Large-current calibrating device and method for electric power meter

Abstract

The invention, which relates to the technical field of the electric power meter, discloses an electric power meter large-current calibrating device and method, the electric power meter large-current calibrating device comprises the following structures: a standard source arranged in a frame of a three-phase calibrating platform; the adjustable current source enables the output current to be stabilized at a set value through a negative feedback circuit; the synchronization unit is used for synchronous communication between the adjustable current source and the standard source; the error display adopts a liquid crystal display or a nixie tube to display the metering error and the input current of the intelligent electric meter; and verification is carried out based on the device. According to the method, a relation model among output current, saturation current and transformation ratio is constructed, the relation model adopts a variable exponential signal and an approximate amplitude change small current signal, whether the slope or the acceleration exceeds a certain threshold value is judged by solving a first derivative and a second derivative, the smart electric meter is judged to be unqualified, and otherwise, the smart electric meter is judged to be qualified; a large supersaturation current does not need to be actually applied, but a small current generated by self-oscillation is used, and safe power utilization is achieved.

Description

Technical Field

[0001] This invention relates to the field of power instrument technology, specifically to a high-current calibration device and method for power instruments. Background Technology

[0002] In economically developed regions of China, electricity load is high, especially during the summer. Most of the smart meters installed in factories are 100A three-phase smart meters with a maximum current of 100A. National standards allow for short-term overcurrent of 1.2 times the maximum current. However, these smart meters are ineffective for measuring peak summer loads and electricity consumption in economically developed regions, as the load can reach around 160A. Custom-made smart meters are needed to meet this requirement, specifically those capable of handling 160A overcurrent. During factory testing, the testing bench must withstand 160A of current. Currently, few manufacturers produce testing benches that meet this condition. Furthermore, the testing bench overheats significantly under such high current, posing a safety hazard and preventing prolonged operation, which would impact production efficiency and potentially burn out the bench. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a high-current calibration device and method for power meters, thereby solving the problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a high-current testing device for power meters, comprising the following structure:

[0006] The standard source is set within the frame of the three-phase calibration bench;

[0007] An adjustable current source, through a negative feedback circuit, stabilizes the output current at a set value;

[0008] Synchronization unit, used for synchronous communication between adjustable current source and standard source;

[0009] The error display uses an LCD or digital tube display to show the metering error and input current of the smart meter.

[0010] To further optimize this technical solution, the adjustable current source includes:

[0011] The input power module uses a DC regulated power supply.

[0012] The reference voltage adjustable module includes a DAC and a reference voltage source;

[0013] The current sampling and signal tuning module includes a high-precision sampling resistor and a differential amplifier;

[0014] The comparison amplification module uses a high-gain operational amplifier;

[0015] The power regulation transistor operates using a P-MOS transistor in the linear region;

[0016] The protection module includes an overcurrent detection circuit, an overtemperature sensor, and a Zener diode;

[0017] The load includes the sampling resistor and the current transformer resistor.

[0018] A method for verifying the high current of power instruments, based on the aforementioned high current verification device, includes the following specific steps:

[0019] S1. Construct a relationship model between saturation current, turns ratio K, and input current, calculate the output current, and then calculate the metering error;

[0020] S2. Determine the data object, obtain historical data from the multi-position calibration station, generate a QR code from the data, and bind the information.

[0021] S3. Construct the error model, saturation current model, and turns ratio K model at 160A, obtain the basic error under the maximum current and predict the metering error under the 160A current, and optimize the three models.

[0022] S4. Calculate the metering error of the smart meter at a high current of 160A based on the high current verification device.

[0023] S5. Within the range of maximum current and saturation current, based on the functional relationship curve between output current and turns ratio K and saturation current, calculate the first and second derivatives.

[0024] S6. By observing the changes in the slopes of the first and second derivatives, determine whether the smart meter is unqualified if the slope or acceleration exceeds a certain threshold; otherwise, it is qualified.

