CN118040679B CN202410430549.8A CN202410430549A CN118040679B CN 118040679 B CN118040679 B CN 118040679B CN 202410430549 A CN202410430549 A CN 202410430549A CN 118040679 B CN118040679 B CN 118040679B
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周福林
朱炳旭
曹毅峰
刘飞帆
田腾宇
祁霁舢
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Southwest Jiaotong University
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Abstract

本发明涉及同相供电系统的扰动与故障辨识技术领域，具体公开了一种同相供电系统的扰动与故障辨识方法，方法包括：构建同相供电系统暂态扰动和同相供电变流器器件故障工况的数据集；构建基于TimeNet的特征提取网络以及能够描述数据集中各类别特征的属性标签；利用构建数据集对搭建的基于TimeNet的特征提取网络进行训练，学习单个扰动或故障的特征；构建属性学习器，学习特征到属性标签的映射，实现扰动或故障类型向复合工况的知识迁移；基于属性学习器提取属性对样本类型进行预测，从而实现对同相供电系统的暂态扰动异常工况与同相供电装置变流器故障异常工况的辨识以及复合工况的辨识。对同相供电变流器运行提供指导，有利于同相供电系统的正常安全运行。

The present invention relates to the technical field of disturbance and fault identification of a co-phase power supply system, and specifically discloses a disturbance and fault identification method of a co-phase power supply system, the method comprising: constructing a data set of transient disturbances of the co-phase power supply system and fault conditions of co-phase power supply converter components; constructing a feature extraction network based on TimeNet and attribute labels that can describe the characteristics of each category in the data set; using the constructed data set to train the constructed feature extraction network based on TimeNet to learn the characteristics of a single disturbance or fault; constructing an attribute learner to learn the mapping from features to attribute labels, and realizing the knowledge transfer from disturbance or fault types to complex conditions; extracting attributes based on the attribute learner to predict sample types, thereby realizing the identification of transient disturbance abnormal conditions of the co-phase power supply system and abnormal fault conditions of the co-phase power supply device converter and the identification of complex conditions. Providing guidance for the operation of the co-phase power supply converter is conducive to the normal and safe operation of the co-phase power supply system.

Description

Disturbance and fault identification method for in-phase power supply system
Technical Field
The invention relates to the technical field of disturbance and fault identification of an in-phase power supply system, in particular to a disturbance and fault identification method of the in-phase power supply system.
Background
The in-phase power supply system realizes in-phase power supply and negative sequence, reactive power and partial harmonic compensation by using the in-phase power supply technical device, thereby solving the problem of electric split phase and improving the working efficiency and the working stability of the traction power supply system. The core equipment of the in-phase power supply device is an in-phase power supply converter, and stable and reliable operation is a precondition of normal operation of an in-phase power supply system. The reliability of the power electronic device is low, and the power electronic device belongs to a weak link of an in-phase power supply system. On the other hand, in the train operation process, the abnormal electrical phenomenon of the original train network coupling is likely to cause the abnormal working condition of the in-phase power supply device, and according to the actual measurement data of the in-phase power supply device of a certain power supply section, when a large excitation surge current occurs at the network side, a large direct current component can cause the bias of a matching transformer, so that magnetic saturation occurs at the zero crossing point of the voltage of the matching transformer, and further, the abnormal triggering of the output current of the converter is caused to trigger the over-current protection.
Aiming at the abnormal working conditions caused by the vehicle network coupling disturbance of the in-phase power supply system and the power electronic device fault, no method is available at present for simultaneously considering the vehicle network coupling disturbance and the power electronic device fault, and the direct cause of the protection action of the in-phase power supply device cannot be analyzed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a disturbance and fault identification method of an in-phase power supply system, which can simultaneously consider two main abnormal working conditions of vehicle network coupling disturbance and in-phase power supply converter power electronic device faults so as to realize the analysis of the reasons of the abnormal working conditions of the in-phase power supply device and solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: a disturbance and fault identification method of an in-phase power supply system comprises the following steps:
S1, constructing a transient disturbance and in-phase power supply device converter fault data set of a single-phase combined in-phase power supply system, and dividing the data set into a training set and a testing set;
s2, constructing a feature extraction network based on TimeNet, adding a Softmax layer at the tail end of the feature extraction network to form a classification network, training the feature extraction network by using a training set, learning the features of transient disturbance and a fault sample of the converter, retaining network parameters of the feature extraction network after training, and realizing feature extraction of the sample by using the feature extraction network after training;
S3, constructing attribute labels describing various characteristics of transient disturbance and converter faults based on current and voltage signals of different types of samples;
S4, constructing a fully-connected network as an attribute learner, and mapping learning features to attribute labels to realize knowledge migration from transient disturbance or converter fault types to composite working conditions;
S5, outputting an attribute vector based on the trained attribute learner, and predicting sample types by adopting maximum likelihood estimation, so that identification of transient disturbance abnormal working conditions of the in-phase power supply system and fault abnormal working conditions of the in-phase power supply device converter and identification of composite working conditions are realized.
