CN115480204A - Current transformer operation error online evaluation optimization method based on big data deduction - Google Patents
Current transformer operation error online evaluation optimization method based on big data deduction Download PDFInfo
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Abstract
The invention relates to a big data deduction-based current transformer operation error online evaluation optimization method, which comprises the following steps of: collecting secondary side three-phase current measurement data when current transformers on the same bus in the transformer substation normally run, and selecting stable section three-phase current measurement data by using an averaging method; dividing three-phase current measurement data of a stable section into data sets of various measuring ranges by adopting a current-carrying classification method; acquiring a training data set of a training error estimation neural network model; respectively training by using the data sets of all the measuring ranges to obtain corresponding error estimation neural network models; inputting the secondary side current data of the current transformer to be evaluated into a corresponding trained error estimation neural network model, and outputting to obtain the state information of the current transformer to be evaluated; the method improves the evaluation accuracy, gets rid of the dependence on power failure and a physical standard, is suitable for current transformers with different principles or accuracy levels, and has the advantages of high precision, strong usability and the like.
Description
Technical Field
The invention relates to the technical field of electric power measurement online monitoring, in particular to a current transformer operation error online evaluation optimization method based on big data deduction.
Background
Current transformers (Current transformers) are important measurement devices in electrical power systems. The primary winding is connected in series in a main transmission and transformation loop, and the secondary winding is respectively connected to equipment such as a measuring instrument, a relay protection or an automatic device and the like according to different requirements and is used for changing large current of the primary loop into small current of the secondary side for the measurement and control protection metering equipment to safely collect. The method is accurate and reliable, and has great significance for safe operation, control protection, electric energy metering and trade settlement of the power system.
At present, the error evaluation of the current transformer generally adopts an off-line checking method or an on-line checking method, and the ratio difference and the angle difference of the current transformer are obtained through a direct comparison method. However, these methods have long verification period, complicated field wiring and low working efficiency. In order to perfect a current transformer error state evaluation system, a current transformer error state evaluation method needs to be established urgently, the problem that the error of the current transformer is out of tolerance is found, the out-of-limit running time of the error of the current transformer is reduced, and the detection work of the current transformer is guided, so that the fairness of electric energy metering is ensured.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the current transformer operation error online evaluation optimization method based on big data deduction, which improves the evaluation accuracy, gets rid of the dependence on power failure and a material object standard device, is suitable for current transformers with different principles or accuracy levels, and has the advantages of high precision, strong usability and the like.
According to a first aspect of the invention, a current transformer operation error online evaluation optimization method based on big data deduction is provided, and the method comprises the following steps:
step 2, dividing the stable section three-phase current measurement data into data sets of various measuring ranges by adopting a current-carrying classification method;
step 3, acquiring a training data set of a training error estimation neural network model according to the physical relation between the individual error of each current transformer and the sum of the current vectors of the measurement nodes, wherein the training data set comprises an input data set and an output data set of the error estimation neural network model; the input data set is data B of a product of a true value and a turn ratio of a current transformer error and sequence data X of a secondary current value of the current transformer, the output data set is rated transformation ratio sequence data T of the current transformer, and TX = B;
step 4, respectively training by using the data sets of the measuring ranges in the step 3 to obtain corresponding error estimation neural network models; and inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and outputting to obtain the state information of the current transformer to be evaluated.
On the basis of the technical scheme, the invention can be improved as follows.
Optionally, the process of selecting stable-segment three-phase current measurement data by using an averaging method in step 1 includes:
performing time scale inspection on the collected current amplitude data, and performing mean value processing on the missing current amplitude data according to a formula (1) to obtain stable section three-phase current data;
i is the data point number with missing time scale check result, amp o For raw current amplitude data, amp calculates the data points for which the current amplitude is missing.
Optionally, the process of selecting stable-segment three-phase current measurement data in step 1 further includes:
and when the line current is lower than the rated current and exceeds a certain range, screening the three-phase current measurement data with the rated range of 50% or more.
Optionally, the dividing, in the step 2, the stable-segment three-phase current measurement data into data sets of various ranges includes:
、
、
and
；
wherein, the first and the second end of the pipe are connected with each other,
。
optionally, the method for acquiring the training data set in step 3 includes:
step 301, defining and determining a ratio difference between current values of a primary side line and a secondary side line
Phase difference of
I represents the ith line;
302, according to the current measurement value of the secondary side line
And the difference of said ratio
Is out of phase with the phase
Calculating to obtain the amplitude of the current true value of the primary side line
(ii) a m and r both represent the phase sequence in a three-phase circuit;
step 303, according to the amplitude of the true current value of the primary side line
Obtaining the true value of the current of the primary side circuit
；
304, obtaining the current according to the kirchhoff current law
(ii) a The product TX of the vector X and the vector T is used for representing the product of the sequence data of the secondary current value of each current transformer and the rated transformation ratio sequence data of the corresponding current transformer to obtain
I.e., TX =0;
step 305, calculating and recording the product of the current deviation true value and the turn ratio as B.