[0025] To further optimize this technical solution, the relationship model between the saturation current, the turns ratio K, and the input current in step S1 is as follows:

[0026]

[0027] in,

[0028] I out This is the output current of the current transformer;

[0029] I in This is the input current of the current transformer;

[0030] K is the turns ratio in the linear region during the test at the maximum current point;

[0031] I sat This is the saturation current, meaning that when the input current exceeds this value, the current transformer enters the saturation region.

[0032] n is a constant, ranging from 3 to 5, used to describe the degree of nonlinearity of the current transformer entering the saturation region.

[0033] To further optimize this technical solution, in step S2, the historical data of the multi-position calibration station includes data from 100,000 smart meters, including temperature rise, basic current, maximum current, as well as the metering error under the maximum current and the transformation ratio of the current transformer.

[0034] To further optimize this technical solution, in step S3, the basic error under the maximum current and the metering error under the predicted 160A current are compared, and the minimum value of the two is taken. The turns ratio K and constant n are then recalculated and adjusted until the predicted value of the metering error is basically equal to the metering error calculated in step S1.

[0035] To further optimize this technical solution, the construction of the error model, saturation current model, and turns ratio K model at 160A in step S3 includes the following process:

[0036] The dataset was established, nonlinear features were added, and error models, saturation current models, and turns ratio K models were constructed.

[0037] The model is evaluated and visualized.

[0038] To further optimize this technical solution, the establishment of the dataset includes:

[0039] Using data from 100,000 smart meters, including a fixed basic current of 10A and a maximum current of 100A, the metering error at 100A was calculated. Data from 20,000 smart meters were randomly selected as a sample, and the temperature rise, actual transformation ratio, and metering error were obtained under the condition of 160A current for 30 minutes.

[0040] The added nonlinear features include:

[0041] The error square term, the temperature rise square term, and the interaction term between error and temperature rise.

[0042] Further optimizations to this technical solution are as follows: the error model, saturation current model, and turns ratio K model at 160A are respectively shown below:

[0043] error 160A =β0+β1·I rated +β2·I max +β3·Error 100A +β4·T rise+β5·Overcurrent Ratio +β6·Saturation Ratio +β7·I applied +β8·t applied +β9·(error) 100A ) 2 +β 10 ·(T rise ) 2 +β 11 ·(error 100A ·T rise )

[0044] K 160A =γ0+γ1·I rated +γ2·I max +γ3·Error 100A +γ4·T rise +γ5·Overcurrent Ratio +γ6·Saturation Ratio +γ7·I applied +γ8·t applied +γ9·(error) 100A ) 2 +γ 10 ·(T rise ) 2 +γ 11 ·(error 100A ·T rise )

[0045] I sat =δ0+δ1·I rated +δ2·I max +δ3·Error 100A +δ4·T rise +δ5·Overcurrent Ratio +δ6·Saturation Ratio +δ7·I applied +δ8·t applied +δ9·(error) 100A ) 2 +δ 10 ·(T rise ) 2 +δ 11 ·(error 100A ·T rise )

[0046] in,

[0047] error 160A The error is at 160A;

[0048] I sat It is the saturation current;

[0049] K 160A The turns ratio K is at 160A;

[0050] I rated Rated current 10A, Imax Maximum current 100A, error 100A The error at 100A, T rise The values ​​represent temperature rise, overcurrent ratio (160A to 100A), saturation ratio (160A to saturation current), and I. applied To apply a current of 160A, t applied The power-on time is 30 minutes, and (error) 100A ) 2 For the squared error term, (T) rise ) 2 For the square term of temperature rise, (error) 100A ·T rise ) represents the interaction term between error and temperature rise;

[0051] β0-β 11 ,γ0-γ 11 ,δ0-δ 11 These are the coefficients of each feature, and the features are normalized.

[0052] Further optimization of this technical solution, including the evaluation and visualization of the model, includes:

[0053] The RMSE is used to evaluate the error model, and MAPE is used to evaluate the prediction effect of the turns ratio and saturation current. The comparison between the actual value and the predicted value is visualized, the relationship between the error prediction deviation and the degree of saturation is analyzed, and the coefficients of each feature of the error model are displayed to reflect the importance of each feature.