Preferably, in step S1, the method specifically includes the following steps: collecting an output current signal of an in-phase power supply converter and a network side 27.5kV voltage signal in a single-phase combined in-phase power supply system, dividing the collected current signal and the collected voltage signal to form independent data samples with the same size, wherein a single data sample contains the current signal and the voltage signal in the same time period; the data samples are tensors with the size of 2 multiplied by 6400, and comprise current waveform data output by the 1s internal converter module and network side 27.5kV voltage waveform data.
Preferably, the training set does not include a composite working condition of transient disturbance of the in-phase power supply system and simultaneous occurrence of converter faults of the in-phase power supply device, namely the composite working condition is of an unobserved type;
The transient disturbance type comprises harmonic resonance, excitation surge current and grounding short circuit; the converter fault type comprises an inverter IGBT dual alpha type open circuit fault, a dual beta type open circuit fault and a non-dual double-tube open circuit fault in the back-to-back converter module.
Preferably, in step S2, the feature extraction network based on TimeNet includes a voltage channel and a current channel, each channel includes a time sequence dimension-increasing module and a feature extraction module, and performs feature extraction on the voltage and current waveforms and merges the extracted voltage and current features, where an inference process of the feature extraction network is expressed asWherein/>Represents the/>Sample number,/>Represents the/>The individual samples correspond to feature vectors.
Preferably, the time sequence dimension-increasing module is used for one-dimensional time sequenceFFT conversion is carried out to obtain the period and amplitude information of different frequency components, a period P corresponding to the frequency component with the largest amplitude is selected, signals are sheared by taking P as a unit and spliced in a first dimension, and a two-dimensional tensor/>, is obtainedAnd finishing the conversion of the one-dimensional time sequence problem into two-dimensional image recognition.
Preferably, the feature extraction module is formed by stacking a two-dimensional convolution layer, a batch normalization layer, a nonlinear activation layer and a maximum pooling layer, and adopts Inception network structure to improve generalization capability of the feature extraction module, and comprises a first Inception module, a second Inception module and a third Inception module;
The stacking sequence is a two-dimensional convolution layer, a batch normalization layer (BN layer), a nonlinear activation layer, a maximum pooling layer, a two-dimensional convolution layer, a first Inception module, a second Inception module and a third Inception module, wherein the first Inception module, the second Inception module and the third Inception module have the same structure, and each Inception module comprises the two-dimensional convolution layer, the Inception convolution layer, the batch normalization layer, the nonlinear activation layer and the maximum pooling layer.
Preferably, in step S3, the attribute tags contain direct descriptions of different types of transient and steady-state features in the sample, and the sample attribute vectors are defined manuallyThe attribute vector contains descriptions of n attributes of a single sample;
attribute vector of composite working condition XY formed by combination of disturbance type X and fault type Y Expressed as: Wherein/> And/>Attribute vectors corresponding to the disturbance type X and the fault type Y respectively;
attribute vectors of different classes form an Attribute description matrix Where k represents the total number of categories of fault category, disturbance category and compound condition category.
Preferably, in step S4, the attribute learner is formed by stacking a full connection layer, a BN layer, and a nonlinear activation layer, where the stacking order is in turn: a full connection layer, a BN layer, a nonlinear activation layer, a full connection layer, a BN layer, a nonlinear activation layer and a full connection layer;
The input of the attribute learner is the output feature vector of the feature extraction network after training Output as attribute vector/>, of corresponding sampleThe mapping relation between the features and the attribute vectors is learned in a supervised mode, and the reasoning process of the attribute learner is expressed as/>Wherein/>Represents the/>Corresponding feature vector of each sample,/>An attribute vector representing the ith sample generated by the attribute learner.
Preferably, the loss function when the attribute learner is trained is:
；
wherein the method comprises the steps of Representing the number of input feature vectors,/>Representing the artificially defined/>The attribute vector of the individual samples is used,An attribute vector representing the ith sample generated by the attribute learner.
Preferably, the maximum likelihood estimation formula is:
；
Wherein M is the number of test set categories; extracting the attribute by the expression algorithm through a feature extraction network and an attribute learner to generate an inference process of an attribute vector; /(I) Representing samples,/>Representing attribute learner outputting attribute vectors, i.e./>；/>Representing the attribute vector of the i-th sample defined manually.