Optionally, the ratio difference in step 301
(ii) a Wherein the content of the first and second substances,
，
and
are the primary and secondary side current measurements respectively,
and
the number of coil turns on the primary side and the secondary side, respectively.
Optionally, the current measurement of the secondary side line in step 302 is performed
；
，
The phase of the current true value of the primary side line is shown, e represents a natural constant e, and j represents a complex number;
the true current value of the primary-side line in step 303
。
Optionally, step 4 further includes:
inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and calculating a ratio error estimation value according to the output of the error estimation neural network model
；
Setting the ratio error estimate
Determining corresponding interval ranges according to the interval ranges to which the ratio error estimation values of the current transformers to be evaluated belongStatus information.
Optionally, the ratio error estimate
；
And representing the corrected transformation ratio of the ith transformer in the corrected transformation ratio sequence data of the current transformer output by the error estimation neural network model.
Optionally, each state of the current transformer includes: normal, alarm and abnormal.
The invention provides a big data deduction-based current transformer operation error online evaluation optimization method, which comprises the steps of constructing a physical relation between each CT individual error and a current vector sum of a measured value node, taking deviation distribution as distribution, training characteristics of the distribution and current transformation ratio data by using a RBM neural network model, and inputting the current data and the error into the obtained model to obtain a CT error estimated value; the method improves the evaluation accuracy, gets rid of the dependence on power failure and a material object standard, is suitable for current transformers with different principles or accuracy levels, and has the advantages of high precision, strong usability and the like.
Drawings
Fig. 1 is a flowchart of an online evaluation optimization method for current transformer operating errors based on big data deduction provided by the present invention;
FIG. 2 (a) is a wiring diagram of an embodiment of a single-phase current transformer;
fig. 2 (b) is an equivalent circuit diagram of fig. 2 (a).
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of an online evaluation optimization method for current transformer operating errors based on big data deduction, as shown in fig. 1, the online evaluation optimization method includes:
And 2, dividing the stable-section three-phase current measurement data into data sets of various measuring ranges by adopting a current-carrying classification method.
Step 3, acquiring a training data set of a training error estimation neural network model according to the physical relation between the individual error of each current transformer and the current vector sum of the measurement node, wherein the training data set comprises an input data set and an output data set of the error estimation neural network model; the input data set is data B of a product of a true value and a turn ratio of a current transformer error and sequence data X of a secondary current value of the current transformer, the output data set is rated transformation ratio sequence data T of the current transformer, and TX = B.
Step 4, respectively training the data sets of the measuring ranges in the step 3 to obtain corresponding error estimation neural network models; and inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and outputting to obtain the state information of the current transformer to be evaluated.
The invention provides a big data deduction-based current transformer operation error online evaluation optimization method, which comprises the steps of constructing a physical relation between each CT individual error and a current vector sum of a measured value node, taking deviation distribution as distribution, training characteristics of the distribution and current transformation ratio data by using a RBM neural network model, and inputting current data and errors into the obtained model to obtain a CT error estimated value; the method improves the evaluation accuracy, gets rid of the dependence on power failure and a material object standard, is suitable for current transformers with different principles or accuracy levels, and has the advantages of high precision, strong usability and the like.
Example 1
In a possible embodiment mode, the process of selecting the stable-segment three-phase current measurement data by using the averaging method in the step 1 comprises the following steps:
the current fluctuation in the power grid is large, and more data breakpoints exist in current data, so that the collected current amplitude data are subjected to time scale inspection in sequence, and the missing current amplitude data are processed by an improved averaging method according to a formula (1), so that the problem of the current data breakpoints is solved.
i is the data point number with missing time scale check result, amp o For raw current amplitude data, amp calculates the data points for which the current amplitude is missing.
In a possible embodiment, the process of selecting the stable section three-phase current measurement data in step 1 further includes:
when the line current is lower than the rated current and exceeds a certain range, three-phase current measurement data with the rated range of 50% or more is screened.
For the current transformer, when the line current is lower than the rated current, the error of the current transformer is larger, and the data quality is lower, so that the current data with the rated range of 50% or more is screened.
