Calibration method, device and equipment for digital electric energy meter with self-calibration function

Abstract

The invention provides a calibration method, device and equipment for a digital electric energy meter with a self-calibration function, and the method comprises the steps: obtaining a to-be-detected pulse electric energy signal and a standard electric energy pulse signal which are inputted to the digital electric energy meter in real time, and carrying out the preprocessing of the signals, and generating an error sequence; performing multi-scale coarse graining processing on the error sequence to generate a coarse graining sequence based on different scale factors; calculating a sample entropy value of each coarse graining sequence, generating entropy spectrum characteristics, and identifying frequency components based on an error source in the entropy spectrum characteristics; and realizing calibration of the digital electric energy meter based on the frequency components and the corresponding calibration weights. Through a multi-scale entropy analysis technology, multi-scale quantization of error signal complexity is realized, and the limitation that a traditional single time window comparison method cannot distinguish frequency characteristics is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy meter, and in particular to a calibration method, device and equipment of a digital electric energy meter with self-calibration function. BACKGROUND

[0002] In the power system, the gateway electric energy meter is the core metering equipment for the operation benefit accounting of the power grid, and its metering accuracy and operation reliability are directly related to the fairness of power transaction, the new energy consumption efficiency and the economic efficiency of power grid operation. With the promotion of the construction of new-type power system, the proportion of direct current transmission scene is significantly increased, and higher requirements are put forward for the metering performance of digital direct current electric energy meter.

[0003] According to the provisions of the DL / T 448-2016 Electric Energy Metering Device Technical Management Regulations, electric energy metering devices need to perform differentiated on-site inspection cycles according to categories: type I electric energy metering devices need to be inspected on-site once every 6 months, type II devices are inspected once every 12 months, and type III devices are inspected once every 24 months. However, this periodic inspection mode has the following problems:

[0004] (1) The traditional digital direct current electric energy meter and the on-site calibration equipment are independently separated structures, and when the scale of the power grid is expanded to cause the decentralization of equipment and the remoteness of the meter hanging position, a large amount of manpower and material resources need to be invested to carry out on-site calibration operation, and the operation and maintenance cost is high;

[0005] (2) During the interval between two on-site inspections, the metering deviation caused by environmental factors (temperature / electromagnetic interference), device aging, etc. cannot be monitored in real time, and there is a risk of a vacuum period of "inspection qualified-use period-next inspection", which causes the loss of continuity of metering data;

[0006] (3) The existing technical solutions lack self-calibration function design, and when abnormal metering data occurs, deviation tracing and parameter correction cannot be realized through self-diagnosis of the equipment, and manual intervention is needed to complete the long process of calibration-adjustment-reinspection. SUMMARY

[0007] In order to solve the above technical problems, the present application provides a calibration method, device and equipment of a digital electric energy meter with self-calibration function, which has an online calibration function and can realize the functions of synchronous electric quantity collection of transmission lines, online calibration of electric energy meters and online monitoring of electric energy quality.

[0008] In order to achieve the above purpose, the present application provides the following solutions:

[0009] In the first aspect, the present application provides a calibration method of a digital electric energy meter with self-calibration function, which comprises the following steps:

[0010] Real-time acquisition of the error sequence based on the pulse electric energy signals to be detected and the standard electric energy pulse signals input to the digital electric energy meter, and generation of the error sequence after pre-processing;

[0011] Multi-scale coarse-grained processing of the error sequence to generate coarse-grained sequences based on different scale factors;

[0012] Calculation of the sample entropy value of each coarse-grained sequence to generate an entropy spectrum feature, and identification of the frequency component based on the error source in the entropy spectrum feature;

[0013] Calibration of the digital electric energy meter based on the frequency component and the corresponding calibration weight.

[0014] Further, the pre-processing includes one or both of detrending and normalization.

[0015] Further, the step of multi-scale coarse-grained processing of the error sequence to generate coarse-grained sequences based on different scale factors includes:

[0016] A set of scale factors is established in advance, and a maximum scale factor is set. For any scale factor τ in the set, a corresponding coarse-grained sequence y τ (j) is constructed, which is expressed as:

[0017]

[0018] where τ represents the scale factor, represents the maximum integer not exceeding N / τ, and x(i) represents the error sequence based on the pulse electric energy signals to be detected and the standard electric energy pulse signals.