The beneficial effects of the invention are as follows:
1) According to the disturbance and fault identification method for the single-phase combined type in-phase power supply system, provided by the invention, the output current of the converter of the single-phase combined type in-phase power supply system and the 27.5kV voltage on the network side are sampled and analyzed, the faults of power electronic devices in the in-phase power supply converter and the abnormal electrical phenomena outside the vehicle network coupling system can be considered at the same time, and the disturbance and fault identification for the in-phase power supply system can be realized; the invention can solve the problem that the fault diagnosis method of the current converter can not identify and classify the external disturbance and the internal power electronic device fault of the in-phase power supply device, judge the position (traction network side and in-phase power supply converter side) of the in-phase power supply system where the fault/disturbance source is located, further check the occurrence reason of abnormal working conditions, provide guidance for the operation of the in-phase power supply converter and be beneficial to the normal and safe operation of the in-phase power supply system.
2) The composite working condition is various in category and high in data set construction difficulty, and the disturbance and fault identification method provided by the invention adopts a zero sample learning method, and under the condition that no composite working condition sample exists, the corresponding semantic information of the fault characteristics is summarized through learning the voltage and current characteristics when single fault or disturbance occurs. The model can realize the identification of the composite working condition by utilizing the attribute information of the learned fault characteristics, and realize the zero sample diagnosis of the composite working condition while avoiding the huge workload of manufacturing the composite working condition data set.
Drawings
FIG. 1 is a schematic diagram of a single-phase in-phase power supply system in an embodiment of the invention;
Fig. 2 is a schematic diagram of a converter module of an in-phase power supply device according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an algorithm for identifying disturbance and fault of a single-phase combined in-phase power supply system according to an embodiment of the present invention;
FIG. 4 is a flowchart of an algorithm construction method for disturbance and fault identification of a single-phase combined in-phase power supply system according to an embodiment of the present invention;
Fig. 5 is a schematic diagram of a Inception network structure in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
A disturbance and fault identification method of an in-phase power supply system comprises the following steps:
S1, constructing a transient disturbance and in-phase power supply device converter fault data set of a single-phase combined in-phase power supply system, and dividing the data set into a training set and a testing set;
in the step S1, the method specifically comprises the steps of collecting an output current signal of an in-phase power supply converter and a network side 27.5kV voltage signal in a single-phase combined in-phase power supply system, dividing the collected current signal and the collected voltage signal to form independent data samples with the same size, wherein a single data sample contains the current signal and the voltage signal in the same time period; the data samples are tensors with the size of 2 multiplied by 6400 and comprise 1s internal converter module output current waveform data and network side 27.5kV voltage waveform data;
The complexity and the bulkiness of the required workload are established by considering a composite working condition data set, wherein the training set does not comprise a composite working condition of transient disturbance of an in-phase power supply system and simultaneous occurrence of converter faults of the in-phase power supply device, namely the composite working condition is of an unobserved type; the recognition problem under the condition of the composite working condition sample missing is solved based on the zero sample learning thought at the algorithm level, and the problems of huge category number and difficult acquisition of the composite working condition data set are solved by adopting zero sample learning.
Transient disturbance types include harmonic resonance, excitation surge current and grounding short circuit; the converter fault type comprises an inverter IGBT dual alpha type open circuit fault, a dual beta type open circuit fault and a non-dual double-tube open circuit fault in the back-to-back converter module;
s2, constructing a feature extraction network based on TimeNet, adding a Softmax layer at the tail end of the feature extraction network to form a classification network, training the feature extraction network by using a training set, learning the features of transient disturbance and a fault sample of the converter, retaining network parameters of the feature extraction network after training, and realizing feature extraction of the sample by using the feature extraction network after training;
The TimeNet-based feature extraction network comprises a voltage channel and a current channel, each channel comprises a time sequence dimension-increasing module and a feature extraction module, the voltage and current waveforms are respectively subjected to feature extraction, and the extracted voltage and current features are combined, wherein the reasoning process of the feature extraction network is expressed as follows
Wherein/>Represents the/>Sample number,/>Represents the/>The individual samples correspond to feature vectors;
The time sequence dimension-increasing module is used for one-dimensional time sequence FFT conversion is carried out to obtain the period and amplitude information of different frequency components, a period P corresponding to the frequency component with the largest amplitude is selected, signals are sheared by taking P as a unit and spliced in a first dimension, and a two-dimensional tensor/>, is obtained. Therefore, the one-dimensional time sequence problem is converted into two-dimensional image recognition without loss;
The feature extraction module is formed by stacking a two-dimensional convolution layer, a batch normalization layer, a nonlinear activation layer and a maximum pooling layer, improves the generalization capability of the feature extraction module by adopting a Inception network structure, and comprises a first Inception module, a second Inception module and a third Inception module;
The stacking sequence is a two-dimensional convolution layer, a batch normalization layer (BN layer), a nonlinear activation layer, a maximum pooling layer, a two-dimensional convolution layer, a first Inception module, a second Inception module and a third Inception module, wherein the first Inception module, the second Inception module and the third Inception module have the same structure, and each Inception module comprises the two-dimensional convolution layer, the Inception convolution layer, the batch normalization layer, the nonlinear activation layer and the maximum pooling layer.
