A smart electric energy meter operation error detection test device

Abstract

The present invention provides an operation error detection device for a smart electric energy meter, which belongs to the technical field of smart electric energy meters, and comprises two AC ADC samplers and a control chip arranged upstream and downstream of the smart electric energy meter to be tested, wherein the control chip is electrically connected to the electric metering output terminal of the smart electric energy meter to be tested and the output terminals of the two AC ADC samplers, and an error detection module is arranged in the control chip, which is used to calculate the operation error of the smart electric energy meter to be tested according to the first instantaneous electric parameter and the second instantaneous electric parameter output by the two AC ADC samplers and the electric metering data of the smart electric energy meter to be tested. The present invention obtains the operation error interval of the electric energy meter to be tested by comprehensively collecting and deeply analyzing the upstream and downstream electric parameters of the electric energy meter to be tested, combining the voltage stability index and the current difference characteristics. This error detection method based on the comparison of upstream and downstream electric parameters can more quickly and accurately evaluate the operation error of the electric energy meter.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart electric energy meters, and in particular relates to an operation error detection and testing device for a smart electric energy meter. Background Art

[0002] As a key device in the current power system, the operating performance of smart energy meters is directly related to the stability of the power system and the accuracy of users' electricity consumption. In recent years, with the continuous advancement of the intelligence of the power system, the technology of energy meters has also experienced rapid development, from the initial mechanical energy meters to modern digital smart energy meters. However, even for smart energy meters, their operating performance cannot be completely ideal, and it is inevitable that certain measurement errors will occur. The existence of such measurement errors will not only affect users' electricity consumption, but may also cause other problems in the power system. Therefore, how to effectively detect the operating errors of energy meters has always been one of the key issues of concern to the power industry.

[0003] The existing energy meter error detection technologies mainly include the following methods:

[0004] 1. Manual inspection method. That is, manually inspect the electric energy meter on site regularly, collect various operating parameters of the electric energy meter, and compare them with the rated parameters to determine the operating status of the electric energy meter. This method has a large workload, low efficiency, and is easily affected by human factors, making it difficult to ensure the accuracy of the test results.

[0005] 2. Online monitoring method. Using the measuring equipment installed near the electric energy meter, the operating parameters of the electric energy meter are monitored in real time, and the data is transmitted to the centralized monitoring center for analysis. This method can continuously monitor the status of the electric energy meter, but it requires additional investment in a large number of monitoring equipment. There are also problems such as data transmission delay, which makes it difficult to detect abnormal conditions of the electric energy meter in time.

[0006] 3. Offline calibration method. The electric energy meter is sent to a professional testing agency for offline calibration on a regular basis. This method has high detection accuracy, but it requires interrupting the normal power supply of the electric energy meter, which brings inconvenience to the user. At the same time, the calibration cycle is long and the operation problems of the electric energy meter cannot be discovered in time.

[0007] In summary, the existing energy meter error detection technology generally has the problem of low online detection efficiency. In order to solve these problems, the industry urgently needs a new type of energy meter operation error detection technology that can achieve fast, accurate and automatic error detection and provide reliable data support for the stable operation of the power system. Summary of the invention

[0008] In view of this, the present invention provides an intelligent electric energy meter operation error detection test device, which can solve the technical problem of low online detection efficiency in the existing electric energy meter error detection technology.

[0009] The present invention is achieved in that:

[0010] A first aspect of the present invention provides an operation error detection test device for a smart electric energy meter, which includes two AC ADC samplers and a control chip arranged upstream and downstream of the smart electric energy meter to be tested. The control chip is electrically connected to the electric metering output end of the smart electric energy meter to be tested and the output ends of the two AC ADC samplers. An error detection module is arranged in the control chip, which is used to calculate the operation error of the smart electric energy meter to be tested based on the first instantaneous electric parameter and the second instantaneous electric parameter output by the two AC ADC samplers and the electric metering data of the smart electric energy meter to be tested.

[0011] Wherein, the error detection module is used to perform the following steps:

[0012] S10, acquiring the first instantaneous electrical parameter, the second instantaneous electrical parameter and electrical metering data, the first instantaneous electrical parameter comprising a first instantaneous current, a first instantaneous voltage, a first instantaneous frequency, and a first instantaneous power, and the second instantaneous electrical parameter comprising a second instantaneous current, a second instantaneous voltage, a second instantaneous frequency, and a second instantaneous power;

[0013] S20, generating a first current curve, a first voltage curve, a first frequency curve, a first power curve, a second current curve, a second voltage curve, a second frequency curve, a second power curve, and an electricity metering curve according to the first instantaneous electrical parameter, the second instantaneous electrical parameter, and the electricity metering data;

[0014] S30, performing multivariate mixed decomposition calculation on the first voltage curve and the second voltage curve to obtain two groups of fundamental wave and harmonic wave data;

[0015] S40, calculating a voltage stability coefficient matrix and an anti-error factor of an electric energy meter input by using the first frequency curve, the second frequency curve, and the obtained two groups of fundamental and harmonic signals;

[0016] S50, calculating the basic error range of the smart electric energy meter to be tested by using the first power curve, the second power curve, the voltage stability coefficient matrix and the electric metering curve;

[0017] S60: Correct the basic error interval by using the second current curve, the third current curve and the electricity metering curve to obtain a detection error interval of the smart electric energy meter, and output the obtained error interval.

[0018] Furthermore, the multivariate mixed decomposition calculation formula is specifically:

[0019]

[0020] Wherein, V(t) is a voltage signal, including a first voltage curve V1(t) and a second voltage curve V2(t); A n is the amplitude of the nth harmonic; f is the fundamental frequency; φ n is the nth harmonic phase; B m ,α m ,f m ,θ m is the attenuated sinusoidal component parameter; N is the preset harmonic order; M is the preset number of attenuated sinusoidal components.

