Automatic error compensation device and method for electric energy meter

Abstract

The present invention relates to the technical field of smart meters. The present invention discloses an automatic error compensation device and method for an electric energy meter, comprising constructing a corresponding digital twin model based on real-time operating information and preset structural parameter information, obtaining real-time error information based on the digital twin model, predicting future error information based on the real-time error information, calculating harmonic flux values ​​and comprehensive flux values ​​based on the real-time parameter information, and finally inputting the real-time measurement information, future error information and comprehensive flux values ​​into a pre-constructed error compensation model to obtain target measurement information. The present invention predicts future error trends based on the error information, thereby effectively solving the problem of lag in error compensation in the prior art. In addition, adaptive adjustment to complex dynamic harmonic environments is achieved, overcoming the limitation of the prior art that it is difficult to adapt to changes in operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent meters, and more particularly to an automatic error compensation device and method for electric energy metering meters. Background Art

[0002] Electronic measuring equipment is extremely sensitive to harmonics in the power system. As a key device for calculating the economic quantity of power grid transmission, the measurement accuracy of electric energy meters is directly related to the economic benefits of power generation and transmission in the power system. Although relevant content has been developed in the existing technology to compensate for the measurement information of electric energy meters, thereby improving the accuracy of electric energy meters, certain problems still exist.

[0003] For example, Chinese patent application publication number CN113589216A discloses an error compensation method, device, and system for an electric energy meter based on direct current and even harmonics. This prior art sets an error compensation function and an error compensation strategy for the electric energy meter to be tested, so that the electric energy meter needs to compensate for the metering error of the electric energy meter in real time according to the current flowing through the meter.

[0004] Although methods for compensating for the measurement information of electric energy meters have been proposed in the prior art, the above-mentioned technologies have a certain lag when compensating through error compensation functions and metering error data. In addition, the prior art is difficult to achieve adaptive adjustment according to the real-time changing operating conditions of the electric energy meter (such as complex dynamic harmonic environments), and thus the compensation effect is relatively limited.

[0005] In view of this, the present invention proposes an automatic error compensation device and method for an electric energy meter to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an automatic error compensation device and method for an electric energy meter.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, a method for automatic error compensation of an electric energy meter is provided, comprising:

[0009] Acquire real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information. The real-time parameter information refers to relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to the operation information of the internal components of the electric energy meter;

[0010] Obtain real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate harmonic flux values ​​and comprehensive flux values ​​based on real-time parameter information;

[0011] The real-time measurement information, future error information and integrated flux value are input into the pre-built error compensation model to obtain the target measurement information.

[0012] Furthermore, the real-time operation information includes voltage coil temperature, current coil temperature, turntable speed value, turntable acceleration value, and magnetic field strength value. The method of constructing a corresponding digital twin model based on the real-time operation information and preset structural parameter information includes:

[0013] A basic structure sub-model is constructed according to the preset structural parameter information, a dynamic response sub-model is constructed according to the turntable speed value and turntable acceleration value in the real-time operation information, and a thermal-magnetic coupling sub-model is constructed according to the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information. The basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model are integrated to obtain a digital twin model.

[0014] Furthermore, the method of constructing a dynamic response sub-model according to the turntable speed value and the turntable acceleration value in the real-time operation information includes:

[0015] F net =m×a-k f ×ω;

[0016] Among them, F net is the net driving force, m is the mass of the turntable, a is the acceleration value of the turntable, k f is the friction coefficient, and ω is the turntable speed.

[0017] Furthermore, a method for constructing a thermal-magnetic coupling sub-model based on the voltage coil temperature, current coil temperature, and magnetic field strength values ​​in the real-time operation information includes:

[0018] δ(T V ,T C ,B)=δ0+q1(T V -T0)+q2(T C -T0)+q3(B-B0);

[0019] Among them, δ(T V ,T C ,B) is characterized by the electric energy meter at T V 、T C , B, δ0 represents the error generated by the electric energy meter under the joint action of T0 and B0, T V is the voltage coil temperature, T Cis the current coil temperature, B is the magnetic field strength value, T0 is the standard coil temperature, B0 is the standard magnetic field strength, q1, q2, q3 are all weight factors.