S3, constructing attribute labels describing various characteristics of transient disturbance and converter faults based on current and voltage signals of different types of samples; the attribute tag comprises direct description of different types of transient and steady state characteristics in the sample, and sample attribute vector is defined manuallyThe attribute vector contains descriptions of n attributes of a single sample. Attribute vector/>Intermediate element/>Representing whether the voltage-current waveform signal contains a representation of a characteristic,/>The value is 0 or 1.
The attribute label of the composite working condition where the fault and the disturbance occur simultaneously can be constructed by the mode that the fault attribute label and the disturbance attribute label are combined.
Attribute vector of composite working condition XY formed by combination of disturbance type X and fault type YExpressed as: Wherein/> And/>Attribute vectors corresponding to the disturbance type X and the fault type Y respectively;
attribute vectors of different classes form an Attribute description matrix Where k represents the total number of categories of fault category, disturbance category and compound condition category.
S4, constructing a fully-connected network as an attribute learner, and mapping learning features to attribute labels to realize knowledge migration from transient disturbance or converter fault types to composite working conditions;
the attribute learner is formed by stacking a full connection layer, a BN layer and a nonlinear activation layer, wherein the stacking sequence is as follows: a full connection layer, a BN layer, a nonlinear activation layer, a full connection layer, a BN layer, a nonlinear activation layer and a full connection layer;
The input of the attribute learner is the output feature vector of the feature extraction network after training Output as attribute vector/>, of corresponding sampleThe mapping relation between the features and the attribute vectors is learned in a supervised mode, and the reasoning process of the attribute learner is expressed as/>Wherein/>Represents the/>Corresponding feature vector of each sample,/>An attribute vector representing the ith sample generated by the attribute learner. And (3) realizing the reasoning process of the attribute learner by constructing a fully connected network.
The loss function of the attribute learner during training is as follows:
；
wherein the method comprises the steps of Representing the number of input feature vectors,/>Attribute vector representing artificially defined ith sample,/>An attribute vector representing the ith sample generated by the attribute learner.
S5, outputting an attribute vector by an attribute learner based on training completionAnd predicting the sample category by adopting maximum likelihood estimation to obtain a sample prediction category label, thereby realizing the identification of transient disturbance abnormal working conditions of the in-phase power supply system and fault abnormal working conditions of the in-phase power supply device converter and the identification of composite working conditions.
The maximum likelihood estimation formula is:
；
Wherein M is the number of test set categories; extracting the attribute by the expression algorithm through a feature extraction network and an attribute learner to generate an inference process of an attribute vector; /(I) Representing samples,/>Representing attribute learner outputting attribute vectors, i.e./>；/>Representing the attribute vector of the i-th sample defined manually.
Example 2:
Firstly, the invention is introduced to the applicable scene, and the invention can be applied to the identification of the vehicle network coupling disturbance and the power electronic device fault of the in-phase power supply converter in the single-phase combined in-phase power supply system.
As shown in figure 1, the single-phase combined type in-phase power supply system is an AC-DC-AC converter, and is connected with a 27.5kV traction network side through a matching transformer, and in the train operation process, the in-phase power supply system can experience transient and quasi-steady states such as harmonic resonance, low-frequency oscillation, excitation surge current and the like to influence the normal operation of the in-phase power supply converter. The in-phase power supply converter is formed by cascading or connecting a plurality of back-to-back H-bridge converter modules in parallel, and the structure of a single converter module is shown in figure 2. The inverter side of the converter is connected with a traction network through a matching transformer, the port voltage of the matching transformer is U M, and the output current of the port of the inverter is Ialpha; the rectifier side is connected with the traction transformer through a matching transformer, the port voltage of the matching transformer is U T, and the output current of the rectifier port is I beta. The open circuit fault of the IGBT in the converter can have a great influence on the output of the in-phase power supply device, and even the converter can trigger overcurrent protection to stop operation.
