CN114114126B - Digital calibration method and system for low-permeability magnetic powder core current transformer - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, in particular to a digital calibration method and a digital calibration system for a low-permeability magnetic powder core current transformer, wherein the calibration method comprises the following steps: adjusting the current output by the standard signal source to obtain an output current sequence of the current transformer to be calibrated; the output current sequence is input into the optimal prediction network, the optimal prediction network outputs the phase difference between the current transformer to be calibrated and the standard current transformer in the next predicted time, and the phase difference is used as a calibration value to calibrate the current transformer to be calibrated.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a digital calibration method and a digital calibration system for a low-permeability magnetic powder core current transformer.
Background
In the power industry, a large number of devices for electric energy metering, harmonic monitoring and control, reactive power compensation and line protection are used, and the common requirement of the devices is that the current needs to be measured. In most cases, the above-mentioned equipment adopts current transformer made of iron-nickel base or ferrosilicon material, and a small quantity of them also adopts Rogowski coil. The traditional calibration method of the current transformer utilizes the linear characteristics presented by the phase difference and the ratio difference of the current transformer in the linear working area, adopts the increase and decrease of turns to calibrate the ratio difference, and compensates the angular difference through secondary output parallel capacitors when necessary.
In practice, the inventors found that the above prior art has the following disadvantages:
because the low-permeability magnetic powder core current transformer does not have a linear working area, the traditional calibration method of the current transformer is not suitable for the low-permeability magnetic powder core current transformer.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a digital calibration method and system for a low-permeability magnetic powder core current transformer, wherein the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a digital calibration method for a low permeability magnetic powder core current transformer, where the calibration method includes a standard signal source and a current transformer to be calibrated, which are connected in series, and the calibration method includes the following steps: adjusting the current output by the standard signal source to obtain an output current sequence of the current transformer to be calibrated; inputting the output current sequence into the optimal prediction network, wherein the optimal prediction network outputs a predicted phase difference between the current transformer to be calibrated and a standard current transformer at the next moment, and the phase difference is used as a calibration value to calibrate the current transformer to be calibrated; the method for acquiring the optimal prediction network comprises the following steps: for each sample in the historical data, the sample is a current transformer to be calibrated, and any one sample corresponds to a group of training data and an initial prediction network; the training data comprises an output current sequence and a phase difference sequence, wherein an element in the phase difference sequence is a phase difference value between a sample and the output current of a standard current transformer after the current is adjusted for the same time, and the standard current transformer is connected with the sample in series; selecting any one initial prediction network as a target network, exchanging training data of the target network and other initial prediction networks, and obtaining average similarity between output values of the target network before and after exchanging the training data; and selecting the target network with the maximum average similarity as the optimal prediction network.
Further, the method for obtaining the average similarity between the output values of the target network before and after the exchange of the training data comprises: and calculating the similarity between the output values of the target network before and after the training data are exchanged each time, wherein the average value of all the obtained similarities is the average similarity.
Further, the method for calculating the similarity between the output values of the target network before and after each training data exchange includes: and calculating the similarity distance by using a dynamic time warping algorithm, and obtaining the similarity according to the similarity distance.
Further, in the step of exchanging training data of the target network and the other initial prediction networks, the target network and the initial prediction network exchanging training data belong to initial prediction networks corresponding to samples of the same batch, or the target network and the initial prediction network exchanging training data belong to initial prediction networks corresponding to samples of different batches respectively.
Further, the loss function of the initial prediction network adopts a mean square error loss function with the value of the output current as a weight.
Further, after the step of selecting the target network with the largest average similarity as the optimal prediction network, the method further includes: training the optimal prediction network by using training data of other initial prediction networks, and comparing similarity E between phase difference output by the initial prediction network corresponding to training by using the training data and phase difference output by the optimal prediction network i (ii) a Obtaining a similarity E i Said average similarity E to the optimal prediction network 0 Absolute deviation between, according to said absolute deviation and the mean similarity E 0 The ratio adjusts the weight of the mean square error loss function in the optimal prediction network.
Further, the absolute deviation and average similarity E 0 The ratio of (d) to (d) is inversely related to the weight of the mean square error loss function.
In a second aspect, another embodiment of the present invention provides a digital calibration system for a low permeability magnetic powder core current transformer, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.