[0020] Furthermore, the method for obtaining real-time error information based on the digital twin model includes:

[0021] By simulating the digital twin model, the magnetic flux distribution information and speed change information are obtained. A magnetic flux distribution map is constructed based on the magnetic flux distribution information, and a speed change curve is constructed based on the speed change information. The magnetic flux distribution map and speed change curve are used as real-time error information.

[0022] Furthermore, the method for predicting future error information based on real-time error information includes:

[0023] Input the real-time error information into the pre-built error prediction model to obtain future error information;

[0024] The method for constructing the error prediction model includes:

[0025] Preset the sliding step size and sliding window length; convert the historical error information into multiple training samples using the sliding window method, use the training samples as the input of the error prediction model, and use the historical error information after the predicted sliding step size as the output. The actual historical error information of each training sample is used as the prediction target, and the prediction accuracy is used as the training target to train the error prediction model; generate an error prediction model that predicts future error information based on real-time error information.

[0026] Furthermore, the real-time parameter information includes an angular frequency value, an initial phase angle, a phase lag angle, a coil inductance value, a harmonic voltage amplitude value, and a coil resistance value. The method for calculating the harmonic magnetic flux value based on the real-time parameter information includes:

[0027]

[0028] Among them, Φ h is the harmonic flux value under the action of the hth harmonic, U h is the harmonic voltage amplitude generated by the hth harmonic, ω h is the angular frequency value generated by the hth harmonic, L is the coil inductance, and R is the coil resistance.

[0029] Furthermore, the method for calculating the comprehensive magnetic flux value based on the real-time parameter information includes:

[0030] Φ h (t)=∑ t Φ h cos[ω h (t)-θ h -α h ];

[0031] Among them, Φ h (t) is the comprehensive magnetic flux value under the action of the hth harmonic, cos[·] is the cosine function, θ h is the initial phase angle under the action of the hth harmonic, α h is the phase lag angle under the action of the hth harmonic, ω h (t) is represented by the angular frequency value of the hth harmonic changing with time t.

[0032] In a second aspect, an automatic error compensation device for an electric energy meter is provided, which is used to implement the above-mentioned automatic error compensation method for the electric energy meter, comprising:

[0033] The first processing module is used to obtain real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and to construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information. The real-time parameter information refers to the relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to the operation information of the internal components of the electric energy meter;

[0034] The second processing module is used to obtain real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate the harmonic flux value and the comprehensive flux value based on the real-time parameter information;

[0035] Compensation module: used to input real-time measurement information, future error information and integrated flux value into the pre-built error compensation model to obtain target measurement information.

[0036] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the automatic error compensation method of the electric energy meter is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention first constructs a corresponding digital twin model based on real-time operation information and preset structural parameter information, then obtains real-time error information based on the digital twin model, predicts future error information based on the real-time error information, calculates harmonic flux values ​​and comprehensive flux values ​​based on the real-time parameter information, and finally inputs the real-time measurement information, future error information and comprehensive flux values ​​into the pre-built error compensation model to obtain target measurement information. By constructing a digital twin model, the present invention can obtain error information of electric energy metering instruments in real time and predict future error trends based on the error information, thereby effectively solving the problem of lag in error compensation in the prior art. In addition, by calculating the harmonic flux values ​​and comprehensive flux values ​​and inputting them into the error compensation model together with the real-time measurement information and future error information, adaptive adjustment to complex dynamic harmonic environments is achieved, overcoming the limitation of the prior art that it is difficult to adapt to changes in operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the flow of the automatic error compensation method of the electric energy meter in the present invention;

[0040] Figure 2 It is a structural diagram of the automatic error compensation device of the electric energy meter in the present invention;

[0041] Figure 3 It is a structural diagram of the internal components of the electric energy meter in the present invention;

[0042] Figure 4 This is a schematic diagram of the process of constructing the corresponding digital twin model in the present invention.