Aiming at the abnormal working conditions caused by the vehicle network coupling disturbance of the in-phase power supply system and the power electronic device fault, no method is available at present for simultaneously considering the vehicle network coupling disturbance and the power electronic device fault, and the direct cause of the protection action of the in-phase power supply device cannot be analyzed.
In conclusion, the disturbance and the fault in the in-phase power supply system are identified, the abnormal working condition of the in-phase power supply converter can be analyzed from the level of the in-phase power supply system, and further the occurrence reason of the abnormal working condition is checked, so that the normal and safe operation of the in-phase power supply system is facilitated.
Based on this, the embodiment of the invention provides a disturbance and fault identification method of an in-phase power supply system, a framework is shown in fig. 3, a construction flow is shown in fig. 4, and the method comprises the following steps:
s1, constructing a data set, and considering that disturbance types comprise harmonic resonance, excitation surge current and grounding short circuit; the converter fault type comprises an inverter IGBT dual alpha type open circuit fault, a dual beta type open circuit fault and a non-dual double-tube open circuit fault in the back-to-back converter module.
Building training data sets based thereonAnd a test dataset. Wherein/>Represents the/>The number of (data) samples and their corresponding tags; /(I)Representing the samples of disturbances or faults of the type seen in the training set and in the test set, respectively, and there are/>；Representing the unoccupied type in the test set, namely the compound working condition; /(I)Representing the visible type tags in the training set,/>Representing the visible type tags and the invisible type tags in the test set, respectively. The sampling frequency of the voltage and current signals is 6400Hz, and the samples in the data set/>The tensors are all tensors with the size of 2 multiplied by 6400, and comprise waveform data of the output current Ialpha of the 1s internal converter module and waveform data of the network side 27.5kV voltage U M.
S2, constructing a TimeNet-based feature extraction network, wherein the input of the network is different types of faults or disturbance samples, and the output is a feature vector. The characteristic extraction network comprises a voltage channel and a current channel, each channel comprises a time sequence dimension increasing module and a characteristic extraction module, and the structures of the two channels are identical. Respectively extracting characteristics of voltage and current waveforms and extracting the extracted current characteristicsVoltage characteristics/>Merging to obtain final features/>. The reasoning process of the feature extraction network is expressed asWherein/>Represents the/>Sample number,/>Represents the/>The individual samples correspond to feature vectors.
Taking a voltage channel as an example, a time sequence dimension-increasing module in the channel performs one-dimensional time sequence on voltage waveformsFFT conversion is carried out to obtain the period and amplitude information of different frequency components, a period P corresponding to the frequency component with the largest amplitude is selected, the signal is sheared by taking P as a unit and is spliced in the first dimension, and the two-dimensional tensor/> isobtained. Therefore, the one-dimensional time sequence problem is converted into the two-dimensional image recognition field without loss.
The feature extraction module in the channel is formed by stacking a two-dimensional convolution layer, a batch normalization layer, a nonlinear activation layer and a maximum pooling layer, wherein the stacking sequence is the two-dimensional convolution layer, a BN layer, the nonlinear activation layer and the maximum pooling layer, the two-dimensional convolution layer, a first Inception module, a second Inception module and a third Inception module, and the first Inception module, the second Inception module and the third Inception module have the same structure as each other, as shown in FIG. 5.
Each Inception module contains a two-dimensional convolution layer, a Inception convolution layer, a BN layer, a nonlinear activation layer, and a max-pooling layer. The stack of Inception convolution layers and pooling layers fully extracts the high-dimensional characteristics of the data on the basis of guaranteeing the generalization capability of the model, reduces the calculated amount and improves the data processing speed.
Setting super parameters, loss functions and optimization strategies of the feature extraction network, and training the feature extraction network by using a training set.
When the feature extraction network is trained, a softmax classifier is added at the end of the network, and the feature extraction network is subjected to supervised training in the form of classification subtasks. In the training process, an Adam algorithm is selected as a model optimization strategy, and a cross entropy loss function is selected as a model loss function.
S3, constructing an attribute description capable of describing fault or disturbance characteristics based on current-voltage signal characteristics of samples of different categories, wherein the attribute description is shown in the following table:
TABLE 1 disturbance or failure attribute information
The attribute tag contains direct description of different types of transient and steady state features in the sample, and sample attribute vector is defined manuallyThe attribute vector contains descriptions of 9 attributes of a single sample. Attribute vectors of different classes make up an attribute description matrix/>. The attribute description matrix is shown in table 2.