[0021] Furthermore, the voltage stability coefficient matrix is ​​specifically:

[0022]

[0023] in:

[0024]

[0025] In the formula, is the i-th harmonic component of the first voltage curve; is the jth harmonic component of the second voltage curve; V nom is the rated voltage; T is the observation period, and i and j are both positive integers less than N.

[0026] Furthermore, the anti-error factor is specifically:

[0027]

[0028] In the formula, A 1n is the nth harmonic amplitude of the first voltage curve; A 2n is the nth harmonic amplitude of the second voltage curve.

[0029] Furthermore, the calculation formula of the basic error interval is specifically:

[0030] E base =[E min ,E max ];

[0031] in:

[0032]

[0033] Where P1(t) is the first power curve; P2(t) is the second power curve; λ max is the maximum eigenvalue of the voltage stability coefficient matrix.

[0034] Furthermore, the correction calculation formula of the basic error interval is specifically:

[0035]

[0036] in:

[0037]

[0038] Wherein, I1(t) is the second current curve, and I2(t) is the third current curve.

[0039] The control chip is also connected to an analog circuit for performing analog pre-processing on the collected analog electrical parameter signals, including filtering and amplification, and then sending them to the ADC for conversion into digital signals, and finally transmitting them to the error detection module for digital processing.

[0040] Wherein, the sampling frequency of the AC ADC sampler is greater than 2KHz.

[0041] Among them, two AC ADC samplers are respectively installed at the electric input end and the electric output end of the smart electric energy meter to be tested.

[0042] Compared with the prior art, the beneficial effects of the intelligent electric energy meter operation error detection test device provided by the present invention are:

[0043] 1. High-precision detection. The device of the present invention adopts advanced AC ADC sampling technology, which can accurately collect key electrical parameters such as instantaneous current, voltage, frequency, power, etc. upstream and downstream of the electric energy meter to be tested. At the same time, the device is equipped with a multivariate hybrid decomposition algorithm, which can extract the fundamental and harmonic components of the voltage curve, providing data support for subsequent voltage stability analysis. Based on these high-precision electrical parameter data, the device can accurately calculate the operating error range of the electric energy meter to be tested, and the detection results are highly reliable.

[0044] 2. Online automatic detection. The device of the present invention can realize automatic monitoring and diagnosis of the operating status of the electric energy meter without human intervention. During operation, the device automatically collects electrical parameter data, and uses the built-in error detection module to perform real-time analysis and calculation, and finally outputs the detection results of the electric energy meter to be tested. This automatic detection method greatly improves work efficiency, avoids interference from human factors, and provides strong support for the intelligent management of the power system.

[0045] 3. Fault diagnosis function. The device of the present invention can not only detect the metering error of the electric energy meter, but also diagnose whether the electric energy meter has a fault or abnormality based on indicators such as the voltage stability coefficient matrix and the anti-error factor. When an abnormality is found, the device can issue an alarm in time, providing a basis for the maintenance of the electric energy meter and improving the reliability of the power system.

[0046] In summary, the intelligent electric energy meter operation error detection device proposed in the present invention effectively solves the technical problem of low online detection efficiency in the electric energy meter error detection technology in the prior art by adopting advanced electrical parameter acquisition technology, efficient error analysis algorithm, and automatic diagnosis function. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0048] Figure 1 A schematic diagram of a smart electric energy meter operation error detection and testing device provided by the present invention;

[0049] Figure 2 Flowchart of the steps performed by the error detection module. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0051] like Figure 1 As shown in the figure, it is a schematic diagram of a smart energy meter operation error detection test device provided by the present invention. The device includes two AC ADC samplers and a control chip arranged upstream and downstream of the smart energy meter to be tested. The control chip is electrically connected to the electric metering output terminal of the smart energy meter to be tested and the output terminals of the two AC ADC samplers. An error detection module is arranged in the control chip, which is used to calculate the operation error of the smart energy meter to be tested according to the first instantaneous electric parameter and the second instantaneous electric parameter output by the two AC ADC samplers and the electric metering data of the smart energy meter to be tested. The two AC ADC samplers are respectively installed at the electric input terminal and the electric output terminal of the smart energy meter to be tested. Figure 2 As shown, the error detection module is used to perform the following steps:

[0052] S10, acquiring the first instantaneous electrical parameter, the second instantaneous electrical parameter and electrical metering data, the first instantaneous electrical parameter comprising a first instantaneous current, a first instantaneous voltage, a first instantaneous frequency, and a first instantaneous power, and the second instantaneous electrical parameter comprising a second instantaneous current, a second instantaneous voltage, a second instantaneous frequency, and a second instantaneous power;

[0053] S20, generating a first current curve, a first voltage curve, a first frequency curve, a first power curve, a second current curve, a second voltage curve, a second frequency curve, a second power curve, and an electricity metering curve according to the first instantaneous electrical parameter, the second instantaneous electrical parameter, and the electricity metering data;

[0054] S30, performing multivariate mixed decomposition calculation on the first voltage curve and the second voltage curve to obtain two groups of fundamental wave and harmonic wave data;

[0055] S40, calculating a voltage stability coefficient matrix and an anti-error factor of an electric energy meter input by using the first frequency curve, the second frequency curve, and the obtained two groups of fundamental and harmonic signals;

[0056] S50, calculating the basic error range of the smart electric energy meter to be tested by using the first power curve, the second power curve, the voltage stability coefficient matrix and the electric metering curve;

[0057] S60: Correct the basic error interval by using the second current curve, the third current curve and the electricity metering curve to obtain a detection error interval of the smart electric energy meter, and output the obtained error interval.

[0058] The specific implementation methods of the above steps are described in detail below:

[0059] Step S10: Obtaining the electrical parameters of the electric energy meter to be tested

[0060] In this step, the device will collect the instantaneous current, voltage, frequency, power and other electrical parameters of the upstream and downstream of the electric energy meter to be tested. It specifically includes the following sub-steps:

[0061] 1) Two AC ADC samplers installed upstream and downstream of the electric energy meter to be tested are used to collect the electrical parameter data such as the instantaneous current, voltage, frequency and power of the upstream and downstream, respectively. These electrical parameter data constitute the first instantaneous electrical parameter and the second instantaneous electrical parameter.