And 2, dividing the stable-section three-phase current measurement data into data sets of various measuring ranges by adopting a current-carrying classification method.
In a possible embodiment, the step 2 of dividing the stable-segment three-phase current measurement data into data sets of various measuring ranges includes:
、
、
and
。
in order to adapt to accurate evaluation of the metering state of the mutual inductor under different current amplitudes, the current-carrying classification method is adopted, and the current data of the stable section is obtained according to the rated current
The percentages are divided into four sets:
wherein the content of the first and second substances,
。
step 3, acquiring a training data set of a training error estimation neural network model according to the physical relation between the individual error of each current transformer and the sum of the current vectors of the measurement nodes, wherein the training data set comprises an input data set and an output data set of the error estimation neural network model; the input data set is data B of a product of a true value and a turn ratio of a current transformer error and sequence data X of a secondary current value of the current transformer, the output data set is rated transformation ratio sequence data T of the current transformer, and TX = B.
In a possible embodiment, the method for acquiring the training data set in step 3 includes:
step 301, defining and determining a ratio difference between current values of a primary side line and a secondary side line
Phase difference of
And i represents the ith line.
302, according to the current measurement value of the secondary side line
And the difference of said ratio
Is out of phase with the phase
Calculating to obtain the amplitude of the current true value of the primary side line
(ii) a And m and r both represent the phase sequence in the three-phase circuit, namely m and r represent the A phase, the B phase or the C phase.
Step 303, according to the magnitude of the true current value of the primary side line
Obtaining the true value of the current of the primary side circuit
。
304, obtaining the current according to the kirchhoff current law
(ii) a The product TX of the vector X and the vector T is used for representing the product of the sequence data of the secondary current value of each current transformer and the rated transformation ratio sequence data of the corresponding current transformer to obtain
I.e. TX =0.
In step 305, the product of the true value of the current deviation and the turns ratio (i.e. the influence on the primary side) is calculated and recorded as B.
Specifically, fig. 2 (a) is a wiring diagram of an embodiment of the single-phase current transformer, and fig. 2 (b) is an equivalent circuit diagram of fig. 2 (a).
Defining a current transformer ratio difference and a phase difference:
the rated current ratio is:
using magnetic potential to balance:
due to the existence of
Then, a ratio difference and a phase difference exist between the primary current value and the secondary current value, and the ratio error is:
wherein the content of the first and second substances,
for the purpose of the rated current ratio,
and
are the primary and secondary side current measurements respectively,
and
the number of coil turns on the primary side and the secondary side, respectively.
And calculating the relation between the three-phase current measurement value and the current measurement truth value.
The three-phase current measured values of the evaluation group form a test data set, and the test data set consists of real values of current of each line on the same bus and individual errors (ratio difference and phase difference) of different CTs. Determined by kirchhoff's current law: the node current vector sum constructed by the true values of the line currents on the same bus is 0, but the measured data set comprises the individual error of each CT, and the individual errors are different due to the physical difference of each CT, so that the node current vector sum constructed by the measured values is not 0, and the node current vector sum is related to the individual error of each CT.
The true value of the primary line current is:
wherein the content of the first and second substances,
represents the true value of the current in the primary side line,
represents the magnitude of the current true value of the primary line, r represents the phase sequence (r = a, B, C), i is the ith line,
the phase of the true value of the current of the primary line is shown.
The current measurement of the secondary side line is noted as:
wherein, the first and the second end of the pipe are connected with each other,
for each true value of the line current,
for each of the line current measurements,
for the purpose of the corresponding ratio difference,
for the corresponding phase difference, n lines are provided, m represents the phase sequence (m = A, B, C),
，
the phase of the true value of the current on the primary line is represented by e, which represents the natural constant e, and j represents the complex number.
According to kirchhoff's current law, there are:
wherein:
for each true value of the line current,
for each of the line current measurements,
for the purpose of the corresponding ratio difference,
for the corresponding phase difference, n lines are provided.
And establishing a judgment model according to the kirchhoff current law.