Table 2 attribute description matrix
Performing attribute description on each sample in the training set to form an attribute vector, and combining the feature vectors of the samples output by the feature extractor to form a second training set. For training the attribute learner. /(I)Representing the corresponding eigenvector of the ith sample,/>Attribute vector representing artificially defined ith sample,/>Representation utilizing training set sample set/>A feature vector set generated by the middle sample through a feature extractor; /(I)Representing a set of attribute vectors corresponding to different types of data.
S4, constructing a fully-connected network as an attribute learner, wherein the attribute learner is formed by stacking a fully-connected layer, a BN layer and a nonlinear activation layer, and the stacking sequence is as follows: full tie layer, BN layer, nonlinear activation layer, full tie layer. The input of the attribute learner is the output feature vector of the feature extraction network after trainingOutput as attribute vector/>, of corresponding sampleThe mapping relationship between the feature and the attribute vector is learned in a supervised manner. The reasoning process of the attribute learner is denoted/>Wherein/>Represents the/>Samples. In order to make the attribute vector generated by the attribute learner to be as close to the artificial attribute vector as possible, the loss function of the attribute learner during training is set as follows:
；
wherein the method comprises the steps of Representing the number of input feature vectors,/>Representing the artificially defined/>The attribute vector of the individual samples is used,Representing the second/>, generated by the attribute learnerAttribute vector of individual samples.
S5, outputting an attribute vector by an attribute learner based on training completionAnd predicting the sample category by adopting maximum likelihood estimation to obtain a sample prediction category label, thereby realizing the identification of transient disturbance abnormal working conditions of the in-phase power supply system and fault abnormal working conditions of the in-phase power supply device converter and the identification of composite working conditions. The maximum likelihood estimation formula is:
；
Wherein M is the number of test set categories; extracting the attribute by the expression algorithm through a feature extraction network and an attribute learner to generate an inference process of an attribute vector; /(I) Representing samples,/>Representing attribute learner outputting attribute vectors, i.e./>；/>Representing the attribute vector of the i-th sample defined manually.
The embodiment of the specification also discloses a computer readable storage medium, wherein the storage medium stores computer instructions, and when the computer reads the computer instructions, the computer can execute the steps of the in-phase power supply system disturbance and fault identification method.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.

Claims (8)

1.一种同相供电系统的扰动与故障辨识方法，其特征在于，包括如下步骤：1. A disturbance and fault identification method for a co-phase power supply system, characterized in that it comprises the following steps: S1、构建单相组合式同相供电系统暂态扰动与同相供电装置变流器故障数据集，将数据集分为训练集和测试集；S1. Construct a data set of transient disturbances of a single-phase combined co-phase power supply system and converter faults of a co-phase power supply device, and divide the data set into a training set and a test set; S2、构建基于TimeNet的特征提取网络，在特征提取网络末端加上Softmax层形成分类网络，利用训练集对特征提取网络进行训练，学习暂态扰动以及变流器故障样本的特征，训练完成后保留特征提取网络的网络参数，使用训练完成的特征提取网络实现对样本的特征提取；S2. Construct a feature extraction network based on TimeNet, add a Softmax layer at the end of the feature extraction network to form a classification network, use the training set to train the feature extraction network, learn the features of transient disturbances and converter fault samples, retain the network parameters of the feature extraction network after training, and use the trained feature extraction network to extract features from samples; S3、基于不同类别样本的电流电压信号构建描述暂态扰动和变流器故障的各类别特征的属性标签；所述属性标签包含对样本中不同类型暂、稳态特征的直接描述，人工定义样本属性向量α＝{a1,a2,...,an}，属性向量中包含对单个样本n个属性的描述；S3. constructing attribute labels describing characteristics of each category of transient disturbance and converter fault based on current and voltage signals of samples of different categories; the attribute labels include direct descriptions of different types of