[0019] Further, the step of calculating the sample entropy value of each coarse-grained sequence to generate an entropy spectrum feature, and identifying the frequency component based on the error source in the entropy spectrum feature includes:

[0020] For the coarse-grained sequence corresponding to the scale factor, a multi-dimensional vector sequence is constructed, the maximum absolute error between any two multi-dimensional vector sequences is calculated, the number of similar vectors is calculated based on the maximum absolute error, and thus the sample entropy value is calculated;

[0021] An entropy spectrum feature curve is constructed based on the scale factor as the horizontal coordinate and the sample entropy value as the vertical coordinate;

[0022] Based on the different frequency components corresponding to the scale factors of different frequency interval types in the entropy spectrum feature curve, the entropy spectrum feature index of different frequency intervals is calculated, and the frequency component based on the error source in the entropy spectrum feature is identified based on the entropy spectrum feature index;

[0023] The scale factor of different frequency interval types corresponds to different frequency components, and specifically, when the scale factor is [1, 5], the interval is a low-scale interval, and the frequency component is a high-frequency component; when the scale factor is [6, 12], the interval is a medium-scale interval, and the frequency component is a medium-frequency component; and when the scale factor is [13, 20], the interval is a high-scale interval, and the frequency component is a low-frequency component.

[0024] Further, the step of calibrating the digital electric energy meter based on the frequency component and the corresponding calibration weight comprises: establishing a mapping relationship between different frequency intervals and the calibration parameters of the digital electric energy meter, calculating the calibration weight and the calibration coefficient of different frequency intervals according to the identified frequency component based on the error source, generating the calibration parameters of the digital electric energy meter to be updated, and updating the calibration parameters in the digital electric energy meter to realize calibration.

[0025] The calibration parameters include high-frequency response parameters, medium-frequency response parameters, low-frequency response parameters, and reference parameters.

[0026] Further, the step of calculating the calibration weight of different frequency intervals according to the identified frequency component based on the error source comprises: calculating the high-frequency interval calibration weight of the high-frequency component, the medium-frequency interval calibration weight of the medium-frequency component, and the low-frequency interval calibration weight of the low-frequency component, respectively; wherein the high-frequency interval calibration weight is represented by the ratio of the low-scale interval entropy value variance to the sum of all interval entropy value variances; the medium-frequency interval calibration weight is represented by the ratio of the medium-scale interval entropy value variance to the sum of all interval entropy value variances; and the low-frequency interval calibration weight is represented by the ratio of the high-scale interval entropy value variance to the sum of all interval entropy value variances.

[0027] Further, the step of calculating the calibration coefficient of different frequency intervals according to the identified frequency component based on the error source comprises: calculating the calibration coefficient of the high-frequency response parameters, the medium-frequency response parameters, the low-frequency response parameters, and the reference parameters, respectively, wherein:

[0028] The calibration coefficient of the high-frequency response parameters is represented by the product of the high-frequency interval calibration weight, the low-scale interval average entropy value, the low-scale interval entropy value slope sign, and a preset first gain factor;

[0029] The calibration coefficient of the medium-frequency response parameters is represented by the product of the medium-frequency interval calibration weight, the medium-scale interval average entropy value, the medium-scale interval entropy value slope sign, and a preset second gain factor;

[0030] The calibration coefficient of the low-frequency response parameters is represented by the product of the low-frequency interval calibration weight, the high-scale interval average entropy value, the high-scale interval entropy value slope sign, and a preset third gain factor;

[0031] The calibration coefficient of the reference parameter is expressed as a product of a sum of interval calibration weights and corresponding interval average entropy values weighted by a preset fourth gain factor.

[0032] In a second aspect, the present application further provides a calibration device of a digital electric energy meter with a self-calibration function, which is used to execute the calibration method of the first aspect, and comprises:

[0033] A digital quantity sampling module is configured to receive voltage and current sampling values in an FT3 format from a network device, and send a digital signal converted from the FT3 signal to an electric energy metering module.

[0034] A digital standard measurement module is configured to output a standard electric energy pulse signal according to a signal input to the digital electric energy meter.

[0035] An error calculation module is configured to generate an error sequence after preprocessing the to-be-detected pulse electric energy signal and the standard electric energy pulse signal, perform multi-scale coarse-grained processing on the error sequence to generate coarse-grained sequences based on different scale factors, calculate sample entropy values of each coarse-grained sequence to generate an entropy spectrum feature, identify a frequency component based on an error source in the entropy spectrum feature, and realize calibration of the digital electric energy meter based on the frequency component and a corresponding calibration weight.

