Method and device for monitoring metering error of intelligent electric energy meter by using electromagnetic signal

Abstract

The invention provides a method and device for monitoring the metering error of an intelligent electric energy meter through an electromagnetic signal, and belongs to the field of electric energy meters. The method comprises the following steps: acquiring real-time electromagnetic interference parameters, and acquiring a metering error analysis model corresponding to the intelligent electric energy meter; inputting the real-time electromagnetic interference parameter into a metering error analysis model, and outputting a metering error value of the electric energy meter; when the metering error value of the electric energy meter is greater than a set threshold value corresponding to the metering error analysis model, error early warning information is generated; wherein the metering error analysis model is obtained by training according to test data after performing error test on the intelligent electric energy meter based on the m-sequence dynamic test signal. The influence of the randomness of the electromagnetic signal on the metering error of the intelligent electric energy meter is considered, and the metering error monitoring accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meters, and particularly to a method and device for monitoring the measurement error of an intelligent electric energy meter caused by electromagnetic signals. Background Technique

[0002] With the rapid development of the smart grid and the user information acquisition system, a modern power grid integrating advanced sensor measurement technology, communication technology, and control technology has taken initial shape. However, the sharp increase in the number of intelligent electric energy meters has also brought challenges to the reliability assessment of the electric energy meters. At present, many research results have been obtained on the influence of environmental factors such as temperature and humidity on the measurement of electric energy meters. However, since intelligent electric energy meters all belong to electronic electric energy meters, a large number of electronic components contained therein are extremely vulnerable to the interference of the surrounding complex electromagnetic fields.

[0003] Currently, scholars at home and abroad have conducted a large number of studies on the influence of intelligent electric energy meters in the electromagnetic environment. These studies include the following directions: proposing an automatic verification method for the measurement error of three-phase electric energy meters based on clustering optimization to improve the verification accuracy; combining the case where the electric energy meter frequently restarts automatically in the radio frequency electromagnetic field, and effectively filtering the interference voltage introduced into the loop by the space radio frequency electromagnetic field by connecting a bypass capacitor between the reference pin of the voltage reference source chip and the ground, thus solving the problem of the electric energy meter automatically resetting and restarting; researching the design and improvement of the power frequency electromagnetic field immunity test; using a combination of multiple algorithms to establish a prediction model for the line loss rate of the power distribution area and estimating the error of the electric energy meter; designing a wide-frequency voltage decoupling network to significantly improve the EMS anti-interference ability of the electric energy meter verification device; establishing an on-off keying (OOK) test dynamic current model and an OOK test dynamic load energy model to improve the problem of the dynamic accuracy of signal testing. However, the above research results have not been analyzed from the perspective of the randomness of electromagnetic signals.

[0004] At present, scholars at home and abroad have conducted extensive research on the impact of smart electricity meters in the electromagnetic environment. The research mainly focuses on the following aspects: First, an automatic verification method for the metering error of three-phase electricity meters based on clustering optimization is proposed to improve the verification accuracy; Second, aiming at the problem that the electricity meter frequently restarts automatically in the radio frequency electromagnetic field, by connecting a bypass capacitor in parallel between the reference pin of the voltage reference source chip and the ground, the interference voltage of the spatial radio frequency electromagnetic field on the circuit loop is effectively eliminated, thus solving the problem of the electricity meter automatically resetting and restarting; In addition, the researchers also explored the design and optimization of the power frequency electromagnetic field immunity test; By combining multiple algorithms, a prediction model for the line loss rate of the power distribution area is constructed, and the error of the electricity meter is estimated; A wide-frequency voltage decoupling network is designed, which significantly enhances the electromagnetic compatibility and anti-interference ability of the electricity meter verification device; An on-off keying (OOK) test dynamic current model and an OOK test dynamic load energy model are established to improve the dynamic accuracy of signal testing.

[0005] Due to the variety of electromagnetic interference signals and different modulation methods in the complex electromagnetic environment, the performance of surrounding electronic device systems declines. The above research results have not been deeply analyzed from the perspective of the randomness of electromagnetic signals, and the monitoring accuracy of the metering error of smart electricity meters by electromagnetic signals needs to be improved. Summary of the Invention

[0006] An embodiment of the present invention provides a method and device for monitoring the metering error of a smart electricity meter by electromagnetic signals to solve the problem that the monitoring accuracy of the metering error of a smart electricity meter by electromagnetic signals needs to be improved.