[0054] After running the program, the performance indicators and visualization results of the three prediction models are obtained, clearly showing the prediction effect of the models and the impact of various factors on the performance of the 160A meter.

[0055] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a high-current verification method for power meters as described in the first aspect of the present invention.

[0056] Compared with the prior art, the present invention provides a high-current calibration device and method for power meters, which has the following beneficial effects:

[0057] This high-current testing device and method for power meters constructs a relationship model between output current, saturation current, and turns ratio. The model uses a variable exponential signal to approximate a small current signal with varying amplitude. By solving the first and second derivatives, it determines whether the slope or acceleration exceeds a certain threshold. If so, the smart meter is deemed unqualified; otherwise, it is qualified. Instead of actually applying an oversaturated high current, it uses a small current generated by self-excited oscillation. By continuously changing the amplitude of the small current through modulation, it can ensure the safety of personnel and equipment, achieve safe electricity use, and significantly reduce energy loss. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a structural block diagram of a high-current power meter calibration device proposed in this invention, under a high-current smart meter calibration bench.

[0060] Figure 2 This is a schematic diagram of the adjustable current source in a high-current calibration device for power instruments.

[0061] Figure 3 The figure shows the model performance optimization test results of three models in the high current verification method for power meters proposed in this invention.

[0062] Figure 4 This is a bar graph showing the coefficients of three models in the high-current verification method for power meters proposed in this invention. Detailed Implementation

[0063] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0066] Example 1:

[0067] Reference Figure 1 This is the first embodiment of the present invention, which provides a high-current testing device for power meters, comprising the following structure:

[0068] The standard source is set within the frame of the high-current smart meter calibration bench;

[0069] An adjustable current source, through a negative feedback circuit, stabilizes the output current at a set value;

[0070] Synchronization unit, used for synchronous communication between adjustable current source and standard source;

[0071] The error display uses an LCD or digital tube display to show the metering error and input current of the smart meter.

[0072] Specifically, because the output current of the current transformer is small (in the mV range), and requires high accuracy and low ripple, the adjustable current source uses a negative feedback circuit composed of a linear regulating transistor and an operational amplifier. The current is stabilized by keeping the regulating transistor in the amplification region, thus forming an adjustable current source, including:

[0073] The input power module uses a DC regulated power supply.

[0074] The reference voltage adjustable module includes a DAC and a reference voltage source;

[0075] The current sampling and signal tuning module includes a high-precision sampling resistor and a differential amplifier;

[0076] The comparison amplification module uses a high-gain operational amplifier;

[0077] The power regulation transistor operates using a P-MOS transistor in the linear region;

[0078] The protection module includes an overcurrent detection circuit, an overtemperature sensor, and a Zener diode;

[0079] The load includes the sampling resistor and the current transformer resistor.

[0080] Example 2:

[0081] Reference Figure 2 This is the second embodiment of the present invention, which provides a method for verifying the high current of power meters. The method is based on the high current verification device described in Embodiment 1 and includes the following specific steps:

[0082] S1. Construct a relationship model between saturation current, turns ratio K, and input current, calculate the output current, and then calculate the measurement error.

[0083] The metering error of smart meters is usually related to the performance of the current transformer. The error typically increases during high-current or non-linear operating conditions. The current transformer's turns ratio K and saturation current I are key factors. sat It is a key factor affecting metering accuracy. When the current exceeds the linear region of the current transformer, the metering error will increase rapidly until the current transformer enters saturation.

[0084] Meanwhile, the saturation phenomenon of current transformers can cause the relationship between the output and input currents to become nonlinear, affecting measurement accuracy. To better understand and compensate for this phenomenon, a nonlinear relationship model is used to describe the saturation characteristics.

[0085] The relationship model between saturation current, turns ratio K, and input current is shown below:

[0086]

[0087] in,

[0088] I out This is the output current of the current transformer;

[0089] I in This is the input current of the current transformer;

[0090] K is the turns ratio in the linear region during the test at the maximum current point;

[0091] I sat This is the saturation current, meaning that when the input current exceeds this value, the current transformer enters the saturation region.