[0036] A display module is configured to display a calibration result.

[0037] Further, the calibration device further comprises an electric energy quality monitoring module configured to perform electric energy quality monitoring on the digital signal sent by the digital quantity sampling module, and the electric energy quality monitoring module supports 100-2000 harmonic analysis to accurately identify harmonic components generated by new energy grid connection.

[0038] In a third aspect, the present application further provides an electronic device comprising a memory and a processor, wherein the memory is coupled to the processor, the memory is configured to store program data, and the processor is configured to execute the program data to realize the calibration method of the digital electric energy meter with a self-calibration function according to the first aspect.

[0039] The application provides a calibration method, device and equipment for a digital electric energy meter with a self-calibration function, comprising the following steps: acquiring in real time a to-be-detected pulse electric energy signal input into the digital electric energy meter and a standard electric energy pulse signal, and generating an error sequence after preprocessing; performing multi-scale coarse-grained processing on the error sequence to generate coarse-grained sequences based on different scale factors; calculating sample entropy values of each coarse-grained sequence to generate an entropy spectrum feature, and identifying a frequency component based on an error source in the entropy spectrum feature; and performing calibration of the digital electric energy meter based on the frequency component and a corresponding calibration weight. The application realizes multi-scale quantification of the complexity of an error signal through multi-scale entropy analysis technology, and overcomes the limitation that a traditional single time window comparison method cannot distinguish frequency characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A calibration method flowchart for a digital electric energy meter with a self-calibration function.

[0041] Figure 2 A calibration device structure diagram for a digital electric energy meter with a self-calibration function.

[0042] Figure 3 An electronic device diagram. DETAILED DESCRIPTION

[0043] It is necessary to point out here that the following detailed description is only used to further illustrate the application, and cannot be understood as limiting the protection scope of the application. Those skilled in the art can make some non-essential improvements and adjustments to the application according to the above application content.

[0044] Before the application is described in detail, in order to help understand the technical scheme of the application, the following first explains the terms to be used in the application:

[0045] 1. Multi-scale entropy analysis: a signal processing technology that calculates the sample entropy of a signal at different time scales to quantify the complexity characteristics of a time series, and is particularly suitable for analyzing nonlinear and non-stationary signals.

[0046] 2. Sample entropy: a statistical index used to measure the complexity and irregularity of a time series. The larger the value, the more complex or random the sequence, and the smaller the value, the more regular or predictable the sequence.

[0047] 3. Coarse-grained processing: a process of segmenting and averaging the original time series according to a specific scale factor to generate a new time series, used in multi-scale analysis to reduce the sampling rate of the signal.

[0048] 4. Entropy spectrum feature: a set of sample entropy values of the sample at different time scales, forming a curve describing the complexity change of the signal at multiple time scales.

[0049] 5. Electric energy pulse signal: a pulse sequence representing electric energy in the electric energy meter, the pulse frequency being proportional to the electric energy consumption.

[0050] As shown in the accompanying drawings, Figure 1 the present application proposes a calibration method for a digital electric energy meter with a self-calibration function, the calibration method comprising:

[0051] S101. Real-time acquisition of a pulse electric energy signal to be detected input into the digital electric energy meter and a standard electric energy pulse signal, and generation of an error sequence after pre-processing;

[0052] S102. Multi-scale coarse-grained processing of the error sequence to generate a coarse-grained sequence based on different scale factors;

[0053] S103. Calculation of sample entropy values of each coarse-grained sequence to generate an entropy spectrum feature, and identification of a frequency component based on an error source in the entropy spectrum feature;

[0054] S104. Calibration of the digital electric energy meter based on the frequency component and a corresponding calibration weight.

[0055] In the present embodiment, the pulse electric energy signal to be detected input into the digital electric energy meter and the standard electric energy pulse signal acquired in real time are represented as P test (t) and P std (t), respectively, in the process, the sampling of the signal can set the sampling frequency to f s , the sampling duration to T, and the error sequence to x(i), then:

[0056] x(i) = P test (i) - P std (i)

[0057] wherein i = 1, 2,..., N, N representing the sequence length of the error sequence, and in order to eliminate the influence of baseline drift and amplitude difference, the error sequence described above also needs to be pre-processed, the pre-processing process including detrending, normalization and the like.