[0007] In a first aspect, an embodiment of the present invention provides a method for monitoring the metering error of a smart electricity meter by electromagnetic signals, including:

[0008] Obtain real-time electromagnetic interference parameters and obtain a metering error analysis model corresponding to the smart electricity meter;

[0009] Input the real-time electromagnetic interference parameters into the metering error analysis model, and output the metering error value of the electricity meter;

[0010] When the metering error value of the electricity meter is greater than the set threshold corresponding to the metering error analysis model, generate an error warning message;

[0011] Wherein, the metering error analysis model is obtained by training according to the test data after testing the error of the smart electricity meter with an m-sequence dynamic test signal.

[0012] In a possible implementation manner, the real-time electromagnetic interference parameters include: the current amplitude, voltage amplitude, and frequency of the external radio frequency signal of the smart electricity meter within a set time period.

[0013] In a possible implementation, the obtaining of the measurement error analysis model corresponding to the smart electricity meter includes:

[0014] Obtain the input parameters of the smart electricity meter, where the input parameters include: the input voltage of the smart electricity meter, the input current of the smart electricity meter, and the input signal frequency of the smart electricity meter;

[0015] Determine the stability of the corresponding input signal according to the input parameters of the smart electricity meter, and determine the measurement error analysis model corresponding to the smart electricity meter according to the input signal stability.

[0016] In a possible implementation, before the obtaining of the real-time electromagnetic interference parameters, the method further includes:

[0017] Obtain a sinusoidal steady-state current signal and a sinusoidal steady-state voltage signal, and generate an m-sequence based on a shift register with linear feedback;

[0018] Modulate the steady-state current signal and the sinusoidal steady-state voltage signal respectively according to the m-sequence to obtain a dynamic test current signal and a dynamic test voltage signal.

[0019] In a possible implementation, before the obtaining of the real-time electromagnetic interference parameters, the method further includes:

[0020] Obtain the input voltage signal of the electricity meter, the input current signal of the electricity meter, the radio frequency current signal, and the radio frequency voltage signal;

[0021] Modulate the radio frequency current signal according to the m-sequence to obtain an interference test current signal;

[0022] Add the interference test current signal and the input current signal of the electricity meter to obtain a measurement error current test signal, and add the radio frequency voltage signal and the input voltage signal of the electricity meter to obtain a measurement error voltage test signal;

[0023] Multiply the measurement error current test signal by the gain coefficient k i , multiply the measurement error voltage test signal by the gain coefficient k u ;

[0024] Discretize and multiply the calculation results respectively to obtain an instantaneous power signal p i (n) containing radio frequency interference;

[0025] Decompose the instantaneous power signal p i (n) containing radio frequency interference into the active power p0(n) of the input signal and the active power p(n) containing radio frequency signals:

[0026] Accumulate the active power respectively to obtain the electrical energy of the corresponding input signal as E0 and the electrical energy containing the radio frequency signal as E;

[0027] Determine the measurement error of the radio frequency electromagnetic signal as:

[0028]

[0029] In a possible implementation manner, the binary waveform function corresponding to the m-sequence is:

[0030]

[0031] where N is 2 l -1, l is the order of the m-sequence; the g(t) function is a window function; T is the period of the sinusoidal steady-state current; k = 1, 2, 3..., N;

[0032] In a possible implementation manner, the sinusoidal steady-state current signal and the sinusoidal steady-state voltage signal are respectively:

[0033]

[0034] where I is the amplitude of the sinusoidal steady-state current; is the phase of the sinusoidal steady-state current; U is the amplitude of the sinusoidal steady-state voltage; is the phase of the sinusoidal steady-state voltage; ω is the angular frequency;

[0035] The dynamic test current signal is:

[0036]

[0037] where I is the maximum amplitude of the modulated current i d (t), and |m(t)| ≤ 1.