[0043] Reference numerals:

[0044] 10. Voltage coil magnetic circuit; 20. Current coil magnetic circuit; 30. Turntable; 40. Permanent magnet. DETAILED DESCRIPTION

[0045] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] Example 1

[0047] See also Figure 1 As shown, this embodiment discloses an automatic error compensation method for an electric energy meter, including:

[0048] S10: Acquire real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information, wherein the real-time parameter information refers to relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to operation information of internal components of the electric energy meter;

[0049] It should be noted that during the operation of the electric energy meter, the metering accuracy is reduced due to the influence of harmonics in the power system. Harmonics can cause nonlinear responses of the internal components of the electric energy meter, thereby leading to metering errors, such as distortion of the main magnetic flux. The above-mentioned real-time measurement information can be real-time current information, real-time voltage information, and real-time power information.

[0050] It is understandable that the real-time operation information is the operation information of the internal components of the electric energy meter, such as Figure 3 As shown, the internal components of the electric energy meter include at least a voltage coil magnetic circuit 10, a current coil magnetic circuit 20, a turntable 30, and a permanent magnet 40. When the electric energy meter is used for measurement, the magnetic flux generated by the voltage coil magnetic circuit 10 and the current coil magnetic circuit 20 interact to form a rotational torque, driving the turntable 30 to rotate, and finally displayed by the meter on the electric energy meter.

[0051] From the above content, it can be seen that the real-time operating information includes the voltage coil temperature, the current coil temperature, the turntable speed value, the turntable acceleration value and the magnetic field strength value. It is not difficult to understand that the voltage coil temperature and the current coil temperature are both obtained by the internal Hall current sensor, the turntable speed value and the turntable acceleration value can be monitored and obtained by the vibration sensor. The magnetic field strength value refers to the average magnetic field strength around the permanent magnet, and the magnetic field strength value can be obtained by the Hall effect sensor. This embodiment will not go into too much detail about this.

[0052] It should be added that the above-mentioned structural parameter information refers to the structural parameters of each internal component pre-stored in the data, such as the number of coil windings and coil resistance of the voltage coil magnetic circuit, the radius and mass of the turntable, the magnet size and hysteresis loss value of the permanent magnet, etc. The hysteresis loss value represents the energy loss generated by the permanent magnet during repeated magnetization, and the hysteresis loss value can be obtained through experiments.

[0053] like Figure 4 As shown, the method for constructing a corresponding digital twin model based on real-time operation information and preset structural parameter information includes:

[0054] A basic structure sub-model is constructed according to the preset structural parameter information, a dynamic response sub-model is constructed according to the turntable speed value and turntable acceleration value in the real-time operation information, and a thermal-magnetic coupling sub-model is constructed according to the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information. The basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model are integrated to obtain a digital twin model.

[0055] It should be noted that the basic structure sub-model represents the static physical structure inside the electric energy meter, the dynamic response sub-model represents the mechanical response of the internal components of the electric energy meter during dynamic operation, and the thermal-magnetic coupling sub-model represents the dynamic impact of the interaction between temperature and magnetic field on the performance of the electric energy meter.

[0056] The above-mentioned integration of the basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model refers to integrating the basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model in the existing multi-physics field simulation software, and using the multi-module and multi-physics field coupling functions of these software to associate the input, output and coupling relationship of each sub-model, so as to build a unified digital twin model. The existing multi-physics field simulation software can be COMSOL Multiphysics or Ansys Workbench. Taking Ansys Workbench as an example, the Ansys Mechanical module is used to build the dynamic response sub-model, the Ansys Maxwell module is used to simulate the coupling relationship between the electromagnetic field and temperature, and the Ansys Fluent or Thermal module is used to calculate heat conduction. In the Workbench environment, the modules are connected through data flow, and the "multi-physics field simulation" function is used to solve the thermal-magnetic-mechanical coupling in a unified way.

[0057] The method of constructing a dynamic response sub-model based on the turntable speed value and turntable acceleration value in the real-time operation information includes:

[0058] F net =m×a-k f ×ω;

[0059] Among them, F net is the net driving force, m is the mass of the turntable, a is the acceleration value of the turntable, k f is the friction coefficient, and ω is the turntable speed.