The invention has the following beneficial effects:
the embodiment of the invention provides a digital calibration method of a low-permeability magnetic powder core current transformer, and the optimal prediction network is selected from the initial prediction network corresponding to a large amount of training data, so that the phase difference between the current transformer to be measured and a standard current transformer can be accurately predicted, and therefore, in the calibration process, the phase difference can be obtained, the participation of the standard current transformer can be saved, and the calibration cost is saved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a digital calibration method for a low permeability magnetic powder core current transformer according to an embodiment of the present invention;
fig. 2 is a flowchart of an optimal prediction network obtaining method according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the digital calibration method for a low permeability magnetic powder core current transformer according to the present invention will be made with reference to the accompanying drawings and preferred embodiments. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
According to the embodiment of the invention, a standard signal source and a current transformer to be calibrated are connected in series, the output current sequence of the current transformer to be calibrated is obtained by adjusting the current magnitude of the standard signal source, the output current sequence is input into a trained prediction network to predict the phase difference between the output current sequence and the standard current transformer, and the phase difference is used as a calibration value to calibrate the current transformer. Compared with the traditional mode that the phase difference is acquired by combining a standard current transformer, the method has the advantages that the participation of the standard current transformer can be eliminated under the condition that the accuracy of the acquired phase difference is ensured in the calibration process, the cost is saved, and the complicated calibration steps are reduced.
In the embodiment of the invention, the standard signal source adopts a current source.
The following describes a specific scheme of a digital calibration method and system of a low-permeability magnetic powder core current transformer provided by the invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a digital calibration method for a low permeability magnetic powder core current transformer according to an embodiment of the present invention is shown, where the calibration method includes the following steps:
step S001: and adjusting the current output by the standard signal source to obtain an output current sequence of the current transformer to be calibrated.
Connecting a standard signal source and a current transformer to be calibrated in series, wherein the current transformer to be calibrated outputs a corresponding current each time the output current of the standard signal source is adjusted; thus, after a number of adjustments, the current transformer to be calibrated outputs a corresponding output current sequence.
Step S002: and inputting the output current sequence into the optimal prediction network, outputting a phase difference between the current transformer to be calibrated and a standard current transformer in the next predicted moment by the optimal prediction network, and calibrating the current transformer to be calibrated by taking the phase difference as a calibration value.
Specifically, referring to fig. 2, the method for obtaining the optimal prediction network includes the following steps:
step S201: for each sample in the historical data, the sample is a current transformer to be calibrated, and any sample corresponds to a group of training data and an initial prediction network; the training data comprises an output current sequence and a phase difference sequence, wherein an element in the phase difference sequence is a phase difference value between a sample and an output current of a standard current transformer after the same current adjustment, and the standard current transformer is connected with the sample in series.
For the samples in the historical data, the standard signal source, the standard current transformer and the samples need to be connected in series in sequence to form a series circuit, so that when the magnitude of the output current of the standard signal is adjusted by the same method as in step S001, the actual output current of the sample and the standard current output by the standard current transformer can be obtained respectively, and the phase difference value can be obtained by comparing the phase between the actual output current of the sample and the standard current. That is, the actual output current and a phase difference value of a sample can be obtained every time the current is adjusted; after a plurality of current adjustments, a plurality of output currents of the sample, from which the output current sequence is formed, and a phase difference value, from which the phase difference value sequence is formed, can thus be obtained. And taking the output current sequence and the phase difference sequence as training data.
The method for obtaining the phase difference value by comparing the phase between the actual output current of the sample and the standard current specifically comprises the following steps: obtaining a current waveform according to the actual output current sequence of the sample, and simultaneously obtaining a current waveform of a standard current sequence output by a standard current transformer; under the same coordinate system, with time as a horizontal axis and the magnitude of a current value as a vertical axis, respectively making current waveform curves of a sample and a standard current transformer, respectively finding out data points of two curves passing through a zero point, and comparing time differences of the data points of two zero-crossing points, wherein the time difference is a phase difference.
In the embodiment of the invention, the optimal prediction network is obtained by training the TCN network, and in other embodiments, the same training method can be adopted to train other network types capable of realizing the same function to obtain the optimal prediction network.
Taking the TCN network as an example, the training process of the initial prediction network is described: acquiring training data of each sample, namely an actual output current sequence and a phase difference sequence of each sample, and inputting the training data into an initial prediction network to predict a phase difference at the next moment; the loss function of the initial prediction network adopts a mean square error loss function.