[0062] 2) At the same time, the electricity meter data of the electric energy meter to be tested is collected as a reference for the test.

[0063] 3) The collected first instantaneous electrical parameter, second instantaneous electrical parameter and electrical metering data are transmitted to the error detection module of the control chip to prepare for subsequent error analysis and calculation.

[0064] This step of data collection lays the foundation for subsequent error analysis and calculation.

[0065] Step S20: Generate electrical parameter curve

[0066] In this step, the device will generate upstream current, voltage, frequency and power curves, as well as downstream current, voltage, frequency and power curves based on the collected electrical parameter data, and use them together with the electrical metering curve of the energy meter for subsequent analysis. It includes the following sub-steps:

[0067] 1) According to the collected first instantaneous electrical parameters, a first current curve, a first voltage curve, a first frequency curve and a first power curve are drawn.

[0068] 2) Based on the collected second instantaneous electrical parameters, draw a second current curve, a second voltage curve, a second frequency curve and a second power curve.

[0069] 3) Draw the electricity metering curve based on the collected electricity metering data.

[0070] These curves will provide necessary data support for subsequent error analysis and calculations.

[0071] Step S30: Multivariate mixed decomposition of voltage curve

[0072] In this step, the device will perform multivariate mixed decomposition on the upstream and downstream voltage curves, extract the fundamental and harmonic components, and prepare for the subsequent voltage stability analysis. The specific steps include the following:

[0073] 1) Using the multivariate hybrid decomposition algorithm, the first voltage curve V1(t) and the second voltage curve V2(t) are decomposed and calculated to obtain their fundamental and harmonic component parameters.

[0074] The mathematical model of the multivariate mixed decomposition algorithm is:

[0075]

[0076] Where V(t) represents the voltage signal, including V1(t) and V2(t). n is the amplitude of the nth harmonic, f is the fundamental frequency, φ n is the phase of the nth harmonic. m , α m 、f m ,θ m is the parameter of the attenuated sinusoidal component. N and M are the harmonic order and number of attenuated sinusoidal components considered, respectively.

[0077] The preset harmonic order (usually represented by N) refers to the order of the highest harmonic component contained in the voltage or current signal analysis. This parameter plays an important role in power system analysis, especially in the error detection of electric energy meters.

[0078] Harmonics refer to sinusoidal components whose frequencies are integer multiples of the fundamental frequency. In power systems, the fundamental frequency is usually 50Hz or 60Hz. For example, 100Hz is the second harmonic, 150Hz is the third harmonic, and so on.

[0079] Mathematical representation: In the multivariate mixture decomposition model, the harmonic part can be expressed as:

[0080]

[0081] Where N is the preset highest harmonic order.

[0082] Considerations for choosing the value of N:

[0083] a) Grid characteristics: Different types of loads will generate harmonics of different orders. For example, rectifiers often generate 5th, 7th, 11th, and 13th harmonics.

[0084] b) Standard requirements: Some power quality standards (such as IEEE 519) specify the harmonic orders that need to be considered.

[0085] c) Measurement equipment capabilities: The sampling rate and resolution of the ADC will limit the highest harmonic order that can be accurately measured.

[0086] d) Computational resources: Higher values ​​of N increase the computational complexity.

[0087] For general electric energy meter error analysis, 13th or 25th harmonics are usually considered; in situations where high precision is required, 50th or even higher harmonics may be considered; the larger the N value, the higher frequency fluctuations the model can describe, but it also increases the amount of calculation and possible noise impact; too small an N value may miss important high-order harmonic information, resulting in inaccurate analysis. According to the Nyquist sampling theorem, to accurately capture the Nth harmonic, the sampling frequency should be at least twice the highest harmonic frequency. For example, to analyze the 25th harmonic (1250Hz), the sampling frequency should be at least 2500Hz.

[0088] The number of decaying sinusoidal components (usually denoted by M) is a key parameter in the multivariate mixed decomposition model, which is used to describe the transient components in the voltage signal. These transient components are usually caused by transient disturbances in the power grid, such as switching operations, load mutations, lightning strikes, etc. Each decaying sinusoidal component can be expressed as In the form of B m is the initial amplitude, α m is the attenuation coefficient, f m is the frequency, θ m is the initial phase.

[0089] The choice of M value is usually between 2 and 5, which can achieve a good balance between model complexity and description accuracy. Smaller M values ​​(such as 2 or 3) are usually suitable for relatively stable distribution networks, while larger M values ​​(such as 4 or 5) may be required in industrial environments with frequent disturbances. Several factors need to be considered when choosing the M value: the complexity of the signal, the required model accuracy, computing resource constraints, and the balance with other model parameters (such as the number of harmonics considered N).

[0090] Increasing the M value can improve the model's ability to capture complex transient phenomena, but it will also significantly increase the computational complexity and the risk of overfitting. In practical applications, adaptive algorithms (such as AIC or BIC) can be used to dynamically select the optimal M value, or a maximum M value can be set and M can be gradually increased using an iterative method until the accuracy requirements are met.

[0091] For the error analysis of electric energy meters, it is crucial to accurately simulate the transient components in the voltage signal, because these transient phenomena may cause measurement errors. By properly selecting the number of attenuated sinusoidal components, the dynamic characteristics of the power grid can be more accurately described, thereby improving the accuracy and reliability of the error detection of the electric energy meter operation.

[0092] 2) Through this algorithm, parameters such as the fundamental amplitude, phase, and harmonic amplitude and phase of the upstream and downstream voltage curves can be extracted to provide basic data for subsequent voltage stability analysis.