[0060] It should be added that the net driving force refers to the residual force that can cause the turntable to accelerate after deducting the resistance (including friction and other resistance) from the total driving force acting on the turntable. The net driving force is the core variable of the dynamic response sub-model, which is used to describe the motion state of the turntable and is the driving input of the entire dynamic response sub-model. The friction coefficient is usually related to the turntable material, bearing status, lubrication conditions, etc. The friction coefficient can be obtained through experiments.

[0061] The method of constructing a thermal-magnetic coupling sub-model based on the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information includes:

[0062] δ(T V ,T C ,B)=δ0+q1(T V -T0)+q2(T C -T0)+q3(B-B0);

[0063] Among them, δ(T V ,T C ,B) is characterized by the electric energy meter at T V 、T C , B, δ0 represents the error generated by the electric energy meter under the joint action of T0 and B0, T V is the voltage coil temperature, T C is the current coil temperature, B is the magnetic field strength value, T0 is the standard coil temperature, B0 is the standard magnetic field strength, q1, q2, q3 are all weight factors.

[0064] It should be noted that the above-mentioned standard coil temperature represents the reference operating temperature of the coil and is a reference value for calculating the temperature deviation. The standard magnetic field strength represents the reference magnetic field strength of the electric energy meter under calibration conditions and is a reference value for calculating the magnetic field deviation. Both the standard coil temperature and the standard magnetic field strength can be pre-set according to actual conditions, and this embodiment will not go into details about this.

[0065] In this embodiment, the above steps are combined in sequence to ensure comprehensive acquisition of the operating status of the electric energy meter, and this information is comprehensively analyzed and coupled through the digital twin model. This global analysis can more accurately identify the sources of errors (such as flux distortion, speed changes, etc.) and their dynamic effects, providing strong support for subsequent error prediction and compensation.

[0066] S20: Acquire real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate harmonic magnetic flux values ​​and comprehensive magnetic flux values ​​based on the real-time parameter information;

[0067] In this embodiment, the method for obtaining real-time error information based on the digital twin model includes:

[0068] By simulating the digital twin model, the magnetic flux distribution information and speed change information are obtained. A magnetic flux distribution map is constructed based on the magnetic flux distribution information, and a speed change curve is constructed based on the speed change information. The magnetic flux distribution map and speed change curve are used as real-time error information.

[0069] In this embodiment, the magnetic flux distribution information represents the distribution of magnetic flux density in the internal magnetic circuit of the electric energy meter and its dynamic changes. The speed change information represents the changes in the speed and acceleration of the turntable during dynamic operation. The magnetic flux distribution information includes at least the main magnetic flux density, harmonic magnetic flux density, and magnetic field direction distribution, and the speed change information includes at least the speed fluctuation amplitude and the turntable acceleration value. It can be understood that the magnetic flux distribution diagram can be constructed by Ansys Maxwell, and the speed change curve can be constructed by Ansys Mechanical. This embodiment will not go into details about this.

[0070] Methods for predicting future error information based on real-time error information include:

[0071] The real-time error information is input into the pre-built error prediction model to obtain future error information.

[0072] The method for constructing the error prediction model includes:

[0073] The sliding step size and sliding window length are preset; the historical error information is converted into multiple training samples using the sliding window method, the training samples are used as the input of the error prediction model, the historical error information after the predicted sliding step size is used as the output, the actual historical error information of each training sample is used as the prediction target, and the error prediction model is trained with the prediction accuracy as the training target; an error prediction model is generated that predicts future error information based on real-time error information, and the error prediction model is an LSTM model.

[0074] It should be noted that the sliding window method is a conventional technical means of the LSTM model, and the present invention will not be further explained in principle here; however, to facilitate the implementation of the present invention, the present invention provides the following example of the sliding window method:

[0075] Assume that the historical data [1, 2, 3, 4, 5, 6] is used to train an LSTM model. In this embodiment, the prediction time step is set to 1 as an example, the sliding step is set to 1, and the sliding window length is set to 3; then three groups of training samples and corresponding prediction target data are generated: [1, 2, 3], [2, 3, 4], and [3, 4, 5] are used as training samples, and [4], [5], and [6] are used as prediction targets respectively.