Step S202: and selecting any one initial prediction network as a target network, exchanging training data of the target network and other initial prediction networks, and acquiring average similarity between output values of the target network before and after the training data is exchanged.
In step S201, each sample in the sample set of the database is a current transformer to be calibrated, and each sample corresponds to a training data and an initial prediction network. Specifically, any sample in the sample set is selected, the selected sample is recorded as a target sample, training data corresponding to the target sample is recorded as first training data, an initial prediction network is recorded as a first initial prediction network, any other sample except the target sample in the sample set is recorded as an exchange sample, training data corresponding to the exchange sample is recorded as second training data, and an initial prediction network corresponding to the exchange sample is recorded as a second initial prediction network. Inputting second training data into a second initial prediction network to obtain an initial phase difference; and recording the phase difference obtained by inputting the second training data into the first initial prediction network as an exchange phase difference, and calculating the similarity between the initial phase difference and the exchange phase difference. Similarly, each second training data in the sample set is respectively input into the first initial prediction network and the second initial prediction network corresponding to each sample to obtain the similarity corresponding to each second training data, and further the similarity corresponding to all the phase differences is obtained. And obtaining the average value of the similarity between the first initial prediction network and all other second initial prediction networks to obtain the average similarity.
As an example, assume for a sample set { x } 1 ，x 2 ，x 3 ，...x i ，...x n H, i =1,2,3 i Represents the ith sample, and the corresponding training set is marked as { I 1 ，I 2 ，I 3 ，...I i ，...I n Selecting the ith sample x from the sample set i As a target sample, a first sample corresponding to the target sampleTraining data are I i Recording a first initial prediction network corresponding to the target sample as an A network; a certain sample x is exchanged q The corresponding second initial prediction network is marked as B network, and the corresponding second training data is marked as I q . Using second training data I q Training B network to obtain initial phase difference delta T 0 Then the second training data I is applied q Training A network to obtain exchange phase difference delta T q The similarity between the initial phase difference and the exchange phase difference is Δ T i And Δ T q Similarity between them S iq . In the same way, the second training data corresponding to the other switching samples are respectively output to the network a and the network B corresponding to the network a to obtain the corresponding similarity, which is marked as S = { S = { S = i1 ，S i2 ，S i3 ，...S iq ，...S in }. The average similarity refers to the average of the similarity set S.
The average similarity can reflect the accuracy of the initial prediction network, the greater the average similarity is, the higher the accuracy of the initial prediction network is, and if the deviation between the predicted value obtained by training the target initial prediction network by using each other sample and the predicted value of the other sample in the initial prediction network is smaller, the stronger the accuracy of the target initial prediction network is.
Step S203: and selecting the target network with the maximum average similarity as the optimal prediction network.
According to the method in step S202, the average similarity of the phase differences predicted by the initial prediction network corresponding to each sample can be obtained, and the initial prediction network corresponding to the maximum value of the average similarity is the prediction network with the highest accuracy in the current training process, so that the network is selected as the optimal prediction network.
In summary, the embodiments of the present invention provide a digital calibration method for a low-permeability magnetic powder core current transformer, in which a standard signal source and a current transformer to be calibrated are connected in series, the output current of the standard signal source is adjusted to obtain the output current of the current transformer, the output current is input to a trained optimal prediction network to predict a corresponding phase difference, and the phase difference is used as a calibration value to calibrate the current transformer. The optimal prediction network is obtained by training sample data in historical data; the sample data is obtained by connecting a corresponding current transformer to be calibrated, a standard current transformer and a standard signal source in series, obtaining a phase difference sequence between the current transformer to be calibrated and the standard current transformer by adjusting the current of the standard signal source, and training the prediction network by using the phase difference sequence and the current sequence as training data, wherein the phase difference sequence is output by the current transformer to be calibrated after the current of the standard signal source is adjusted. The optimal prediction network is selected from the initial prediction networks corresponding to a large amount of training data, so that the phase difference between the current transformer to be tested and the standard current transformer can be accurately predicted, the phase difference can be obtained by the calibration method in the calibration process, the participation of the standard current transformer can be saved, and the calibration cost is saved.