[0038] In a possible implementation manner, the mathematical models of the radio frequency current signal and the radio frequency voltage signal are respectively:

[0039] e i (t) = I e cos(ω1t + ψ i ) - I e sin(ω1t + ψ i ) + σ i

[0040] e u (t) = U e cos(ω1t + ψ u ) - U e sin(ω1t + ψ u ) + σ u

[0041] Among them, U e , I e are the amplitudes of the radio frequency voltage signal and the radio frequency current signal respectively; ω1 = 2πf1 is the angular frequency of the radio frequency signal; ψ i and ψ u are the phases of the radio frequency current signal and the radio frequency voltage signal respectively; σ i and σ u are the noise errors of the radio frequency current signal and the radio frequency voltage signal respectively; ψ i , ψ u , σ i and σ u all follow a Gaussian distribution with a mean of 0 and a variance of 1;

[0042] The interference test current signal is:

[0043]

[0044] Among them, N is the period length of the m-sequence, l is the order of the m-sequence, N = 2 l - 1.

[0045] In a possible implementation, the step of discretizing and multiplying the calculation results respectively to obtain the instantaneous power signal p i (n) containing radio frequency interference includes:

[0046] Performing analog-to-digital conversion on the calculation result of multiplying i e (t) by the gain coefficient k i to discretize and obtain i e (n), and discretizing the calculation result of multiplying u e (t) by the gain coefficient k u to obtain u e (n):

[0047]

[0048] Among them, t n is the code element width of the m-sequence, t s is the time interval of the sampling point, β is the zero-crossing radian of the first sampling point, l is the order of the m-sequence;

[0049] Multiplying the discrete signals i e (n) and u e (n) can obtain the instantaneous power signal p i (n) containing radio frequency interference:

[0050] p i (n) = u e (n)i e (n).

[0051] In a second aspect, an embodiment of the present invention provides a monitoring device for the metering error of an intelligent electric energy meter caused by electromagnetic signals, including:

[0052] An acquisition module, configured to acquire real-time electromagnetic interference parameters and acquire a metering error analysis model corresponding to the intelligent electric energy meter;

[0053] An error determination module, configured to input the real-time electromagnetic interference parameters into the metering error analysis model and output a metering error value of the electric energy meter;

[0054] A correction module, configured to correct metering data according to the metering error value of the electric energy meter when the metering error value of the electric energy meter is greater than a set threshold;

[0055] Wherein, the metering error analysis model is obtained by training according to test data after performing error tests on the intelligent electric energy meter based on an m-sequence dynamic test signal.

[0056] The embodiment of the present invention provides a method and device for monitoring the metering error of an intelligent electric energy meter caused by electromagnetic signals. By acquiring real-time electromagnetic interference parameters and acquiring a metering error analysis model corresponding to the intelligent electric energy meter, it can adapt to different electromagnetic interference environments and improve the efficiency of metering error analysis. Inputting the real-time electromagnetic interference parameters into the metering error analysis model, outputting the metering error value of the electric energy meter, and generating an error warning message when the metering error value of the electric energy meter is greater than the set threshold corresponding to the metering error analysis model. Among them, the metering error analysis model is obtained by training according to test data after performing error tests on the intelligent electric energy meter based on an m-sequence dynamic test signal. Since in the actual working environment of the electric energy meter, the regional electromagnetic signals will have abnormal changes in terms of time, frequency domain, energy, etc., and the surrounding electromagnetic signals have characteristics such as randomness and time-variation. Performing error tests on the intelligent electric energy meter based on an m-sequence dynamic test signal can improve the efficiency of metering error analysis. The present invention takes into account the influence of electromagnetic signal randomness on the metering error of the intelligent electric energy meter and improves the accuracy of metering error monitoring. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is a schematic diagram of the interference path of radio frequency signals on the metering of the electric energy meter;

[0059] Figure 2It is the implementation flowchart of the monitoring method for the metering error of smart energy meters by electromagnetic signals provided by the embodiments of the present invention;

[0060] Figure 3 It is the general structure diagram of the m-sequence provided by the embodiments of the present invention;

[0061] Figure 4 It is the schematic diagram of the 4-stage feedback shift register;

[0062] Figure 5 It is the generation schematic diagram of the 4-stage m-sequence;