[0092] n is a constant, ranging from 3 to 5, used to describe the degree of nonlinearity of the current transformer entering the saturation region.

[0093] S2. Identify the data object, obtain historical data from multiple calibration stations, generate a QR code from the data, and bind the information.

[0094] The historical data of the multi-position calibration station includes data from 100,000 smart meters, including temperature rise, basic current, maximum current, as well as metering error under maximum current and current transformer ratio.

[0095] Artificial intelligence algorithms are used to learn from historical data and predict the turns ratio and saturation current of current transformers. The steps are as follows:

[0096] (1) Data collection and processing;

[0097] Historical measurement data from 300,000 smart meters were collected, including current data, metering error of 1.2 times the maximum current (the inflection point between the linear and saturation regions), temperature changes, turns ratio K, and saturation current.

[0098] (2) Feature selection and processing;

[0099] Selecting appropriate features, including the metering error, rated current, maximum current, temperature rise, turns ratio, and error of the smart meter, will help predict the turns ratio and saturation current.

[0100] (3) Model training;

[0101] The data is trained using a regression model (such as linear regression, support vector machine, decision tree, or neural network). The goal is to predict the saturation current and the transformer ratio K using input parameters (such as temperature, measurement error, transformer ratio, etc.).

[0102] (4) Model validation and prediction;

[0103] The accuracy of the trained model was verified using test data, and the trained model was then used to predict new smart meter data. The prediction results include the saturation current and transformation ratio K under different operating conditions.

[0104] (5) Optimize and improve prediction accuracy

[0105] To improve the prediction accuracy of the model, the following strategies can be adopted:

[0106] Error compensation: Combining the metering error of the smart meter with the performance changes of the current transformer, error compensation is performed to correct the prediction results.

[0107] Dynamic adjustment: The model's predicted output is dynamically adjusted based on the real-time operating status of the current transformer (such as temperature, operating current, etc.).

[0108] Multivariate regression: Incorporate more input features (such as temperature rise, measurement error, etc.) into the model and perform multivariate regression analysis to obtain more accurate prediction results.

[0109] By combining artificial intelligence algorithms with the operating characteristics of current transformers (such as transformation ratio, temperature rise, and saturation current) and the metering error of smart meters, it was found that the maximum current error, metering error, saturation current, and transformation ratio K are strongly correlated. Therefore, the above features can more accurately predict the saturation current and transformation ratio K of current transformers.

[0110] S3. Construct the error model, saturation current model, and turns ratio K model at 160A, obtain the basic error under the maximum current and predict the metering error under the 160A current, and optimize the three models.

[0111] Compare the basic error under the maximum current with the metering error under the predicted 160A current, take the minimum of the two, recalculate and adjust the turns ratio K and constant n until the predicted value of the metering error is basically equal to the metering error calculated in step S1.

[0112] The construction of the error model, saturation current model, and turns ratio K model at 160A includes the following steps:

[0113] The dataset was established, nonlinear features were added, and error models, saturation current models, and turns ratio K models were constructed.

[0114] The establishment of the dataset includes:

[0115] Using data from 100,000 smart meters, including a fixed basic current of 10A and a maximum current of 100A, the metering error at 100A was calculated. Considering the basic error, temperature effect, and individual differences, data from 20,000 smart meters were randomly selected as a sample. Under a current of 160A for 30 minutes, the temperature rise, actual transformation ratio, and metering error under this condition were obtained, taking into account overcurrent and saturation effects.

[0116] The added nonlinear features include:

[0117] The addition of error squared terms, temperature rise squared terms, and the interaction term between error and temperature rise enhances the model's expressive power.