Preferably, in step S202, a dynamic time warping algorithm is used to calculate a similarity between the first phase difference and the second phase difference, specifically, if the first initial prediction network is marked as a, the second initial prediction network is marked as B, the first phase difference obtained by training the first initial prediction network with the second training set is marked as X, and the second phase difference obtained by training the second initial prediction network with the second training set is marked as Y, then a similarity S between the phase differences output by the first initial prediction network a and the second initial prediction network B is calculated AB Comprises the following steps:
wherein, DTW (X, Y) represents the distance between the parameters X and Y calculated by the dynamic time warping algorithm, the larger the distance value is, the smaller the similarity between the two is, and the smaller the distance value is, the larger the similarity between the two is. By comparing the similarity of the output results of the prediction networks before and after sample exchange, the more similar the output results, the more similar the prediction results of the first initial prediction network and the second initial prediction network, that is, the first initial prediction network can replace the second initial prediction network.
Preferably, in order to make the prediction result of the network more accurate, further improvement on the loss function is needed. As the input current of the current transformer increases, the loss angle of the iron core also increases, and the phase difference is reduced; however, after the current increases to a certain degree, the core saturates, the core angle decreases instead, and the phase difference increases. Therefore, a weight is assigned to the mean square error loss using the rate of change of the phase difference, a lower weight is assigned to a phase difference having a large rate of change, and a higher weight is assigned to a phase difference having a small rate of change. The Loss corresponding to the ith output current after training is recorded as Loss i And the weight of the rate of change of the phase difference corresponding to the ith output current is represented as C i The Loss function of the initial prediction network is marked as Loss, and for all C i After normalization, the overall loss of the initial prediction network is obtained by performing weighted summation on the loss corresponding to each training data in the sample, and then:
Loss＝∑(Loss i *C i )
more accurate prediction results can be obtained through the improvement of the loss function.
Preferably, in order to obtain a more accurate prediction result, an allowable error of the average similarity is preset, training data corresponding to the average similarity within an allowable error range is obtained, the training data is recorded as continuous training data, the selected optimal prediction network is retrained again by using the continuous training data to obtain a third phase difference, a fourth phase difference obtained after the continuous training data is input into the initial prediction network is obtained, a similarity between the third phase difference and the fourth phase difference is obtained, and the similarity is recorded as E i (ii) a Recording the average similarity of the selected optimal prediction network before continuing training as E 0 And the weight of the ith continuous training data is recorded as C i (ii) a Similarity E i The average similarity E with the optimal prediction network 0 The absolute deviation between them is recorded as | E 0 -E i According to the absolute deviation and the average similarity E 0 Ratio adjustment in the optimal prediction networkThe weight of the variance loss function is recorded as C j Then, there are:
wherein L represents a loss value.
The method for acquiring the loss value comprises the following steps: marking the phase difference obtained by adjusting the current for the ith time in the current continuous training data of the sample as Ang i (ii) a For this sample, at the time of shipment, in order to reduce the error caused by the line delay, therefore, different phase differences can be obtained by adjusting the length of the line under the condition that the current is not changed, and the average phase difference obtained from the average of all the phase differences is recorded as Ang 0i Then the loss value L:
wherein Ang 0i Indicates the average phase difference, ang, corresponding to the i-th adjustment of the current Δ Phase difference Ang obtained for i-th adjustment of current i And its corresponding average phase difference Ang 0i N is the number of times the current is adjusted.
After continuing training, the resulting loss function of the optimal prediction network is: loss = ∑ (Loss) j *C j )。
Preferably, the allowable error is set to ± 0.1 in the embodiment of the present invention, and in other embodiments, the allowable error may be set according to actual requirements.
Preferably, in the embodiment of the present invention, the loss value at the time of shipment is set to 0. In other embodiments, the loss value may be set as desired.
Preferably, in the step S202 of selecting the optimal prediction network, the category to which the sample belongs may be selected according to needs, for example, if the target sample and the exchange sample may be samples of the same batch, the selected optimal prediction network is the optimal prediction network most suitable for the samples of the batch; the target sample and the exchange sample can also belong to two different batches of samples, and the selected optimal prediction network is the optimal prediction network suitable for the samples of different batches; or selecting in a progressive manner, wherein in the initial selection process, the target sample and the exchange sample are samples of the same batch, an initial optimal prediction network is selected, and then when the target sample and the exchange sample are selected again, the target sample and the exchange sample belong to two different batches of samples, and a final optimal prediction network is selected.