[0063] Figure 6 It is the m-sequence test current signal model;

[0064] Figure 7 It is the schematic diagram of the influence of m-sequence modulation on the metering error of the energy meter;

[0065] Figure 8 It is the schematic diagram of the influence of the m-sequence level on the metering error of the energy meter;

[0066] Figure 9 It is the schematic diagram of the influence of the input current amplitude on the metering error;

[0067] Figure 10 It is the experimental site diagram;

[0068] Figure 11 It is the three-sigma criterion diagram;

[0069] Figure 12 It is the error analysis diagram of 3 energy meters;

[0070] Figure 13 It is the structural schematic diagram of the monitoring device for the metering error of smart energy meters by electromagnetic signals provided by the embodiments of the present invention. Specific implementation manners

[0071] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0072] Before describing the solution of the present application, the electromagnetic interference coupling mechanism and the influence mechanism on the energy meter are introduced first:

[0073] The three elements of electromagnetic interference include the interference source (harassing source), the coupling path, and the sensitive body. To solve the electromagnetic interference problem, it is necessary to study from the interference source and the propagation path to find corresponding solutions.

[0074] Electromagnetic interference can be divided into radiation mode and conduction mode according to the coupling method. In the near-field case, the radiation coupling may be mainly magnetic-field coupling or mainly electric-field coupling; in the far-field case, it is mainly coupled in the form of electromagnetic waves, and the ratio of the electric field to the magnetic field is fixed.

[0075] Electromagnetic interference can be divided according to spectrum description into: audio noise (0 - 20 kHz), which is mainly generated by capacitors and high-frequency transformers and affects the normal metering and phase-sequence judgment of the electric energy meter; radio-frequency interference (20 kHz - 50 MHz), which affects the normal operation and accuracy of the electric energy meter; and radiation interference (> 50 MHz), which affects the accuracy of the electric energy meter. Most common smart electric energy meters currently use ordinary filters, and the effective filtering range is often less than 20 kHz, and the influence of radio-frequency signals and radiation interference cannot be effectively filtered out. Therefore, radio-frequency signals and radiation interference will cause large errors in the metering of smart electric energy meters.

[0076] The influence of radio-frequency signals on smart electric energy meters is mainly reflected in two aspects: on the one hand, radio-frequency signals can penetrate the outer shell of the electric energy meter and directly enter its internal structure; on the other hand, radio-frequency signals can also enter its internal through each wiring port of the electric energy meter, thereby generating interference effects. The interference path of radio-frequency signals on the metering of the electric energy meter is as Figure 1 shown.

[0077] The electric energy meter obtains voltage signals and current signals through voltage sampling and current sampling, and converts the collected analog signals into digital signals for processing. Since the external analog quantity interface of the high-precision metering chip inside the electric energy meter is directly connected to the wiring of the external PCB circuit board, when these analog quantity signal lines and reference voltage and current signal lines are coupled with high-frequency interference signals, the low-pass filter in the subsequent digital circuit may not be able to effectively perform filtering processing. If the intensity of the interference signal exceeds the noise threshold of the internal circuit of the electric energy meter, it may have a significant impact on the metering accuracy of the electric energy meter.

[0078] In a complex electromagnetic environment, there is a wide variety of electromagnetic interference signals with different modulation methods, which leads to the degradation of the performance of surrounding electronic device systems. With the wide application of smart electricity meters in the field of electricity metering, the impact of complex electromagnetic signals on the metering error of electricity meters has gradually attracted attention. In the research on the impact of electromagnetic signals on the metering error of electricity meters, the problem of random interference of electromagnetic signals remains a challenging problem. In view of the characteristics of randomness and dynamics of complex electromagnetic signals, by analyzing the coupling mechanism of electromagnetic interference and the impact mechanism of electromagnetic signals on electricity meters, starting from the uncertain characteristics in the process of electromagnetic signal propagation, this application first establishes a parameter model of the m-sequence dynamic test signal; secondly, based on the m-sequence dynamic test signal model, a structured measurement model of the smart electricity meter and a mathematical model of the impact of electromagnetic signals on the metering error of the smart electricity meter are established, and then the impact of random interference of electromagnetic signals on the electricity meter is simulated; finally, the metering error of the electricity meter is obtained through simulation and experimental tests, and then the impact of complex electromagnetic signals on the metering error of the electricity meter is analyzed.