[0076] The prediction accuracy can be measured using mean square error or mean absolute error as the loss function, and the weights and biases of the model are updated through the backpropagation algorithm to generate an error prediction model.

[0077] It should be added that the real-time error information includes the magnetic flux distribution diagram and the speed change curve. Similarly, the future error information includes the magnetic flux distribution diagram and the speed change curve. The only difference between the future error information and the real-time error information is that the future error information is an estimate of the magnetic flux distribution diagram and the speed change curve that may appear in the future, reflecting the error trend that may be caused by future changes in operating conditions. Then, when the magnetic flux distribution diagram has areas with too high or too low density, it manifests as abnormal color distribution (for example, some areas are too bright or too dark), or the direction of the magnetic flux vector arrow shows irregular changes in the diagram, such as the appearance of "eddy current" or asymmetric distribution, it indicates that the measurement error trend of the electric energy meter is greater; then, when the speed change curve shows obvious nonlinear steep fluctuations, rapid acceleration or deceleration occurs in a short period of time, or the frequency of change (fluctuation period) of the speed change curve is significantly shortened, it indicates that the measurement error trend of the electric energy meter is greater.

[0078] In this embodiment, the real-time parameter information includes an angular frequency value, an initial phase angle, a phase lag angle, a coil inductance value, a harmonic voltage amplitude, and a coil resistance value. The angular frequency value refers to the angular frequency of the harmonic voltage. The initial phase angle refers to the initial phase of the harmonic component, that is, the phase offset of the harmonic signal at time t=0. The phase lag angle refers to the phase lag of the magnetic flux generated by the voltage coil relative to the voltage harmonic signal. The phase lag angle is determined by the inductance and resistance values ​​of the coil, indicating the degree to which the magnetic flux lags behind the voltage harmonic signal due to the impedance characteristics of the voltage coil. The coil inductance value refers to the inductance value of the voltage coil, and the coil inductance value represents the ability of the voltage coil to store magnetic field energy. The harmonic voltage amplitude refers to the voltage amplitude of each harmonic component in the voltage coil. The coil resistance value refers to the DC resistance value of the voltage coil.

[0079] Methods for calculating harmonic flux values ​​based on real-time parameter information include:

[0080]

[0081] Among them, Φ h is the harmonic flux value under the action of the hth harmonic, U h is the harmonic voltage amplitude generated by the hth harmonic, ω h is the angular frequency value generated by the hth harmonic, L is the coil inductance, and R is the coil resistance.

[0082] Methods for calculating comprehensive magnetic flux values ​​based on real-time parameter information include:

[0083] Φ h (t)=∑ t Φ h cos[ω h (t)-θ h -α h ];

[0084] Among them, Φ h (t) is the comprehensive magnetic flux value under the action of the hth harmonic, cos[·] is the cosine function, θ h is the initial phase angle under the action of the hth harmonic, α h is the phase lag angle under the action of the hth harmonic, ω h (t) is represented by the angular frequency value of the hth harmonic changing with time t.

[0085] It should be noted that the above-mentioned harmonic flux value represents a single harmonic flux component under the action of the hth harmonic, that is, the response of the voltage coil to a specific harmonic component. The comprehensive flux value represents the comprehensive effect of the flux of all harmonic components, that is, the flux performance under the joint action of all harmonic components in the voltage coil. The harmonic flux value is the flux component generated in the voltage coil by the high-order harmonic voltage or current component. The larger the harmonic flux value, the stronger the interference of the harmonic component on the main flux. Therefore, when the harmonic flux value is larger, the measurement error trend of the electric energy meter is greater. Similarly, when the comprehensive flux value is larger, the measurement error trend of the electric energy meter is greater.