transient and steady-state characteristics in the samples, and manually defining a sample attribute vector α={a 1 ,a 2 ,...,a n }, wherein the attribute vector includes descriptions of n attributes of a single sample; 由扰动类型X和故障类型Y组合形成的复合工况XY的属性向量αXY表示为：αXY＝αX∪αY，其中，αX和αY分别为扰动类型X和故障类型Y对应的属性向量；The attribute vector α XY of the composite operating condition XY formed by the combination of the disturbance type X and the fault type Y is expressed as: α XY = α X ∪ α Y , where α X and α Y are the attribute vectors corresponding to the disturbance type X and the fault type Y respectively; 不同类别的属性向量构成属性描述矩阵A＝{α1,α2,...,αk}，其中k表示故障类别、扰动类别与复合工况类别的总类别数量；The attribute vectors of different categories constitute the attribute description matrix A={α 1 ,α 2 ,...,α k }, where k represents the total number of fault categories, disturbance categories and composite operating condition categories; S4、构建全连接网络作为属性学习器，学习特征到属性标签的映射，实现暂态扰动或变流器故障类型向复合工况的知识迁移；所述属性学习器由全连接层、批归一化层、非线性激活层堆叠而成，堆叠顺序依次为：全连接层、批归一化层、非线性激活层、全连接层、批归一化层、非线性激活层、全连接层；S4. Construct a fully connected network as an attribute learner to learn the mapping from features to attribute labels, and realize the knowledge transfer from transient disturbances or converter fault types to composite working conditions; the attribute learner is stacked by a fully connected layer, a batch normalization layer, and a nonlinear activation layer, and the stacking order is: fully connected layer, batch normalization layer, nonlinear activation layer, fully connected layer, batch normalization layer, nonlinear activation layer, and fully connected layer; 所述属性学习器的输入为训练完成的特征提取网络的输出特征向量f，输出为对应样本的属性向量α'＝{a1',a2',...,an'}，有监督地学习特征与属性向量之间的映射关系，属性学习器的推理过程表示为G:fi→αi'，其中fi表示第i个样本对应特征向量，αi'表示属性学习器生成的第i个样本的属性向量；The input of the attribute learner is the output feature vector f of the trained feature extraction network, and the output is the attribute vector α'={a 1 ',a 2 ',...,a n '} of the corresponding sample. The mapping relationship between the feature and the attribute vector is learned in a supervised manner. The reasoning process of the attribute learner is expressed as G: fi → αi ', wherefi represents the feature vector corresponding to the ith sample, and αi ' represents the attribute vector of the ith sample generated by the attribute learner; S5、基于训练完成的属性学习器输出属性向量，采用最大似然估计对样本类别进行预测，从而实现对同相供电系统的暂态扰动异常工况与同相供电装置变流器故障异常工况的辨识以及复合工况的辨识。S5. Based on the attribute vector output by the trained attribute learner, the sample category is predicted using maximum likelihood estimation, thereby realizing the identification of abnormal transient disturbance conditions of the same-phase power supply system and abnormal fault conditions of the converter of the same-phase power supply device, as well as the identification of composite conditions. 2.根据权利要求1所述的同相供电系统的扰动与故障辨识方法，其特征在于：在步骤S1中，具体包括如下：采集单相组合式同相供电系统中同相供电变流器输出电流信号和网侧27.5kV电压信号，对采集电流信号和电压信号进行划分，构成独立且尺寸相同的数据样本，单个数据样本中包含同一时间段的电流信号和电压信号；所述数据样本均为大小为2×6400的张量，包含1s内变流器模块输出电流波形数据和网侧27.5kV电压波形数据。2. The disturbance and fault identification method of the co-phase power supply system according to claim 1 is characterized in that: in step S1, it specifically includes the following: collecting the output current signal of the co-phase power supply converter in the single-phase combined co-phase power supply system and the 27.5kV voltage signal on the grid side, dividing the collected current signal and voltage signal to form independent data samples of the same size, and a single data sample contains the current signal and voltage signal of the same time period; the data samples are all tensors of size 2×6400, containing the output current waveform data of the converter module and the 27.5kV voltage waveform data on the grid side within 1s. 3.根据权利要求1所述的同相供电系统的扰动与故障辨识方法，其特征在于：所述训练集中不包括同相供电系统暂态扰动与同相供电装置变流器故障同时发生的复合工况，即复合工况类型为未见类型；3. The disturbance and fault identification method of the co-phase power supply system according to claim 1 is characterized in that: the training set does not include a composite operating condition in which a transient disturbance of the co-phase power supply system and a converter fault of the co-phase power supply device occur simultaneously, that is, the composite operating condition type is an unseen type; 其中，暂态扰动类型包含谐波谐振、励磁涌流、接地短路；变流器故障类型包含背靠背变流器模块中逆变器IGBT对偶性α类开路故障、对偶性β类开路故障和非对偶性双管开路故障。Among them, transient disturbance types include harmonic resonance, excitation inrush current, and ground short circuit; converter fault types include inverter IGBT dual α-type open circuit fault, dual β-type open circuit fault, and non-dual double-tube open circuit fault in the back-to-back converter module. 