[0093] Step S40: Calculate voltage stability coefficient matrix and anti-error factor

[0094] In this step, the device will use the fundamental and harmonic parameters obtained in the previous step to calculate the coefficient matrix reflecting the voltage stability and the anti-error factor reflecting the anti-interference ability, providing a basis for error analysis. It includes the following sub-steps:

[0095] 1) Calculate the voltage stability coefficient matrix S:

[0096]

[0097] in,

[0098]

[0099] and are the i-th and j-th harmonic components of the first voltage curve and the second voltage curve, respectively, V nom is the rated voltage, and T is the observation period.

[0100] 2) Calculate the anti-error factor η:

[0101]

[0102] Among them, A 1n and A 2n are the nth harmonic amplitudes of the first voltage curve and the second voltage curve respectively.

[0103] The voltage stability coefficient matrix reflects the degree of difference in the harmonic characteristics of the upstream and downstream voltages, and the anti-error factor quantifies this difference, providing an important basis for subsequent error calculations.

[0104] Step S50: Calculate the basic error interval

[0105] In this step, the device will use the electrical parameter curve and voltage stability index obtained in the previous step to calculate the basic error range of the electric energy meter to be tested, preparing for the final error correction. It specifically includes the following sub-steps:

[0106] 1) According to the first power curve P1(t), the second power curve P2(t) and the maximum eigenvalue λ of the voltage stability coefficient matrix S max and the error resistance factor η, calculate the basic error interval [E min ,E max ]:

[0107]

[0108]

[0109] 2) The correction factors (1-η) and (1+η) are introduced here to better reflect the impact of the anti-interference ability of the electric energy meter on the error. When η is small, it means that the electric energy meter has strong anti-interference ability, and the basic error range will be reduced accordingly; when η is large, it means that the electric energy meter may be subject to greater interference, and the basic error range will be expanded accordingly.

[0110] Through this step of calculation, the basic error range of the electric energy meter to be tested is obtained, providing a basis for subsequent error correction.

[0111] Step S60: Correct the basic error interval

[0112] In this step, the device will use the second current curve and the third current curve to further correct the basic error interval calculated previously to obtain the final detection error interval. Specifically, it includes the following sub-steps:

[0113] 1) Calculate the current difference coefficient δ:

[0114]

[0115] Among them, I1(t) is the second current curve, I2(t) is the third current curve, and T is the observation period.

[0116] 2) Using δ to calculate the basic error interval [E min ,E max ] to correct and obtain the final detection error interval

[0117]

[0118] 3) The current difference coefficient δ is introduced here for correction in order to better reflect the impact of the actual operating state of the electric energy meter on the error. When δ is small, it means that the electric energy meter is running relatively stably, and the corrected error interval will be correspondingly reduced; when δ is large, it means that there may be problems with the electric energy meter, and the corrected error interval will be correspondingly expanded.

[0119] Through this correction step, the final detection error range of the electric energy meter to be tested is obtained, which can more accurately evaluate the operating performance of the electric energy meter.

[0120] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below:

[0121] Step S10: Obtaining the electrical parameters of the electric energy meter to be tested

[0122] This step is the basis of the entire detection process. It collects electrical parameter data from upstream and downstream of the energy meter to provide raw data for subsequent analysis and calculation. Specifically, it includes:

[0123] 1. First instantaneous electrical parameters (upstream):

[0124] The first instantaneous current I1(t): represents the instantaneous current value at the input end of the electric energy meter, in ampere (A);

[0125] The first instantaneous voltage V1(t): represents the instantaneous voltage value at the input end of the energy meter, in volts (V);

[0126] First instantaneous frequency f1(t): represents the instantaneous frequency value at the input end of the electric energy meter, in Hertz (Hz);

[0127] First instantaneous power P1(t): represents the instantaneous power value at the input end of the electric energy meter, in watts (W);

[0128] 2. Second instantaneous electrical parameters (downstream):

[0129] The second instantaneous current I2(t): represents the instantaneous current value at the output end of the electric energy meter, in ampere (A);

[0130] The second instantaneous voltage V2(t): represents the instantaneous voltage value at the output end of the electric energy meter, in volts (V);

[0131] The second instantaneous frequency f2(t): represents the instantaneous frequency value at the output end of the electric energy meter, in Hertz (Hz);

[0132] Second instantaneous power P2(t): represents the instantaneous power value at the output of the electric energy meter, in watts (W);

[0133] 3. Electricity metering data E(t): represents the electricity consumption recorded by the electricity meter itself, in kilowatt-hours (kWh);

[0134] These data are collected by high-precision AC ADC samplers, and the sampling frequency is usually between several thousand hertz and tens of thousands of hertz to ensure that the high-order harmonic components in the power grid can be captured. The sampling time span is usually from a few minutes to a few hours to observe the performance of the energy meter under different load conditions.

[0135] Step S20: Generate electrical parameter curve

[0136] In this step, the collected discrete data points are converted into continuous curves to facilitate subsequent analysis and calculations. The generated curves include:

[0137] The first current curve I1(t): reflects the change of upstream current over time;

[0138] The first voltage curve V1(t): reflects the change of the upstream voltage over time;

[0139] The first frequency curve f1(t): reflects the change of upstream frequency over time;

[0140] The first power curve P1(t): reflects the change of upstream power over time;

[0141] The second current curve I2(t): reflects the change of downstream current over time;

[0142] The second voltage curve V2(t): reflects the change of downstream voltage over time;

[0143] The second frequency curve f2(t): reflects the change of downstream frequency over time;

[0144] The second power curve P2(t): reflects the change of downstream power over time;

[0145] Electricity metering curve E(t): reflects the accumulation of electricity consumption recorded by the electricity meter over time.

[0146] These curves are usually generated by interpolation or fitting methods, such as linear interpolation, cubic spline interpolation, etc., to ensure the smoothness and continuity of the curves.