[0086] S30: inputting the real-time measurement information, the future error information and the integrated magnetic flux value into a pre-built error compensation model to obtain target measurement information;

[0087] The error compensation model construction method includes:

[0088] Acquire a sample data set, wherein the sample data set includes historical measurement information, historical error information, historical integrated magnetic flux values, and historical target measurement information;

[0089] Divide the sample data set into a sample training set and a sample test set, and build a regression network;

[0090] The historical measurement information, historical error information, and historical integrated magnetic flux values ​​in the sample training set are used as input data of the regression network, and the historical target measurement information in the sample training set is used as output data of the regression network. The regression network is trained to obtain an initial regression network for predicting target measurement information.

[0091] The initial regression network is tested using a sample test set, and the output of the initial regression network with an error value smaller than the preset value is used as the error compensation model. The initial regression network is a deep neural network model.

[0092] It is understandable that, based on the above content, when areas with too high or too low density appear in the magnetic flux distribution diagram, it will manifest as abnormal color distribution (for example, the colors of some areas are too bright or too dark). In addition, when the speed change curve shows obvious nonlinear steep fluctuations, and rapid acceleration or deceleration occurs in a short period of time, it indicates that the measurement error trend of the electric energy meter is increasing.

[0093] Similarly, by analyzing the magnetic flux distribution diagram and the speed change curve, the deviation direction of the real-time measurement information can be further determined, for example:

[0094] If the color of some areas in the magnetic flux distribution map is too bright, it means that the magnetic flux density in this area is abnormally high, resulting in an excessively large induced electromotive force. The real-time measurement information is greater than the precise measurement information, resulting in a positive deviation. If the color of some areas in the magnetic flux distribution map is too dark, it means that the magnetic flux density in this area is significantly reduced, resulting in a low induced electromotive force. The real-time measurement information is less than the precise measurement information, resulting in a negative deviation.

[0095] Similarly, the speed change curve can also be analyzed. For example, a steep upward fluctuation (sharp acceleration in a short period of time) appears in the speed change curve: it means that the turntable speed has increased significantly, resulting in a larger mechanical torque sensed by the meter. At this time, the real-time measurement information is greater than the precise measurement information, resulting in a positive deviation. On the contrary, a steep downward fluctuation (sharp deceleration in a short period of time) appears in the speed change curve, resulting in a negative deviation.

[0096] This embodiment first constructs a corresponding digital twin model based on real-time operating information and preset structural parameter information, then obtains real-time error information based on the digital twin model, predicts future error information based on the real-time error information, calculates harmonic flux values ​​and comprehensive flux values ​​based on the real-time parameter information, and finally inputs the real-time measurement information, future error information, and comprehensive flux values ​​into a pre-built error compensation model to obtain target measurement information. By constructing a digital twin model, this embodiment can obtain error information of the electric energy meter in real time and predict future error trends based on the error information, thereby effectively solving the problem of lag in error compensation in the prior art. In addition, by calculating the harmonic flux values ​​and comprehensive flux values ​​and inputting them into the error compensation model together with the real-time measurement information and future error information, adaptive adjustment to complex dynamic harmonic environments is achieved, overcoming the limitation of the prior art that it is difficult to adapt to changes in operating conditions. The implementation of this embodiment can improve the measurement accuracy of the electric energy meter in a complex harmonic environment and enhance the compensation effect and real-time performance.

[0097] Example 2

[0098] See also Figure 2As shown, based on the same inventive concept, this embodiment discloses an automatic error compensation device for an electric energy meter. For details not provided in this embodiment, please refer to the description of the relevant parts in Example 1. The device includes:

[0099] The first processing module is used to obtain real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and to construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information. The real-time parameter information refers to the relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to the operation information of the internal components of the electric energy meter;

[0100] It should be noted that during the operation of the electric energy meter, the metering accuracy is reduced due to the influence of harmonics in the power system. Harmonics can cause nonlinear responses of the internal components of the electric energy meter, thereby leading to metering errors, such as distortion of the main magnetic flux. The above-mentioned real-time measurement information can be real-time current information, real-time voltage information, and real-time power information.