[0118] The error model, saturation current model, and turns ratio K model at 160A are shown below:

[0119] error 160A =β0+β1·I rated +β2·I max +β3·Error 100A +β4·T rise +β5·Overcurrent Ratio +β6·Saturation Ratio +β7·I applied +β8·t applied +β9·(error) 100A ) 2 +β 10 ·(T rise ) 2 +β 11 ·(error 100A ·T rise )

[0120] K 160A =γ0+γ1·I rated +γ2·I max +γ3·Error 100A +γ4·T rise +γ5·Overcurrent Ratio +γ6·Saturation Ratio +γ7·I applied +γ8·t applied +γ9·(error) 100 A) 2 +γ 10 ·(T rise ) 2 +γ 11 ·(error 100A ·T rise )

[0121] I sat =δ0+δ1·I rated +δ2·I max+δ3·Error 100A +δ4·T rise +δ5·Overcurrent Ratio +δ6·Saturation Ratio +δ7·I applied +δ8·t applied +δ9·(error) 100A ) 2 +δ 10 ·(T rise ) 2 +δ 11 ·(error 100A ·T rise )

[0122] in,

[0123] error 160A The error is at 160A;

[0124] I sat It is the saturation current;

[0125] K 160A The turns ratio K is at 160A;

[0126] I rated Rated current 10A, I max Maximum current 100A, error 100A The error at 100A, T rise The values ​​represent temperature rise, overcurrent ratio (160A to 100A), saturation ratio (160A to saturation current), and I. applied To apply a current of 160A, t applied The power-on time is 30 minutes, and (error) 100A ) 2 For the squared error term, (T) rise ) 2 For the square term of temperature rise, (error) 100A ·T rise ) represents the interaction term between error and temperature rise;

[0127] β0-β 11 ,γ0-γ 11 ,δ0-δ 11 These are the coefficients of each feature, and the features are normalized.

[0128] The model is evaluated and visualized, including:

[0129] The RMSE is used to evaluate the error model, and MAPE is used to evaluate the prediction effect of the turns ratio and saturation current. The comparison between the actual value and the predicted value is visualized, the relationship between the error prediction deviation and the degree of saturation is analyzed, and the coefficients of each feature of the error model are displayed to reflect the importance of each feature.

[0130] like Figure 4 As shown, the influence of each feature on the prediction result is intuitively displayed (the larger the absolute value of the coefficient, the stronger the influence; positive and negative indicate the direction of influence).

[0131] like Figure 3 As shown, the evaluation results of the prediction model at a current of 160A are as follows:

[0132] Error model: Training set RMSE = 0.2994%, Test set RMSE = 0.2996%;

[0133] Variation K: MAPE = 5.48% for training set, MAPE = 5.46% for test set;

[0134] Saturation current I sat Training set MAPE = 5.65%, test set MAPE = 5.63%.

[0135] The specific conclusions are as follows:

[0136] The model exhibits excellent overall performance and high prediction accuracy;

[0137] Error model: The RMSE (root mean square error) of the training set and the test set are 0.2994% and 0.2996%, respectively, both below 0.3%. This indicates that the model has extremely high accuracy in predicting measurement errors under 160A overcurrent conditions and can accurately capture the influence of factors such as overcurrent, temperature rise, and saturation on measurement errors.

[0138] Transformer ratio K prediction: The MAPE (mean absolute percentage error) for the training set and the test set were 5.48% and 5.46%, respectively, with the error remaining stable within 5.5%. This indicates that the model can effectively reflect the nonlinear changes in the transformer ratio under overcurrent conditions (such as ratio shift caused by saturation).

[0139] Saturation current prediction: The MAPE values ​​for the training and test sets were 5.65% and 5.63%, respectively, with the error controlled within 5.7%. Compared to before optimization (approximately 13%), the accuracy improvement is significant, indicating that the newly added features such as overcurrent ratio and saturation ratio, as well as the nonlinear term, effectively enhance the model's ability to predict saturation current.

[0140] The model has strong generalization ability and no risk of overfitting;

[0141] The error metrics of the training set and the test set are almost identical (the difference is within 0.02%), indicating that the model did not overfit during training and can stably adapt to unknown data.

[0142] From the perspective of actual electricity metering needs, the metering error prediction accuracy (<0.3%) is far lower than the conventional error allowable range of smart meters (usually ±1% to ±2%), which can meet the needs of high-precision metering scenarios; the prediction error of transformation ratio K and saturation current Isat (<6%) can provide a reliable basis for metering deviation correction under overcurrent conditions and transformer performance evaluation, which helps to improve the metering accuracy and fault diagnosis capability of smart grids.