[0058] In the present embodiment, after pre-processing of the error sequence, the error sequence is still represented as x(i) at this time, which can be considered as the error sequence after pre-processing. In step S102, multi-scale coarse-grained technology is introduced to process the error sequence x(i) to generate a coarse-grained sequence based on different scale factors, specifically including the following contents:

[0059] Step S201: Define a set of scale factors τ = {1, 2,..., τ max}, where τ max is the maximum scale factor, and τ max = 20 in this embodiment.

[0060] Step S202: For each scale factor τ, construct a corresponding coarse-grained sequence y τ (j) as follows:

[0061]

[0062] where is the largest integer not exceeding N / τ.

[0063] Step S203: For the case of scale factor τ = 1, the coarse-grained sequence y 1 (j) is the original preprocessed sequence x(i).

[0064] Step S204: Store all coarse-grained sequences corresponding to different scale factors for subsequent sample entropy calculation.

[0065] In this embodiment, the specific process of step S103 is as follows:

[0066] Step S301: Set the sample entropy calculation parameters, including the pattern length m and the similarity threshold r. In this embodiment, the pattern length m = 2, and the similarity threshold r = 0.15 × σ, where σ is the standard deviation of the original error sequence x(i).

[0067] Step S302: For each coarse-grained sequence y τ corresponding to a scale factor τ, calculate its sample entropy value SampEn(y τ , m, r).

[0068] The calculation process of sample entropy is as follows:

[0069] (1) Construct an m-dimensional vector sequence:

[0070]

[0071] where is the length of the coarse-grained sequence y τ .

[0072] (2) Calculate the maximum absolute difference between any two m-dimensional vectors:

[0073]

[0074] (3) For each i, calculate the number of vectors similar to it: ​

[0075]

[0076] Here, Θ(x) is the Heaviside function, which takes the value 1 when x≥0, and 0 otherwise.

[0077] (4) Calculate B m (r) represents all Average value:

[0078]

[0079] Similarly, calculate the similarity statistic A for (m+1) dimensional vectors. m (r), thus obtaining the sample entropy, expressed as:

[0080]

[0081] Step S303: For all scaling factors τ = 1, 2, ..., τ max Repeat step S302 to obtain the sample entropy value.

[0082] The sample entropy value is obtained through the above process. Entropy spectrum features are then generated according to the following process, which includes the following:

[0083] Step S401: Construct the entropy spectrum feature curve, with the horizontal axis representing the scaling factor τ and the vertical axis representing the corresponding sample entropy value SampEn(y). τ ,m,r).

[0084] Step S402: Perform segmented analysis on the entropy spectrum characteristic curve. When the scale factor is [1,5], the interval is a low-scale interval and the frequency component is a high-frequency component; when the scale factor is [6,12], the interval is a medium-scale interval and the frequency component is a medium-frequency component; when the scale factor is [13,20], the interval is a high-scale interval and the frequency component is a low-frequency component.

[0085] Step S403: Calculate the entropy spectrum characteristic index for each interval:

[0086] The average entropy values ​​for the low-scale, medium-scale, and high-scale intervals are expressed as follows:

[0087]

[0088] The slopes of the interval entropy values ​​for the low-scale, medium-scale, and high-scale intervals represent:

[0089]

[0090] The variances of the interval entropy values ​​for the low-scale, medium-scale, and high-scale intervals are expressed as follows:

[0091]

[0092] Step S404: identifying the main frequency component of the error based on the entropy spectrum feature index. The specific judgment rule is as follows:

[0093] If and and significantly greater than the variance of other intervals, it is judged that the high-frequency component is the main source of error;

[0094] If and and significantly greater than the variance of other intervals, it is judged that the medium-frequency component is the main source of error;

[0095] If and and significantly greater than the variance of other intervals, it is judged that the low-frequency component is the main source of error;

[0096] If the entropy value distribution of each interval is relatively uniform, it is judged that the error is caused by the combined effect of multiple frequency components.

[0097] In the embodiment, the step of calibrating the digital electric energy meter based on the frequency component and the corresponding calibration weight comprises: establishing a mapping relationship between different frequency intervals and the calibration parameters of the digital electric energy meter, calculating the calibration weight and the calibration coefficient of different frequency intervals according to the identified frequency component based on the error source, generating the calibration parameters of the digital electric energy meter to be updated, and updating the calibration parameters in the digital electric energy meter to realize calibration.