[0101] The method of constructing a corresponding digital twin model based on real-time operation information and preset structural parameter information includes:

[0102] A basic structure sub-model is constructed according to the preset structural parameter information, a dynamic response sub-model is constructed according to the turntable speed value and turntable acceleration value in the real-time operation information, and a thermal-magnetic coupling sub-model is constructed according to the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information. The basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model are integrated to obtain a digital twin model.

[0103] The method of constructing a dynamic response sub-model based on the turntable speed value and turntable acceleration value in the real-time operation information includes:

[0104] F net =m×a-k f ×ω;

[0105] Among them, F net is the net driving force, m is the mass of the turntable, a is the acceleration value of the turntable, k f is the friction coefficient, and ω is the turntable speed.

[0106] The method of constructing a thermal-magnetic coupling sub-model based on the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information includes:

[0107] δ(T V ,T C ,B)=δ0+q1(T V -T0)+q2(T C -T0)+q3(B-B0);

[0108] Among them, δ(T V ,T C ,B) is characterized by the electric energy meter at T V 、T C , B, δ0 represents the error generated by the electric energy meter under the joint action of T0 and B0, T V is the voltage coil temperature, T C is the current coil temperature, B is the magnetic field strength value, T0 is the standard coil temperature, B0 is the standard magnetic field strength, q1, q2, q3 are all weight factors.

[0109] The second processing module is used to obtain real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate the harmonic flux value and the comprehensive flux value based on the real-time parameter information;

[0110] In this embodiment, the method for obtaining real-time error information based on the digital twin model includes:

[0111] By simulating the digital twin model, the magnetic flux distribution information and speed change information are obtained. A magnetic flux distribution map is constructed based on the magnetic flux distribution information, and a speed change curve is constructed based on the speed change information. The magnetic flux distribution map and speed change curve are used as real-time error information.

[0112] Methods for calculating harmonic flux values ​​based on real-time parameter information include:

[0113]

[0114] Among them, Φ h is the harmonic flux value under the action of the hth harmonic, U h is the harmonic voltage amplitude generated by the hth harmonic, ω h is the angular frequency value generated by the hth harmonic, L is the coil inductance, and R is the coil resistance.

[0115] Methods for calculating comprehensive magnetic flux values ​​based on real-time parameter information include:

[0116] φ h (t)=∑ t φ h cos[ω h (t)-θ h -α h ];

[0117] Among them, Φ h (t) is the comprehensive magnetic flux value under the action of the hth harmonic, cos[·] is the cosine function, θ h is the initial phase angle under the action of the hth harmonic, αh is the phase lag angle under the action of the hth harmonic, ω h (t) is represented by the angular frequency value of the hth harmonic changing with time t.

[0118] Compensation module: used to input real-time measurement information, future error information and integrated flux value into the pre-built error compensation model to obtain target measurement information.

[0119] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0121] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0126] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An automatic error compensation method for an electric energy meter, characterized in that: include: Acquire real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information. The real-time parameter information refers to relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to the operation information of the internal components of the electric energy meter; Obtain real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate harmonic flux values ​​and comprehensive flux values ​​based on real-time parameter information; The real-time measurement information, future error information and integrated flux value are input into the pre-built error compensation model to obtain the target measurement information.

2. The automatic error compensation method for electric energy meter according to claim 1, characterized in that: The real-time operation information includes voltage coil temperature, current coil temperature, turntable speed value, turntable acceleration value and magnetic field strength value. The method of constructing a corresponding digital twin model according to the real-time operation information and preset structural parameter information includes: A basic structure sub-model is constructed according to the preset structural parameter information, a dynamic response sub-model is constructed according to the turntable speed value and turntable acceleration value in the real-time operation information, and a thermal-magnetic coupling sub-model is constructed according to the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information. The basic structure sub-model, dynamic response sub-model and thermal-magnetic coupling sub-model are integrated to obtain a digital twin model.