[0147] Step S30: Multivariate mixed decomposition of voltage curve

[0148] The core of this step is to perform multivariate mixed decomposition on the voltage curve to extract the fundamental and harmonic components. The mathematical model used is:

[0149]

[0150] in:

[0151] V(t): represents voltage signal, which can be V1(t) or V2(t).

[0152] t: time variable, in seconds (s).

[0153] n: harmonic order, n=1 represents fundamental wave, n>1 represents harmonic.

[0154] N: The highest harmonic order considered, usually taken to be 13th or 25th, depending on the required accuracy.

[0155] A n : The amplitude of the nth harmonic, in volts (V).

[0156] f: Fundamental frequency, usually 50Hz or 60Hz, depending on the grid standard.

[0157] φ n : The phase angle of the nth harmonic, in radians (rad).

[0158] m: The number of the decaying sinusoidal component.

[0159] M: The number of decaying sinusoidal components considered, usually 2-5.

[0160] B m : The initial amplitude of the mth decaying sinusoidal component, in volts (V).

[0161] α m : The attenuation coefficient of the mth decaying sinusoidal component, in 1 / second (s -1 ).

[0162] f m : The frequency of the mth decaying sinusoidal component in Hertz (Hz).

[0163] θ m : The initial phase of the mth decaying sinusoidal component, in radians (rad).

[0164] The first part of this model Represents steady-state harmonic components, including fundamental and higher harmonics. Part II Represents the transient component and is used to describe transient disturbances in the power grid.

[0165] By performing multivariate mixed decomposition on V1(t) and V2(t), we can obtain:

[0166] 1. Fundamental wave parameters: A1, φ1 (calculated for V1 and V2 separately)

[0167] 2. Harmonic parameters: A n ,φ n (n>1, calculated for V1 and V2 separately)

[0168] 3. Transient component parameters: B m ,α m ,f m ,θ m (Calculated separately for V1 and V2)

[0169] These parameters reflect the detailed characteristics of the voltage waveform and provide a basis for subsequent stability analysis.

[0170] Step S40: Calculate voltage stability coefficient matrix and anti-error factor

[0171] 1. Calculation of voltage stability coefficient matrix S:

[0172]

[0173] The calculation formula of the matrix elements is:

[0174]

[0175] here:

[0176] i,j: represents the harmonic order, from 1 to N.

[0177] T: Observation period, usually taken as an integer multiple of the fundamental wave period, such as 1 second or 10 seconds.

[0178] The i-th harmonic component of the first voltage curve.

[0179] The j-th harmonic component of the second voltage curve.

[0180] V nom : Rated voltage, usually 220V or 380V, depending on the grid standard.

[0181] The physical meaning of the voltage stability coefficient matrix S is:

[0182] Diagonal elements s ii Indicates the degree of difference between the same harmonic in the upstream and downstream. The smaller the value, the smaller the change of the harmonic in the transmission process and the better the stability; the non-diagonal element s ij(i≠j) indicates the mutual influence between harmonics of different orders. The smaller the value, the weaker the coupling between harmonics and the more stable the system.

[0183] 2. Calculation of the anti-error factor η:

[0184]

[0185] in:

[0186] n: harmonic number, from 1 to N.

[0187] A 1n : The nth harmonic amplitude of the first voltage curve (upstream);

[0188] A 2n : The nth harmonic amplitude of the second voltage curve (downstream).

[0189] The physical meaning of the anti-error factor η:

[0190] The value range of η is [0,1].

[0191] The closer η is to 0, the more similar the upstream and downstream voltage waveforms are, and the stronger the anti-interference ability of the electric energy meter is;

[0192] The closer η is to 1, the greater the difference between the upstream and downstream voltage waveforms, and the electric energy meter may be subject to greater interference;

[0193] The voltage stability coefficient matrix S and the anti-error factor η jointly reflect the voltage stability and anti-interference ability of the electric energy meter in the actual operating environment, providing an important basis for subsequent error analysis.

[0194] Step S50: Calculate the basic error interval

[0195] In this step, the basic error range of the electric energy meter to be tested is calculated using the previously obtained electrical parameter curve and voltage stability index. The calculation formula is as follows:

[0196]

[0197] in:

[0198] t: time variable, ranging from [0, T], where T is the observation period;

[0199] P1(t): first power curve, indicating upstream power;

[0200] P2(t): second power curve, indicating downstream power;

[0201] λ max : The maximum eigenvalue of the voltage stability coefficient matrix S.

[0202] η: Anti-error factor.

[0203] The meaning and function of the various parts of these formulas are as follows:

[0204] This part calculates the relative difference between the upstream and downstream powers; the minimum and maximum values ​​are used to calculate the lower and upper limits of the error respectively; this reflects the basic error range of the energy meter during the entire observation period.

[0205] 2.(1-λ max ) and (1+λ max ):λ max is the maximum eigenvalue of the voltage stability coefficient matrix S, reflecting the overall stability of the voltage waveform. This factor is used to adjust the error range according to voltage stability; when λ max When it is smaller, it means the voltage is more stable and the error range will be reduced accordingly; when λ max When it is larger, it means the voltage is unstable and the error range will expand accordingly.

[0206] 3. (1-η) and (1+η): η is the anti-error factor, which reflects the anti-interference ability of the electricity meter.

[0207] This factor is used to further adjust the error range according to the anti-interference ability of the energy meter;

[0208] When η is small, it means that the electric energy meter has strong anti-interference ability and the error range will be further reduced;

[0209] When η is large, it means that the electric energy meter may be subject to greater interference and the error range will be further expanded.

[0210] The [E min ,E max ] is the basic error range of the energy meter. This range takes into account the direct error of power measurement, the influence of voltage stability and the anti-interference ability of the energy meter itself, providing a more comprehensive error estimate.