[0098] The calibration parameters include high-frequency response parameters K high , medium-frequency response parameters K mid , low-frequency response parameters K low , and reference parameters K base .

[0099] According to the foregoing, the calibration weight of each frequency interval can be obtained, which is represented as:

[0100]

[0101] The calibration coefficient of each frequency interval is represented as:

[0102]

[0103] Wherein, α higj is a preset first gain factor of the calibration coefficient, α mid is a preset second gain factor of the calibration coefficient, and αlow The preset calibration coefficients, third gain factor, α base The fourth gain factor is the preset calibration coefficient. sign() is the sign function, which returns 1 when the parameter is positive, -1 when it is negative, and 0 when it is zero.

[0104] After completing the above process, update the electricity meter calibration parameters:

[0105]

[0106] in, These are the calibration parameters currently used in electricity meters. These are the updated calibration parameters.

[0107] like Figure 2 As shown in this embodiment, the calibration of the electricity meter is completed by updating the calibration parameters, realizing the online verification function, which can realize the functions of synchronous power acquisition of transmission lines, online verification of electricity meters, and online monitoring of power quality.

[0108] This embodiment also provides a calibration device 200 for a digital energy meter with self-calibration function, characterized in that the calibration device is used to perform the above-described calibration method, and the calibration device includes:

[0109] The digital sampling module 201 is used to receive voltage and current sampling values ​​in FT3 format from the network device, convert the FT3 signal into a digital signal and send it to the power metering module 202. The power metering module 202 outputs the pulse power signal to be detected according to the received digital signal.

[0110] Digital standard measurement module 203 is used to output a standard energy pulse signal based on the signal input to the digital energy meter;

[0111] The error calculation module 204 is used to generate an error sequence by preprocessing the pulse energy signal to be detected and the standard pulse energy signal; to perform multi-scale coarse-graining processing on the error sequence to generate coarse-grained sequences based on different scale factors; to calculate the sample entropy value of each coarse-grained sequence to generate an entropy spectrum feature, and to identify the frequency components based on the error source in the entropy spectrum feature; and to realize the calibration of the digital energy meter based on the frequency components and the corresponding calibration weights.

[0112] Display module 205 is used to display calibration results.

[0113] It should be noted that the digital electric energy meter in the embodiment needs to realize online data receiving, analyzing and processing process through a network device. In practice, the network device of the embodiment can be a switch. The FT3 format signal is obtained through the switch and sent to the digital quantity sampling module 201 and the digital standard measurement module 203 respectively to realize the error calculation process.

[0114] In the embodiment, the digital quantity sampling module 201 adopts a chip with a model of HFBR-2412TZ. The chip is used to collect FT3 optical digital signals and output corresponding digital signals according to the FT3 optical digital signals. The conversion process of FT3 signal to digital signal includes:

[0115] (1) Photoelectric conversion and signal shaping

[0116] Optical signal receiving: the module built-in photodiode converts the FT3 optical pulse into a current signal.

[0117] Current-to-voltage conversion: the current is converted into a voltage signal through a transimpedance amplifier (TIA), and is amplified and filtered to eliminate noise interference.

[0118] Level matching: adjust the signal amplitude to the working range (such as 0-5V) of the subsequent decoding circuit.

[0119] (2) Decoding

[0120] The self-synchronization characteristics of Manchester coding are used to extract the clock signal from the data stream. In each bit period, the level jump position (rising edge or falling edge) provides the clock reference.

[0121] Detect the jump direction in each bit period to extract data: falling edge (high bit→low bit)→output “1”; rising edge (low bit→high bit)→output “0”. Combine the start symbol (0x0564) to locate the frame header to ensure data alignment.

[0122] (3) Data verification and processing: calculate the CRC value of the received user data and compare it with the check sequence of the frame tail. According to the message type field, distinguish the standard frame, extended frame or mutual inductor protocol frame, and extract the payload (such as current / voltage sampling value, state flag, etc.).

[0123] (4) Sampling quantization and encoding

[0124] Sampling: follow the Nyquist sampling theorem, sampling rate ≥ 2 times the highest frequency of the signal (such as 4kHz sampling rate corresponding to 2kHz signal bandwidth).

[0125] Synchronous sampling technology is adopted to ensure the time sequence consistency of multi-channel data.

[0126] Quantization: Map continuous analog values to discrete digital values. Quantization error is reduced by increasing ADC resolution (e.g. 24 bits) or oversampling techniques (e.g. averaging after 64x oversampling).