3. The automatic error compensation method for electric energy metering instrument according to claim 2, characterized in that: The method for constructing a dynamic response sub-model according to the turntable speed value and the turntable acceleration value in the real-time operation information includes: F net =m×a-k f ×ω; Among them, F net is the net driving force, m is the mass of the turntable, a is the acceleration value of the turntable, k f is the friction coefficient, and ω is the turntable speed.

4. The automatic error compensation method for electric energy meter according to claim 2, characterized in that: The method for constructing a thermal-magnetic coupling sub-model according to the voltage coil temperature, current coil temperature and magnetic field strength values ​​in the real-time operation information includes: δ(T V ,T C ,B)=δ0+q1(T V -T0)+q2(T C -T0)+q3(B-B0); Among them, δ(T V ,T C ,B) is characterized by the electric energy meter at T V 、T C , B, δ0 represents the error generated by the electric energy meter under the joint action of T0 and B0, T V is the voltage coil temperature, T C is the current coil temperature, B is the magnetic field strength value, T0 is the standard coil temperature, B0 is the standard magnetic field strength, q1, q2, q3 are all weight factors.

5. The automatic error compensation method for electric energy meter according to claim 1, characterized in that: The method for obtaining real-time error information based on the digital twin model includes: By simulating the digital twin model, the magnetic flux distribution information and speed change information are obtained. A magnetic flux distribution map is constructed based on the magnetic flux distribution information, and a speed change curve is constructed based on the speed change information. The magnetic flux distribution map and speed change curve are used as real-time error information.

6. The automatic error compensation method for electric energy metering instrument according to claim 5, characterized in that: The method for predicting future error information based on real-time error information includes: Input the real-time error information into the pre-built error prediction model to obtain future error information; The method for constructing the error prediction model includes: Preset the sliding step size and sliding window length; convert the historical error information into multiple training samples using the sliding window method, use the training samples as the input of the error prediction model, and use the historical error information after the predicted sliding step size as the output. The actual historical error information of each training sample is used as the prediction target, and the prediction accuracy is used as the training target to train the error prediction model; generate an error prediction model that predicts future error information based on real-time error information.

7. The automatic error compensation method for electric energy metering instrument according to claim 1, characterized in that: The real-time parameter information includes an angular frequency value, an initial phase angle, a phase lag angle, a coil inductance value, a harmonic voltage amplitude, and a coil resistance value. The method for calculating the harmonic magnetic flux value based on the real-time parameter information includes: Among them, Φ h is the harmonic flux value under the action of the hth harmonic, U h is the harmonic voltage amplitude generated by the hth harmonic, ω h is the angular frequency value generated by the hth harmonic, L is the coil inductance, and R is the coil resistance.

8. The automatic error compensation method for electric energy meter according to claim 7, characterized in that: The method for calculating the comprehensive magnetic flux value according to the real-time parameter information includes: F h (t)=∑ t F h cos[ω h (t)-θ h -a h ]; Among them, Φ h (t) is the comprehensive magnetic flux value under the action of the hth harmonic, cos[·] is the cosine function, θ h is the initial phase angle under the action of the hth harmonic, α h is the phase lag angle under the action of the hth harmonic, ω h (t) is represented by the angular frequency value of the hth harmonic changing with time t.

9. An automatic error compensation device for an electric energy meter, which is used to implement the automatic error compensation method for an electric energy meter according to any one of claims 1 to 8, characterized in that: include: The first processing module is used to obtain real-time measurement information, real-time parameter information, and real-time operation information of the electric energy meter during operation, and to construct a corresponding digital twin model based on the real-time operation information and preset structural parameter information. The real-time parameter information refers to the relevant parameter information collected during the influence of harmonics on the electric energy meter, and the real-time operation information refers to the operation information of the internal components of the electric energy meter; The second processing module is used to obtain real-time error information based on the digital twin model, predict future error information based on the real-time error information, and calculate the harmonic flux value and the comprehensive flux value based on the real-time parameter information; Compensation module: used to input real-time measurement information, future error information and integrated flux value into the pre-built error compensation model to obtain target measurement information.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the automatic error compensation method for the electric energy meter according to any one of claims 1 to 8 is implemented.

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