Based on the same inventive concept as the method embodiment, another embodiment of the present invention further provides a digital calibration system of a low permeability magnetic powder core current transformer, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the digital calibration method of the low permeability magnetic powder core current transformer according to any one of the embodiments when executing the computer program, where the digital calibration method of the low permeability magnetic powder core current transformer has been described in detail in the embodiments, and is not described again.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A digital calibration method for a low-permeability magnetic powder core current transformer comprises a standard signal source and a current transformer to be calibrated which are connected in series, and is characterized by comprising the following steps:
adjusting the current output by the standard signal source to obtain an output current sequence of the current transformer to be calibrated;
inputting the output current sequence into an optimal prediction network, wherein the optimal prediction network outputs a predicted phase difference between the current transformer to be calibrated and a standard current transformer at the next moment, and the phase difference is used as a calibration value to calibrate the current transformer to be calibrated;
the method for acquiring the optimal prediction network comprises the following steps: for each sample in the historical data, the sample is a current transformer to be calibrated, and any one sample corresponds to a group of training data and an initial prediction network; the training data comprises an output current sequence and a phase difference sequence, wherein an element in the phase difference sequence is a phase difference value between a sample and the output current of a standard current transformer after the current is adjusted for the same time, and the standard current transformer is connected with the sample in series; selecting any one initial prediction network as a target network, exchanging training data of the target network and other initial prediction networks, and obtaining average similarity between output values of the target network before and after exchanging the training data; selecting a target network with the maximum average similarity as an optimal prediction network;
in the step of exchanging training data of the target network and other initial prediction networks, the target network and the initial prediction network for exchanging training data belong to initial prediction networks corresponding to samples in the same batch, or the target network and the initial prediction network for exchanging training data belong to initial prediction networks corresponding to samples in different batches respectively;
after the step of selecting the target network with the maximum average similarity as the optimal prediction network, the method further includes:
presetting an allowable error of the average similarity, acquiring training data corresponding to the average similarity within an allowable error range, recording the training data as continuous training data, retraining the selected optimal prediction network again by using the continuous training data to obtain a third phase difference, acquiring a fourth phase difference obtained after the continuous training data is input into the initial prediction network, acquiring the similarity between the third phase difference and the fourth phase difference, and recording the similarity as
(ii) a Recording the average similarity of the selected optimal prediction network before continuing training
First, aiThe weight of each continuous training data is recorded as
(ii) a Will similarity
The average similarity to the optimal prediction network
The absolute deviation therebetween is recorded as $
-
According to the absolute deviation and the average similarity
The weight of the mean square error loss function in the optimal prediction network is adjusted according to the ratio of the mean square error loss function to the optimal prediction network, and the improved weight is recorded as
；
Wherein,
the value of the loss is represented by,
to the adjusted firstiThe weight of the mean square error loss function of the continuous training data in the optimal prediction network; where the loss value is related to the phase difference value.
2. The digital calibration method for the low-permeability magnetic powder core current transformer according to claim 1, characterized in that the method for obtaining the average similarity between the output values of the target network before and after the exchange of training data comprises: and calculating the similarity between the output values of the target network before and after the training data are exchanged each time, wherein the average value of all the obtained similarities is the average similarity.
3. The digital calibration method of the low permeability magnetic powder core current transformer according to claim 2, wherein the method for calculating the similarity between the output values of the target network before and after each training data exchange comprises: and calculating the similarity distance by using a dynamic time warping algorithm, and obtaining the similarity according to the similarity distance.
4. The method for digitally calibrating a low permeability flux-cored current transformer of claim 1, wherein the loss function of the initial prediction network is a mean square error loss function weighted by the value of the output current.
5. The digital calibration method for a low permeability magnetic powder core current transformer as claimed in claim 1, wherein the absolute deviation and average similarity
The ratio of (d) to (d) is inversely related to the weight of the mean square error loss function.
6. A digital calibration system for a low permeability flux core current transformer comprising a memory, a processor and a computer program stored in said memory and run on said processor, wherein said processor when executing said computer program implements the steps of the method according to any of claims 1-5.
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| WO2021007812A1 (en) * | 2019-07-17 | 2021-01-21 | 深圳大学 | Deep neural network hyperparameter optimization method, electronic device and storage medium |
| CN110632545B (en) * | 2019-09-25 | 2021-09-21 | 国网江苏省电力有限公司电力科学研究院 | Regression neural network-based error risk assessment method and device for electronic transformer |
| CN111814959A (en) * | 2020-06-30 | 2020-10-23 | 北京百度网讯科技有限公司 | Model training data processing method, device and system and storage medium |
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