[0079] To make the objectives, technical solutions and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the accompanying drawings.

[0080] Figure 2 The following is the implementation flowchart of the monitoring method for the impact of electromagnetic signals on the metering error of smart electricity meters provided by the embodiments of the present invention, which is described in detail as follows:

[0081] S201: Obtain real-time electromagnetic interference parameters and obtain the metering error analysis model corresponding to the smart electricity meter.

[0082] S202: Input the real-time electromagnetic interference parameters into the metering error analysis model and output the metering error value of the electricity meter.

[0083] In a possible implementation manner, the real-time electromagnetic interference parameters include: the current amplitude, voltage amplitude and frequency of the external radio frequency signal of the smart electricity meter within a set time period.

[0084] In the specific implementation process, through signal simulation, it is found that: if the frequency of the signal source remains unchanged, the power metering error will increase with the increase of the signal source power. When the power increases to a certain threshold, the change amplitude of the error will start to decrease. On the contrary, if the signal source power is fixed, the power metering error will gradually increase with the increase of the signal source frequency until it reaches a peak, and then the error will show a downward trend. In addition, under different transmission power conditions, the turning frequency is roughly the same. The specific signal simulation process will be introduced in detail in the following embodiments. Since the influence degree of electromagnetic signals on the power meter metering error varies with working conditions, in order to improve the accuracy of the power meter metering error, the embodiments of this application customize a special metering error analysis model for different types of smart power meters for targeted analysis. This can ensure the adaptability to various working conditions, accurately determine the error of the power meter, and then improve the accuracy of error monitoring.

[0085] Specifically, in a possible implementation manner, the obtaining of the metering error analysis model corresponding to the smart power meter includes:

[0086] Obtain the input parameters of the smart power meter, where the input parameters include: the input voltage of the smart power meter, the input current of the smart power meter, and the input signal frequency of the smart power meter;

[0087] Determine the stability of the corresponding input signal according to the input parameters of the smart power meter, and determine the metering error analysis model corresponding to the smart power meter according to the input signal stability.

[0088] Among them, by integrating the input voltage, input current, and input signal frequency of the smart power meter, the metering error analysis model can be accurately determined to improve the accuracy of power meter error analysis.

[0089] S203, when the power meter metering error value is greater than the set threshold corresponding to the metering error analysis model, generate an error warning message. Among them, the metering error analysis model is obtained by training according to the test data after testing the error of the smart power meter based on the m-sequence dynamic test signal.

[0090] Since in the actual working environment of the power meter, the regional electromagnetic signal will have abnormal changes in terms of time, frequency domain, energy, etc., and its surrounding electromagnetic signals have characteristics such as randomness and time-variation. Therefore, in order to more accurately characterize the radio frequency electromagnetic signal, this application adopts the method of converting the steady-state signal into a dynamic signal to establish an m-sequence dynamic test signal model.

[0091] In this embodiment, by obtaining real-time electromagnetic interference parameters and obtaining a measurement error analysis model corresponding to the intelligent electricity meter, different electromagnetic interference environments can be adapted to improve the efficiency of measurement error analysis. The real-time electromagnetic interference parameters are input into the measurement error analysis model, and the electricity meter measurement error value is output. When the electricity meter measurement error value is greater than the set threshold corresponding to the measurement error analysis model, an error warning message is generated. Among them, the measurement error analysis model is obtained by training according to the test data after performing error tests on the intelligent electricity meter based on the m-sequence dynamic test signal. Since in the actual working environment of the electricity meter, the regional electromagnetic signals will change abnormally in terms of time, frequency domain, energy, etc., and the surrounding electromagnetic signals have characteristics such as randomness and time-variation. Performing error tests on the intelligent electricity meter based on the m-sequence dynamic test signal can improve the efficiency of measurement error analysis. The present invention takes into account the influence of electromagnetic signal randomness on the measurement error of the intelligent electricity meter and improves the accuracy of measurement error monitoring.