[0143] After running the program, the performance indicators and visualization results of the three prediction models are obtained, clearly showing the prediction effect of the models and the impact of various factors on the performance of the 160A meter.

[0144] The three models, through a combination of linear and nonlinear terms, accurately capture the key characteristics of smart meters under 160A overcurrent conditions. The error model focuses on reflecting the nonlinear effects of saturation and temperature rise; the transformer ratio K model reflects the proportional shift of the transformer under overcurrent conditions; and the saturation current model is constructed based on the maximum current and the degree of overcurrent, with clear physical meaning. The visualization results of the model coefficients can be directly used for engineering analysis. For example, the error model can be used to identify the main error sources under overcurrent conditions, providing a basis for meter design optimization.

[0145] S4. Calculate the metering error of the smart meter at a high current of 160A based on the high current calibration device.

[0146] S5. Within the range of maximum current and saturation current, based on the functional relationship curve between output current and turns ratio K and saturation current, calculate the first and second derivatives.

[0147] S6. By observing the changes in the slopes of the first and second derivatives, determine whether the smart meter is unqualified if the slope or acceleration exceeds a certain threshold; otherwise, it is qualified.

[0148] This invention generates a weak AC current signal on the secondary side of the current transformer without applying a 160A overcurrent. This small signal is essentially the same as the secondary-side signal generated by the current transformer when a 160A overcurrent is applied. Therefore, during factory testing, it avoids the problem of not being able to find a suitable testing platform for 160A overcurrent testing, and also avoids the unpredictable and potentially fatal dangers caused by temperature rise during testing even if a suitable platform is found.

[0149] Example 3:

[0150] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a high-current verification method for power meters as proposed in the above embodiments.

[0151] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0152] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0153] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A high-current calibration device for power meters, characterized in that, Includes the following structure: The standard source is set within the frame of the three-phase calibration bench; An adjustable current source, through a negative feedback circuit, stabilizes the output current at a set value; Synchronization unit, used for synchronous communication between adjustable current source and standard source; The error display uses an LCD or digital tube display to show the metering error and input current of the smart meter.

2. The high-current calibration device for power meters according to claim 1, characterized in that, The adjustable current source includes: The input power module uses a DC regulated power supply. The reference voltage adjustable module includes a DAC and a reference voltage source; The current sampling and signal tuning module includes a high-precision sampling resistor and a differential amplifier; The comparison amplification module uses a high-gain operational amplifier; The power regulation transistor operates using a P-MOS transistor in the linear region; The protection module includes an overcurrent detection circuit, an overtemperature sensor, and a Zener diode; The load includes the sampling resistor and the current transformer resistor.

3. A method for verifying the high current of an electrical instrument, wherein the high current is verified based on the high current verification device described in any one of claims 1-2, characterized in that, The specific steps include the following: S1. Construct a relationship model between saturation current, turns ratio K, and input current, calculate the output current, and then calculate the metering error; S2. Determine the data object, obtain historical data from the multi-position calibration station, generate a QR code from the data, and bind the information. S3. Construct the error model, saturation current model, and turns ratio K model at 160A, obtain the basic error under the maximum current and predict the metering error under the 160A current, and optimize the three models. S4. Calculate the metering error of the smart meter at a high current of 160A based on the high current verification device. S5. Within the range of maximum current and saturation current, based on the functional relationship curve between output current and turns ratio K and saturation current, calculate the first and second derivatives. S6. By observing the changes in the slopes of the first and second derivatives, determine whether the smart meter is unqualified if the slope or acceleration exceeds a certain threshold; otherwise, it is qualified.

4. The method for verifying the high current of power instruments according to claim 3, characterized in that, In step S1, the relationship between saturation current, turns ratio K, and input current is shown in the following model: in, I out This is the output current of the current transformer; I in This is the input current of the current transformer; K is the turns ratio in the linear region during the test at the maximum current point; I sat This is the saturation current, meaning that when the input current exceeds this value, the current transformer enters the saturation region. n is a constant, ranging from 3 to 5, used to describe the degree of nonlinearity of the current transformer entering the saturation region.