[0211] Step S60: Correct the basic error interval

[0212] In the last step, the basic error interval is further corrected using the current information to obtain the final detection error interval. The calculation process of this step is as follows:

[0213] 1. First, calculate the current difference coefficient δ:

[0214]

[0215] in:

[0216] T: observation period, in seconds (s);

[0217] I1(t): the second current curve, indicating the upstream current;

[0218] I2(t): the third current curve, indicating the downstream current;

[0219] The physical meaning of the current difference coefficient δ:

[0220] δ reflects the average relative difference between upstream and downstream currents.

[0221] The value range of δ is [0,+∞);

[0222] The closer δ is to 0, the closer the upstream and downstream currents are, and the more stable the operation of the energy meter is;

[0223] The larger the δ, the greater the difference between upstream and downstream currents, which may indicate energy metering problems or load fluctuations.

[0224] 2. Then, use δ to calculate the basic error interval [E min ,E max ] to make corrections:

[0225]

[0226] in:

[0227] in:

[0228] Corrected lower error limit;

[0229] The corrected upper error limit;

[0230] E min : The lower limit of the basic error interval;

[0231] E max : The upper limit of the basic error interval;

[0232] The principle and effect of this correction process:

[0233] 1. When δ is small (close to 0):

[0234] It indicates that the upstream and downstream currents are very close and the energy meter is running stably.

[0235] (1-δ) is close to 1, Almost equal to E min ;

[0236] (1+δ) is also close to 1, Almost equal to E max ;

[0237] Results: The corrected error interval is basically the same as the basic error interval, indicating that the electric energy meter operates in a stable state.

[0238] 2. When δ is large:

[0239] This indicates that there is a significant difference in the upstream and downstream currents, which may be due to metering problems or load fluctuations;

[0240] (1-δ) is significantly less than 1, so E min Smaller;

[0241] (1+δ) is obviously greater than 1, so E max Bigger;

[0242] Results: The corrected error interval is wider than the basic error interval, reflecting that the electric energy meter may produce larger errors when in an unstable state.

[0243] Advantages of this correction method:

[0244] 1. Adaptability: Dynamically adjust the error range according to the actual situation of current difference.

[0245] 2. Sensitivity: Ability to capture subtle changes in the operating status of the electricity meter.

[0246] 3. Comprehensiveness: The influence of both voltage and current is taken into consideration.

[0247] Things to note during implementation:

[0248] 1. Sampling frequency: In order to accurately calculate δ, the current sampling frequency must be high enough, usually not less than 10kHz.

[0249] 2. Filtering: When calculating |I1(t)-I2(t)|, it may be necessary to perform low-pass filtering on the original data to remove the influence of high-frequency noise.

[0250] 3. Time synchronization: Ensure that the time of I1(t) and I2(t) is completely synchronized, otherwise it may cause errors in the calculation of δ.

[0251] 4. Threshold setting: You can set a threshold δ for δ th (such as 0.01), when δ<δ th When , the current can be considered to be basically stable and the error range does not need to be expanded.

[0252] 5. Nonlinear correction: In some cases, it is possible to consider using a nonlinear function to replace (1-δ) and (1+δ) to more accurately reflect the impact of current differences on errors. For example:

[0253]

[0254] Where k is an adjustable parameter that controls the sensitivity of the correction.

[0255] 6. Periodic inspection: During long-term operation, δ and error interval should be recalculated regularly to adapt to possible load changes or grid status changes.

[0256] The final detection error interval obtained by this method Not only the stability of the voltage waveform and the anti-interference ability of the energy meter are taken into account, but also the actual change of the current is further considered. This provides a more comprehensive and accurate error evaluation result of the energy meter.

[0257] Specifically, the principle of the present invention is to evaluate the actual operating state of the electric energy meter and calculate its operating error by comparing and analyzing the upstream and downstream electrical parameters of the electric energy meter to be tested. The specific principle is as follows:

[0258] 1. Electric parameter collection. The device sets an AC ADC sampler upstream and downstream of the electric energy meter to be tested, which can accurately collect electric parameter data such as instantaneous current, voltage, frequency and power. At the same time, the device also collects the electric meter data of the electric energy meter to be tested as a reference standard for detection.

[0259] 2. Electric parameter curve generation. The device draws the current curve, voltage curve, frequency curve and power curve, as well as the electric meter measurement curve of the electric energy meter, based on the collected upstream and downstream electric parameter data. These curves provide basic data for subsequent error analysis and calculation.

[0260] 3. Voltage stability analysis. The device uses a multivariate hybrid decomposition algorithm to perform frequency domain analysis on the upstream and downstream voltage curves, extracting the amplitude, phase and other parameters of the fundamental and harmonic waves. Based on these parameters, the device calculates the coefficient matrix reflecting the voltage stability and the anti-error factor reflecting the anti-interference ability. These indicators provide a basis for error analysis.

[0261] 4. Basic error interval calculation. The device uses the upstream and downstream power curves, voltage stability coefficient matrix and anti-error factor to calculate the basic error interval of the electric energy meter to be tested. This step takes into account the impact of voltage characteristics on the error and lays the foundation for subsequent error correction.

[0262] 5. Error correction. The device further uses the upstream and downstream current curves to calculate the current difference coefficient, and uses this to correct the basic error range to obtain the final detection error range. This step takes into account the impact of current characteristics on the error, so that the detection results more accurately reflect the actual operating status of the energy meter.

[0263] In summary, the working principle of the device of the present invention is to obtain the operating error range of the electric energy meter to be tested by comprehensively collecting and deeply analyzing the upstream and downstream electrical parameters of the electric energy meter to be tested, combining the voltage stability index and the current difference characteristics. This error detection method based on the comparison of upstream and downstream electrical parameters can more accurately evaluate the actual operating status of the electric energy meter and provide reliable data support for the stable operation of the power system.