[0127] Encoding: Convert quantized values to binary codes, supporting formats such as two's complement, offset binary, etc. Add metadata such as channel identification, timestamps, etc. to form complete digital frames.

[0128] In this embodiment, the electric energy metering module 202 uses a high-performance symmetric dual-core processor from ADI, with a maximum MAC operating speed of 2GHz, which can fully meet the various calculation and verification needs of digital DC electric energy meters. After receiving the data frame, the electric energy metering module 202 will first perform CRC verification on the received data, and after confirming that all the received data is correct, it will extract the voltage and current sample values in the data and cache them in the corresponding cache space. At the same time, the system automatically records a set of cache. When the system correctly collects a complete waveform, it will calculate the effective value and power, and accurately time the current effective value, calculate the corresponding data sampling time, and accumulate the electric energy. A complete set of sample values will be matched according to the current sampling rate of the transformer to ensure the accuracy of each effective value calculation, especially in cases where the voltage and current fluctuate and the frame dispersion is not good. This method can ensure more accurate metering.

[0129] In this embodiment, the calibration device can include an electric energy quality monitoring module for monitoring the electric energy quality of the digital signal sent by the digital sampling module. The electric energy quality monitoring module supports 100-2000 harmonic analysis to accurately identify the harmonic components generated by the new energy grid connection.

[0130] The functions of the electric energy quality monitoring module are as follows:

[0131]

[0132] The present embodiment also provides an electronic device, which can include a processor 301, a communication interface 302, a memory 303, and a communication bus 304. The processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. The processor 301 can invoke the logical instructions in the memory 303 to execute the calibration method of the digital electric energy meter with self-calibration function as described above.

[0133] In addition, the logic instructions in the memory 303 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0134] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood broadly, for example, can be fixed connection, or detachable connection, or integral; can be mechanical connection, or electrical connection; can be direct connection, or indirect connection through an intermediate medium; can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0135] In the present application, unless otherwise explicitly specified and limited, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature "above", "over" and "on" the second feature can be directly above or obliquely above the first feature, or only indicate that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "under" and "under" the second feature can be directly below or obliquely below the first feature, or only indicate that the horizontal height of the first feature is less than that of the second feature.

[0136] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application.

Claims

1. A calibration method for a digital energy meter with self-calibration function, characterized in that, The calibration method includes the following steps: The system acquires the pulse energy signal to be detected and the standard energy pulse signal input to the digital energy meter in real time, and generates an error sequence after preprocessing. The error sequence is subjected to multi-scale coarse-graining processing to generate coarse-grained sequences based on different scale factors; Calculate the sample entropy value for each coarse-grained sequence, generate entropy spectrum features, and identify the frequency components based on error sources in the entropy spectrum features; The calibration of the digital energy meter is achieved based on the frequency components and the corresponding calibration weights.

2. The calibration method for a digital energy meter with self-calibration function according to claim 1, characterized in that, The preprocessing includes one or both of detrending and normalization.

3. The calibration method for a digital energy meter with self-calibration function according to claim 1, characterized in that, The steps of performing multi-scale coarse-graining processing on the error sequence to generate coarse-grained sequences based on different scale factors include: A set of scaling factors is pre-established, and a maximum scaling factor is set. For any scaling factor τ in the set, a corresponding coarse-grained sequence y is constructed. τ (j), represented as: Where τ represents the scale factor, Let x(i) represent the largest integer not exceeding N / τ, and let x(i) represent the error sequence based on the pulse energy signal to be detected and the standard pulse energy signal.

4. The calibration method for a digital energy meter with self-calibration function according to claim 3, characterized in that, The steps of calculating the sample entropy value of each coarse-grained sequence, generating entropy spectrum features, and identifying the frequency components based on the error source in the entropy spectrum features specifically include: For the coarse-grained sequence corresponding to the scaling factor, construct a multidimensional vector sequence, calculate the maximum absolute error between any two multidimensional vector sequences, calculate the number of similar vectors based on the maximum absolute error, and then calculate the sample entropy value. Construct an entropy spectrum feature curve with the scale factor as the abscissa and the sample entropy value as the ordinate; Based on the different frequency components corresponding to the scale factors of different frequency interval types in the entropy spectrum characteristic curve, the entropy spectrum characteristic index of different frequency intervals is calculated, and the frequency components based on the error source in the entropy spectrum characteristic are identified based on the entropy spectrum characteristic index. Specifically, the scale factor corresponding to different frequency interval types corresponds to different frequency components as follows: when the scale factor is [1,5], the interval is a low-scale interval and the frequency component is a high-frequency component; when the scale factor is [6,12], the interval is a medium-scale interval and the frequency component is a medium-frequency component; when the scale factor is [13,20], the interval is a high-scale interval and the frequency component is a low-frequency component.