5. The method for verifying the high current of an electric instrument according to claim 3, characterized in that, In step S2, the historical data of the multi-meter calibration station includes data from 100,000 smart meters, including temperature rise, basic current, maximum current, as well as the metering error under the maximum current and the transformation ratio of the current transformer.

6. The method for verifying the high current of power meters according to claim 3, characterized in that, In step S3, the basic error under the maximum current and the metering error under the predicted 160A current are compared, and the minimum value of the two is taken. The turns ratio K and constant n are then recalculated and adjusted until the predicted value of the metering error is basically equal to the metering error calculated in step S1.

7. The method for verifying the high current of power instruments according to claim 3, characterized in that, In step S3, the construction of the error model, saturation current model, and turns ratio K model at 160A includes the following process: The dataset was established, nonlinear features were added, and error models, saturation current models, and turns ratio K models were constructed. The model is evaluated and visualized.

8. The method for verifying the high current of an electric instrument according to claim 7, characterized in that, The dataset creation includes: Using data from 100,000 smart meters, including a fixed basic current of 10A and a maximum current of 100A, the metering error at 100A was calculated. Data from 20,000 smart meters were randomly selected as a sample, and the temperature rise, actual transformation ratio, and metering error were obtained under the condition of 160A current for 30 minutes. The added nonlinear features include: The error square term, the temperature rise square term, and the interaction term between error and temperature rise.

9. The method for verifying the high current of an electric instrument according to claim 7, characterized in that, The error model, saturation current model, and turns ratio K model at 160A are shown below: error 160A =β0+β1·I rated +β2·I max +β3·Error 100A +β4·T rise +β5·Overcurrent Ratio +β6·Saturation Ratio +β7·I applied +β8·t applied +β9·(error) 100A ) 2 +β 10 ·(T rise ) 2 +β 11 ·(error 100A ·T rise ) K 160A =γ0+γ1·I rated +γ2·I max +γ3·Error 100A +γ4·T rise +γ5·Overcurrent Ratio +γ6·Saturation Ratio +γ7·I applied +γ8·t applied +γ9·(error) 100 A) 2 +γ 10 ·(T rise ) 2 +γ 11 ·(error 100A ·T rise ) I sat =δ0+δ1·I rated +δ2·I max +δ3·Error 100A +δ4·T rise +δ5·Overcurrent Ratio +δ6·Saturation Ratio +δ7·I applied +δ8·t applied +δ9·(error) 100 A) 2 +δ 10 ·(T rise ) 2 +δ 11 ·(error 100A ·T rise ) in, error 160A The error is at 160A; I sat It is the saturation current; K 160A The turns ratio K is at 160A; I rated Rated current 10A, I max Maximum current 100A, error 100A The error at 100A, T rise The values ​​represent temperature rise, overcurrent ratio (160A to 100A), saturation ratio (160A to saturation current), and I. applied To apply a current of 160A, t applied The power-on time is 30 minutes, and (error) 100A ) 2 For the squared error term, (T) rise ) 2 For the square term of temperature rise, (error) 100A ·T rise ) represents the interaction term between error and temperature rise; β0-β 11 ,γ0-γ 11 ,δ0-δ 11 These are the coefficients of each feature, and the features are normalized.

10. A method for verifying the high current of an electric instrument according to claim 7, characterized in that, The evaluation and visualization of the model include: The RMSE is used to evaluate the error model, and MAPE is used to evaluate the prediction effect of the turns ratio and saturation current. The comparison between the actual value and the predicted value is visualized, the relationship between the error prediction deviation and the degree of saturation is analyzed, and the coefficients of each feature of the error model are displayed to reflect the importance of each feature. After running the program, the performance indicators and visualization results of the three prediction models are obtained, clearly showing the prediction effect of the models and the impact of various factors on the performance of the 160A meter.

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