[0264] The following is an example 2 of a specific application scenario of the present invention: A power company is responsible for managing the power system of a city, which includes many industrial and commercial users and residential users. In order to ensure the stable operation of the power system and the accuracy of users' electricity consumption, the power company decided to deploy the smart electric energy meter operation error detection device proposed by the present invention at key users to monitor and analyze the operating conditions of key electric energy meters in real time. In the specific implementation process, the power company selected a large shopping mall as a pilot and deployed a set of detection devices of the present invention. The shopping mall consumes about 500,000 kWh of electricity per month and is a key user of the local power grid.

[0265] 1. Installation and initialization of the device

[0266] The staff of the power company first installed two AC ADC samplers in the distribution room of the mall, and connected them to the upstream and downstream circuits of the energy meter to be tested. The sampling rate of the sampler was set to 10kHz to ensure that the instantaneous changes of electrical parameters can be accurately captured. At the same time, the staff also connected the electrical meter output end of the energy meter to be tested with the communication interface of the control chip to realize data exchange.

[0267] AC ADC sampler: Model: ADE7878; Sampling accuracy: 24 bits; Sampling frequency: 10kHz; Input voltage range: ±0.5V; Input current range: ±50mA; Power consumption: <15mW;

[0268] Control chip: Model: STM32F407VGT6; CPU: ARM Cortex-M4; Main frequency: 168MHz; Flash: 1MB; RAM: 192KB; Interface: SPI, UART, I2C, etc.;

[0269] Connection mode: ADC sampler and control chip: SPI interface, 3.3V level; the energy meter to be tested and the control chip: RS485 interface, 9600bps;

[0270] Implementation steps:

[0271] Two AC ADC samplers are installed at the input and output ends of the smart electric energy meter to be tested respectively.

[0272] The ADC sampler is connected to the control chip through the SPI interface.

[0273] The smart energy meter to be tested is connected to the control chip via the RS485 interface.

[0274] After the control chip is started, the data of the two ADC samplers are read through the SPI interface to obtain the first instantaneous electrical parameter and the second instantaneous electrical parameter.

[0275] The control chip reads the electricity metering data of the smart energy meter to be tested through the RS485 interface.

[0276] The control chip is equipped with an error detection module, which is responsible for analyzing and calculating the collected electrical parameter data. During the initialization process, the staff configured the parameters of this module:

[0277] The harmonic order N considered is set to 50, that is, the harmonic components within 50 in the voltage signal are analyzed;

[0278] The number of decaying sinusoidal components considered, M, is set to 20, which can better fit the decaying components in the voltage signal;

[0279] The voltage stability coefficient matrix S is calculated using an observation period T of 1 second;

[0280] The calculation of the error-proof factor η is also based on an observation period of 1 second.

[0281] After the configuration is completed, the control chip starts to automatically collect data and transfers it to the built-in storage module for temporary storage, preparing for subsequent error analysis.

[0282] 2. Collection of electrical parameter data and curve drawing

[0283] During continuous operation, the AC ADC sampler collects instantaneous current, voltage, frequency and power data of the upstream and downstream of the electric energy meter under test every 0.1 second. The specific collection results are shown in Table 1:

[0284] Table 1 Collection results

[0285]

[0286] According to the collected instantaneous electrical parameter data, the control chip draws the upstream and downstream current curves, voltage curves, frequency curves and power curves, as well as the electrical metering curve of the electric energy meter to be tested. Due to the aging of the electric energy meter and other reasons, there are certain differences in the upstream and downstream electrical parameters, which may cause the electric energy meter to produce measurement errors. In order to further analyze this difference, it is necessary to perform harmonic analysis on the voltage curve.

[0287] 3. Multivariate mixed decomposition of voltage curve

[0288] The control chip uses a multivariate hybrid decomposition algorithm to perform frequency domain analysis on the upstream and downstream voltage curves and extract the parameters of the fundamental and harmonic components.

[0289] For the upstream voltage curve V1(t), the decomposition results are as follows:

[0290] Fundamental frequency f = 50 Hz

[0291] Fundamental wave amplitude A1=220V

[0292] 2nd harmonic amplitude A2=3.2V, phase φ2=30°

[0293] 3rd harmonic amplitude A3=2.1V, phase φ3=45° ...

[0294] 50th harmonic amplitude A50=0.3V, phase φ50=90° Attenuated sine component parameters:

[0295] B1=1.5V, α1=2, f1=100Hz, θ1=60°

[0296] B2=1.2V, α2=3, f2=150Hz, θ2=75° ...

[0297] B20=0.4V, α20=10, f20=500Hz, θ20=90°

[0298] For the downstream voltage curve V2(t), the decomposition results are as follows:

[0299] Fundamental frequency f = 50 Hz

[0300] Fundamental amplitude A1=219.5V

[0301] Second harmonic amplitude A2 = 2.9V, phase φ2 = 32°

[0302] 3rd harmonic amplitude A3=1.8V, phase φ3=48° ...

[0303] 50th harmonic amplitude A50=0.2V, phase φ50=92° Attenuated sine component parameters:

[0304] B1=1.3V, α1=2.5, f1=100Hz, θ1=65°

[0305] B2=1.0V, α2=3.2, f2=150Hz, θ2=80° ...

[0306] B20=0.3V, α20=12, f20=500Hz, θ20=95°

[0307] From the above results, it can be seen that there are certain differences in the fundamental amplitude and harmonic characteristics of the upstream and downstream voltage curves, which may affect the measurement accuracy of the electric energy meter. In order to further evaluate the impact of this difference on the error, it is necessary to calculate the voltage stability coefficient matrix and the anti-error factor.

[0308] 4. Calculation of voltage stability coefficient matrix and anti-error factor

[0309] Based on the fundamental and harmonic parameters obtained in the previous step, the control chip calculates the voltage stability coefficient matrix S and the anti-error factor η.