On the basis of current-carrying classification, the four data sets are respectively subjected to the following operations to establish a model. According to kirchhoff's law of current
In the ideal case, a node is flowed by a currentThe value is 0. Recording three-phase current data Amp (i) of the stable segment as xi (i)<N) = n), n is the number of lines, namely the number of current transformers, ti is the ratio of the secondary side to the primary side (rated ratio), and the ratio can be obtained from kirchhoff's current law KCL
The product of the sequence data of the secondary current value of each current transformer and the rated transformation ratio sequence data of the corresponding current transformer is represented by the product TX of the vector X and the vector T, namely TX =0. Recording data of current transformer offline detection in the region under the condition to obtain the error (ratio difference) true value E of the current transformer, calculating and counting the rated transformation ratio sequence data of the current transformer multiplied by the sequence data of the secondary current value, namely TX, calculating and recording the product of the current deviation true value and the turn ratio, namely the influence on the primary side, as B, (the ratio of the current deviation true value to the turn ratio is calculated and recorded as
) The mean is μ and the standard deviation is σ, i.e. B-N (μ, σ), and T, X, and B are all wide-area CT data at a certain time, and TX = B is obtained in summary.
Step 4, respectively training by using the data sets of the measuring ranges in the step 3 to obtain corresponding error estimation neural network models; and inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and outputting to obtain the state information of the current transformer to be evaluated.
The method comprises the steps of taking T, X and B data of different time as data sets and using the data sets as limited Boltzmann machine (RBM) neural network models, wherein the models are special topological structures in neural networks, are suitable for distributed input, are poor in performance, but cannot influence efficiency due to data volume in the scene, taking X, B-N (mu, sigma) as input data and T as output data, obtaining a model M through training, and obtaining 4 models according to a current-carrying grading result, wherein the models are respectively marked as M1, M2, M3 and M4.
In a possible embodiment, step 4 further includes:
two of the current transformers to be evaluatedAfter the secondary side current data is input into the corresponding trained error estimation neural network model, the ratio error estimation value is calculated according to the output of the error estimation neural network model
。
And when the transformer state is predicted, importing a corresponding model M according to the current-carrying classification result, namely importing B and secondary side current sequence data X 'of the transformer at the moment to be detected, outputting T', which is the corrected transformation ratio, and calculating a ratio error estimation value.
In one possible embodiment, the ratio error estimate
；
And representing the modified transformation ratio of the ith transformer in the modified transformation ratio sequence data of the current transformer output by the error estimation neural network model.
Setting ratio error estimate
And determining corresponding state information according to the interval range to which the ratio error estimation value of the current transformer to be evaluated belongs.
In one possible embodiment, the respective states of the current transformer include: normal, alarm and abnormal.
Specifically, according to the current-carrying classification result, models M1, M2, M3 and M4 are correspondingly used for the current data of the secondary side of the current transformer, and error estimation values are calculated
And outputting the state information of the current transformer by a threshold value method.
Setting a ratio error estimate
An example of each state of the current transformer to be evaluated corresponding to each interval range may be:
1) When error occurs
Fall within the interval [ -0.1776%,0.1776%]The probability of the corresponding CT being out of tolerance is no higher than 38.26%, giving "normal" information.
2) When error occurs
Falls within the interval [ -0.2735%, -0.1776%), (0.1776%, 0.2735%]Then the probability of the corresponding CT being out of tolerance will be higher than 38.26% and not higher than 83.65%, giving an "alarm" message.
3) When error occurs
Falls within the interval [ - ∞, -0.2735%), (0.2735%, ∞]The probability of the corresponding CT being out of tolerance is higher than 83.65%, and the abnormal information is given.
The embodiment of the invention provides a big data deduction-based current transformer operation error online evaluation optimization method, which comprises the steps of constructing a physical relation between each CT individual error and a current vector sum of a measured value node, taking deviation distribution as distribution, training characteristics of the distribution and current transformation ratio data by using a RBM neural network model, and inputting current data and errors into the obtained model to obtain a CT error estimation value; the method improves the evaluation accuracy, gets rid of the dependence on power failure and a material object standard, is suitable for current transformers with different principles or accuracy levels, and has the advantages of high precision, strong usability and the like.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. The online evaluation optimization method for the operation error of the current transformer based on big data deduction is characterized by comprising the following steps of:
step 1, collecting secondary side three-phase current measurement data when current transformers on the same bus in a transformer substation normally operate, and selecting stable section three-phase current measurement data by using an averaging method;
step 2, dividing the stable section three-phase current measurement data into data sets of various measuring ranges by adopting a current-carrying classification method;
step 3, acquiring a training data set of a training error estimation neural network model according to the physical relation between the individual error of each current transformer and the sum of the current vectors of the measurement nodes, wherein the training data set comprises an input data set and an output data set of the error estimation neural network model; the input data set is data B of a product of a true value and a turn ratio of a current transformer error and sequence data X of a secondary current value of the current transformer, the output data set is rated transformation ratio sequence data T of the current transformer, and TX = B;
step 4, respectively training by using the data sets of the measuring ranges in the step 3 to obtain corresponding error estimation neural network models; and inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and outputting to obtain the state information of the current transformer to be evaluated.