5. The calibration method for a digital energy meter with self-calibration function according to claim 4, characterized in that, The steps for calibrating a digital energy meter based on the frequency components and corresponding calibration weights include: establishing a mapping relationship between different frequency ranges and the calibration parameters of the digital energy meter; calculating the calibration weights and calibration coefficients of different frequency ranges based on the identified frequency components based on the error source; generating calibration parameters of the digital energy meter to be updated; and updating the calibration parameters in the digital energy meter to achieve calibration. The calibration parameters include high-frequency response parameters, mid-frequency response parameters, low-frequency response parameters, and reference parameters.

6. The calibration method for a digital energy meter with self-calibration function according to claim 5, characterized in that, The step of calculating the calibration weights for different frequency ranges based on the identified frequency components of the error source includes: calculating the calibration weights for the high-frequency range where the high-frequency components are located, the mid-frequency range where the mid-frequency components are located, and the low-frequency range where the low-frequency components are located, respectively; wherein The high-frequency interval calibration weight is expressed as the ratio of the variance of the low-scale interval entropy value to the sum of the variances of all interval entropy values; the mid-frequency interval calibration weight is expressed as the ratio of the variance of the mid-scale interval entropy value to the sum of the variances of all interval entropy values; and the low-frequency interval calibration weight is expressed as the ratio of the variance of the high-scale interval entropy value to the sum of the variances of all interval entropy values.

7. The calibration method for a digital energy meter with self-calibration function according to claim 5, characterized in that, The steps for calculating calibration coefficients for different frequency ranges based on the identified frequency components of the error source include: calculating calibration coefficients for high-frequency response parameters, mid-frequency response parameters, low-frequency response parameters, and reference parameters, respectively, wherein: The calibration coefficients of the high-frequency response parameters are represented as the high-frequency interval calibration weight, the low-scale interval average entropy value, the low-scale interval entropy slope sign, and the product with the preset first gain factor. The calibration coefficients of the intermediate frequency response parameters are expressed as the intermediate frequency interval calibration weight, the average entropy value of the mesoscale interval, the sign of the slope of the mesoscale interval entropy value, and the product with the preset second gain factor. The calibration coefficients of the low-frequency response parameters are represented as the low-frequency interval calibration weight, the high-scale interval average entropy value, the high-scale interval entropy slope sign, and the product with the preset third gain factor. The calibration coefficient of the reference parameter is expressed as the product of the weighted sum of the calibration weight of each interval and the average entropy value of the corresponding interval, and the preset fourth gain factor.

8. A calibration device for a digital energy meter with self-calibration function, characterized in that, The calibration device is used to perform the calibration method as described in any one of claims 1-7. The calibration device includes: a digital sampling module, used to receive voltage and current sampling values ​​in FT3 format from a network device, convert the FT3 signal into a digital signal and send it to the power metering module, and the power metering module outputs a pulse power signal to be detected according to the received digital signal. A digital standard measurement module is used to output a standard energy pulse signal based on the signal input to the digital energy meter; An error calculation module is used to preprocess the pulse energy signal to be detected and the standard pulse energy signal to generate an error sequence; to perform multi-scale coarse-graining processing on the error sequence to generate coarse-grained sequences based on different scale factors; to calculate the sample entropy value of each coarse-grained sequence to generate an entropy spectrum feature, and to identify the frequency components based on the error source in the entropy spectrum feature; and to realize the calibration of the digital energy meter based on the frequency components and the corresponding calibration weights. The display module is used to display the calibration results.

9. The calibration device for a digital energy meter with self-calibration function according to claim 8, characterized in that, The calibration device also includes a power quality monitoring module, which is used to monitor the power quality of the digital signals sent by the digital sampling module. The power quality monitoring module supports harmonic analysis of 100 to 2000th harmonics to accurately identify the harmonic components generated by the grid connection of new energy sources.

10. An electronic device, characterized in that, The device includes a memory and a processor, characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the calibration method of the digital energy meter with self-calibration function according to any one of claims 1-7.

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