[0310] The voltage stability coefficient matrix S is:

[0311]

[0312] Anti-error factor η=0.08

[0313] It can be seen from the voltage stability coefficient matrix that the harmonic characteristics of the upstream and downstream voltages are slightly different, and the matrix element values ​​are all within 0.01, indicating that the voltage quality is relatively stable. The anti-error factor η is also small, indicating that the electric energy meter has a strong anti-interference ability. These indicators provide a favorable basis for subsequent error analysis.

[0314] 5. Calculation of basic error interval

[0315] Based on the voltage stability coefficient matrix and anti-error factor calculated in the previous step, as well as the collected power curve data, the control chip calculates the basic error range of the electric energy meter to be tested:

[0316] First, calculate the maximum eigenvalue λ of the voltage stability coefficient matrix S max =0.015.

[0317] Then, based on the power curve data, calculate:

[0318]

[0319] Therefore, the basic error range of the electric energy meter to be tested is [0.12%, 0.28%].

[0320] The calculation results of this step show that when the voltage quality and anti-interference performance are good, the basic measurement error of the electric energy meter to be tested is within a small range, but it still needs further correction.

[0321] Finally, the control chip uses the collected upstream and downstream current data to calculate the current difference coefficient δ:

[0322]

[0323] Then, substitute δ into the formula of the basic error interval for correction to obtain the final detection error interval:

[0324]

[0325] Therefore, after correction, the final detection error range of the electric energy meter to be tested is [0.115%, 0.291%].

[0326] In summary, during the on-site pilot test at the shopping mall, the smart electric energy meter operation error detection device of the present invention has demonstrated good performance:

[0327] 1. The various electrical parameters of the upstream and downstream of the electric energy meter to be tested were collected and detailed curves were drawn, providing a reliable data basis for subsequent analysis.

[0328] 2. Through the multivariate mixed decomposition of the voltage curve, the fundamental and harmonic characteristics are extracted, and the coefficient matrix reflecting the voltage stability and the anti-error factor reflecting the anti-interference ability are calculated. These indicators show that the power supply quality of the mall is relatively stable and the electric energy meter is less affected by external interference.

[0329] 3. Based on the above electrical parameter analysis results, the device calculated the basic error range of the electric energy meter to be tested, and further considered the influence of current characteristics to obtain the final detection error range. This result shows that the actual operating error of the electric energy meter is within a controllable range and meets the requirements of the power system.

[0330] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart electric energy meter operation error detection test device, characterized in that: The invention comprises two AC ADC samplers and a control chip arranged upstream and downstream of the smart electric energy meter to be tested. The control chip is electrically connected to the electric metering output terminal of the smart electric energy meter to be tested and the output terminals of the two AC ADC samplers. The control chip is provided with an error detection module for calculating the operation error of the smart electric energy meter to be tested according to the first instantaneous electric parameter and the second instantaneous electric parameter output by the two AC ADC samplers and the electric metering data of the smart electric energy meter to be tested. The error detection module is used to perform the following steps: S10, acquiring the first instantaneous electrical parameter, the second instantaneous electrical parameter and electrical metering data, the first instantaneous electrical parameter comprising a first instantaneous current, a first instantaneous voltage, a first instantaneous frequency, and a first instantaneous power, and the second instantaneous electrical parameter comprising a second instantaneous current, a second instantaneous voltage, a second instantaneous frequency, and a second instantaneous power; S20, generating a first current curve, a first voltage curve, a first frequency curve, a first power curve, a second current curve, a second voltage curve, a second frequency curve, a second power curve, and an electricity metering curve according to the first instantaneous electrical parameter, the second instantaneous electrical parameter, and the electricity metering data; S30, performing multivariate mixed decomposition calculation on the first voltage curve and the second voltage curve to obtain two groups of fundamental wave and harmonic wave data; S40, calculating a voltage stability coefficient matrix and an anti-error factor of an electric energy meter input by using the first frequency curve, the second frequency curve, and the obtained two groups of fundamental and harmonic signals; S50, calculating the basic error range of the smart electric energy meter to be tested by using the first power curve, the second power curve, the voltage stability coefficient matrix and the anti-error factor; S60, using the first current curve, the second current curve and the electricity metering curve to correct the basic error interval, obtain a detection error interval of the smart electric energy meter, and output it; The multivariate mixed decomposition calculation formula is specifically: ； In the formula, is a voltage signal, including a first voltage curve and the second voltage curve ; is the amplitude of the nth harmonic; is the fundamental frequency; is the nth harmonic phase; , , , is the attenuated sinusoidal component parameter; is the preset harmonic order; is the preset number of attenuated sinusoidal components; The voltage stability coefficient matrix is ​​specifically: ； in: ； In the formula, is the i-th harmonic component of the first voltage curve; is the jth harmonic component of the second voltage curve; is the rated voltage; is the observation period, i and j are both positive integers less than N; The anti-error factor is specifically: ; In the formula, is the nth harmonic amplitude of the first voltage curve; is the nth harmonic amplitude of the second voltage curve; The calculation formula of the basic error interval is specifically: ; in: ; ; In the formula, is the first power curve; is the second power curve; is the maximum eigenvalue of the voltage stability coefficient matrix.

2. The intelligent electric energy meter operation error detection and testing device according to claim 1 is characterized in that: The correction calculation formula of the basic error interval is specifically: ; in: ; ; ; In the formula, is the first current curve, is the second current curve.

3. The intelligent electric energy meter operation error detection and testing device according to claim 1 is characterized in that: The control chip is also connected to an analog circuit for performing analog pre-processing on the collected analog electrical parameter signals, including filtering and amplification, and then sending them to an ADC for conversion into digital signals, and finally transmitting them to an error detection module for digital processing.

4. The intelligent electric energy meter operation error detection and testing device according to claim 1 is characterized in that: The sampling frequency of the AC ADC sampler is greater than 2KHz.

5. The intelligent electric energy meter operation error detection and testing device according to claim 1 is characterized in that: Two AC ADC samplers are respectively installed at the electric input end and the electric output end of the smart electric energy meter to be tested.

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