2. The online evaluation optimization method of claim 1, wherein the step 1 of selecting stable three-phase current measurement data by using an averaging method comprises the following steps:
performing time scale inspection on the collected current amplitude data, and performing mean value processing on the missing current amplitude data according to a formula (1) to obtain stable section three-phase current data;
i is the data point number with missing time scale check result, amp o For raw current amplitude data, amp calculates the data points for which the current amplitude is missing.
3. The online evaluation optimization method according to claim 1, wherein the step 1 of selecting stable-segment three-phase current measurement data further comprises:
and when the line current is lower than the rated current and exceeds a certain range, screening the three-phase current measurement data with the rated range of 50% or more.
4. The online evaluation optimization method of claim 1, wherein the step 2 of dividing the stable section three-phase current measurement data into data sets of various measurement ranges comprises:
、
、
and
；
wherein, the first and the second end of the pipe are connected with each other,
。
5. the online evaluation optimization method according to claim 1, wherein the method for acquiring the training data set in step 3 comprises:
step 301, defining and determining a ratio difference between current values of a primary side line and a secondary side line
Phase difference with
I represents the ith line;
302, according to the current measurement value of the secondary side line
And the difference of the ratio
Is out of phase with the phase
Calculating to obtain the amplitude of the current true value of the primary side line
(ii) a m and r both represent the phase sequence in a three-phase circuit;
step 303, according to the magnitude of the true current value of the primary side line
Obtaining the true value of the current of the primary side circuit
；
Step 304, obtaining the current according to the kirchhoff current law
(ii) a The product TX of the vector X and the vector T is used for representing the product of the sequence data of the secondary current value of each current transformer and the rated transformation ratio sequence data of the corresponding current transformer to obtain
I.e., TX =0;
step 305, calculating and recording the product of the current deviation true value and the turn ratio as B.
6. The online evaluation optimization method of claim 5, wherein the ratio difference in step 301
(ii) a Wherein the content of the first and second substances,
，
and
are the primary and secondary side current measurements respectively,
and
the number of coil turns on the primary side and the secondary side, respectively.
7. The on-line evaluation optimization method of claim 5, wherein the current measurement of the secondary side line in step 302 is a current measurement of the secondary side line
；
，
The phase of the current truth value of the primary side circuit is shown, e represents a natural constant e, and j represents a complex number;
step 303 is to determine a true current value of the primary-side line
。
8. The online evaluation optimization method according to claim 5, wherein the step 4 further comprises:
inputting the secondary side current data of the current transformer to be evaluated into the corresponding trained error estimation neural network model, and calculating a ratio error estimation value according to the output of the error estimation neural network model
；
Setting the ratio error estimate
Determining corresponding state information according to the interval range to which the ratio error estimation value of the current transformer to be evaluated belongs.
9. The method of claim 8The online evaluation optimization method is characterized in that the ratio error estimation value
；
And representing the corrected transformation ratio of the ith transformer in the corrected transformation ratio sequence data of the current transformer output by the error estimation neural network model.
10. The online evaluation optimization method of claim 8, wherein the respective states of the current transformer comprise: normal, alarm and abnormal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116106815A (en) * | 2023-02-06 | 2023-05-12 | 广州市德珑电子器件有限公司 | Method and system for reducing measurement error of current transformer |
| CN117151932B (en) * | 2023-10-27 | 2024-01-12 | 武汉纺织大学 | Method and system for predicting error state of non-stationary output current transformer |
| CN117991171A (en) * | 2024-04-03 | 2024-05-07 | 国网山东省电力公司营销服务中心(计量中心) | Method, system, medium, equipment and product for monitoring metering error of mutual inductor |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116106815A (en) * | 2023-02-06 | 2023-05-12 | 广州市德珑电子器件有限公司 | Method and system for reducing measurement error of current transformer |
| CN116106815B (en) * | 2023-02-06 | 2023-10-27 | 广州市德珑电子器件有限公司 | Method and system for reducing measurement error of current transformer |
| CN117151932B (en) * | 2023-10-27 | 2024-01-12 | 武汉纺织大学 | Method and system for predicting error state of non-stationary output current transformer |
| CN117991171A (en) * | 2024-04-03 | 2024-05-07 | 国网山东省电力公司营销服务中心(计量中心) | Method, system, medium, equipment and product for monitoring metering error of mutual inductor |
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