4.根据权利要求1所述的同相供电系统的扰动与故障辨识方法，其特征在于：在步骤S2中，所述基于TimeNet的特征提取网络包含电压通道和电流通道，每个通道均包括时序升维模块和特征提取模块，对电压、电流波形分别进行特征提取并将提取得到的电压、电流特征合并，其中，特征提取网络的推理过程表示为F:xi→fi，其中xi表示第i个样本，fi表示第i个样本对应特征向量。4. The disturbance and fault identification method of the co-phase power supply system according to claim 1 is characterized in that: in step S2, the TimeNet-based feature extraction network includes a voltage channel and a current channel, each channel includes a time series dimension upgrading module and a feature extraction module, and feature extraction is performed on the voltage and current waveforms respectively and the extracted voltage and current features are merged, wherein the reasoning process of the feature extraction network is expressed as F: xi → fi , whereinxi represents the i-th sample, andfi represents the feature vector corresponding to the i-th sample. 5.根据权利要求4所述的同相供电系统的扰动与故障辨识方法，其特征在于：所述时序升维模块对一维时间序列X1∈R1×6400进行FFT变换，得到不同频率分量的周期与幅值信息，选取幅值最大的频率分量对应的周期P，以P为单位对信号进行剪切并在第一维度进行拼接，得到二维张量X2∈RP×(6400/P)，完成将一维时序问题转化到二维图像识别。5. The disturbance and fault identification method of the co-phase power supply system according to claim 4 is characterized in that: the time series dimension upgrading module performs FFT transformation on the one-dimensional time series X 1 ∈R 1×6400 to obtain the period and amplitude information of different frequency components, selects the period P corresponding to the frequency component with the largest amplitude, cuts the signal in units of P and splices it in the first dimension to obtain a two-dimensional tensor X 2 ∈R P×(6400/P) , thereby completing the transformation of the one-dimensional time series problem into two-dimensional image recognition. 6.根据权利要求4所述的同相供电系统的扰动与故障辨识方法，其特征在于：所述特征提取模块由二维卷积层、批归一化层、非线性激活层、最大池化层堆叠而成，采用Inception网络结构提高特征提取模块泛化能力，包括第一Inception模块、第二Inception模块和第三Inception模块；6. The disturbance and fault identification method of the co-phase power supply system according to claim 4 is characterized in that: the feature extraction module is stacked by a two-dimensional convolution layer, a batch normalization layer, a nonlinear activation layer, and a maximum pooling layer, and an Inception network structure is used to improve the generalization ability of the feature extraction module, including a first Inception module, a second Inception module, and a third Inception module; 所述堆叠的顺序为二维卷积层、批归一化层、非线性激活层、最大池化层、二维卷积层、第一Inception模块、第二Inception模块、第三Inception模块，其中第一、第二、第三Inception模块结构相同，且每个Inception模块中包含二维卷积层、Inception卷积层、批归一化层、非线性激活层、最大池化层。The stacking order is two-dimensional convolution layer, batch normalization layer, non-linear activation layer, maximum pooling layer, two-dimensional convolution layer, first Inception module, second Inception module, third Inception module, wherein the first, second, and third Inception modules have the same structure, and each Inception module includes a two-dimensional convolution layer, an Inception convolution layer, a batch normalization layer, a non-linear activation layer, and a maximum pooling layer. 7.根据权利要求1所述的同相供电系统的扰动与故障辨识方法，其特征在于：所述属性学习器训练时的损失函数为：7. The disturbance and fault identification method of the co-phase power supply system according to claim 1 is characterized in that: the loss function of the attribute learner during training is: 其中NS代表输入特征向量的个数，αi表示人工定义的第i个样本的属性向量，αi'表示属性学习器生成的第i个样本的属性向量。Where N S represents the number of input feature vectors, α i represents the attribute vector of the i-th sample defined manually, and α i ' represents the attribute vector of the i-th sample generated by the attribute learner. 8.根据权利要求1所述的同相供电系统的扰动与故障辨识方法，其特征在于：所述最大似然估计公式为：8. The disturbance and fault identification method of the same-phase power supply system according to claim 1, characterized in that: the maximum likelihood estimation formula is: 其中，M为测试集类别个数；G(F(·))表示算法经过特征提取网络和属性学习器对属性进行提取，生成属性向量的推理过程；x表示样本，G(F(x))表示属性学习器输出属性向量，即αi'；αi表示人工定义的第i个样本的属性向量。Where M is the number of categories in the test set; G(F(·)) represents the inference process of the algorithm extracting attributes through the feature extraction network and the attribute learner to generate the attribute vector; x represents the sample, G(F(x)) represents the attribute vector output by the attribute learner, that is, α i '; α i represents the attribute vector of the manually defined i-th sample.
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