[0092] In the foregoing embodiment, it was introduced that during the signal simulation process, a certain correlation was found between the signal source parameters and the error. The initial stage of the signal simulation process is mainly divided into two parts: firstly, constructing a dynamic test signal related to the signal source, and secondly, constructing a radio frequency signal related to the electromagnetic signal.

[0093] In a possible implementation manner, before the obtaining of the real-time electromagnetic interference parameters, the method further includes:

[0094] Obtaining a sinusoidal steady-state current signal and a sinusoidal steady-state voltage signal, and generating an m-sequence based on a shift register with linear feedback;

[0095] Modulating the steady-state current signal and the sinusoidal steady-state voltage signal respectively according to the m-sequence to obtain a dynamic test current signal and a dynamic test voltage signal.

[0096] Among them, the generation principle of the m-sequence is specifically as follows:

[0097] The m-sequence is also called a linear feedback shift register sequence, and its general structure diagram is as Figure 3 shown. It is the sequence with the longest period generated by a shift register with linear feedback.

[0098] Assume that the initial state of the m-sequence is (a0, a1,..., a n-2 , a n-1 ). After one shift of linear feedback, the input of the first stage of the shift register is:

[0099]

[0100] Among them, c i is the feedback coefficient. After k shifts, the input of the first stage is:

[0101]

[0102] Among them, l = n + k - 1, (n, k = 1, 2, 3...). It can be seen that the input of the first stage of the shift register is determined by the feedback logic and the original state of the shift register. Taking a 4-stage linear feedback shift register as an example, as Figure 4 shown, its period is p = 2 4 - 1 = 15, and its characteristic polynomial is a 4th-degree primitive polynomial that can divide (x 15 + 1). First, factorize (x 15 + 1) so that each factor is an irreducible polynomial (a polynomial that cannot be factored into the product of two non-constant polynomials within the domain), and then find f(x) as follows:

[0103] x 15 + 1 = (x + 1)(x 15 + x + 1)(x 4 + x + 1)×(x 4 + x 3 + 1)(x 4 + x 3 + x + 1)

[0104] Among them, there are 3 4th-degree irreducible polynomials, but only two can generate m-sequences. The m-sequence formed by f(x) = x 4 + x + 1 is as Figure 5 shown.

[0105] Its initial state is (a3, a2, a1, a0) = (1, 0, 0, 0). When shifted once, a new input is generated by adding a3 and a0 modulo 2 and placed in register a 3， Register a2 is updated to the original value of a3, a1 is updated to the original value of a2, and a0 is updated to the original value of a1. Therefore, the state at this time becomes (1, 1, 0, 0). After shifting 15 times in this way, it returns to the initial state. From the above shifting method, it can be seen that if the initial state is (0, 0, 0, 0), it remains in the all-zero state after shifting. Therefore, this feedback shift register should avoid the all-zero state.

[0106] Based on the above analysis of the principle of the m-sequence, it is not difficult to see that within one period, it can effectively reflect the random changes of dynamic signals and has strong autocorrelation. However, after exceeding the period length, it will enter the next period cycle. Therefore, by using the pseudo-randomness characteristics of the m-sequence, the radio frequency electromagnetic signal model under a complex electromagnetic environment can be simulated.

[0107] In a possible implementation manner, before obtaining the real-time electromagnetic interference parameters, the method further includes:

[0108] Obtain the input voltage signal of the electric energy meter, the input current signal of the electric energy meter, the radio frequency current signal, and the radio frequency voltage signal;

[0109] Modulate the radio frequency current signal according to the m-sequence to obtain an interference test current signal;

[0110] Add the interference test current signal and the input current signal of the electric energy meter to obtain a measurement error current test signal, and add the radio frequency voltage signal and the input voltage signal of the electric energy meter to obtain a measurement error voltage test signal;

[0111] Multiply the measurement error current test signal by the gain coefficient k i , multiply the measurement error voltage test signal by the gain coefficient k u ;

[0112] Discretize and multiply the calculation results respectively to obtain an instantaneous power signal p i (n) containing radio frequency interference;

[0113] Decompose the instantaneous power signal p i (n) containing radio frequency interference into the active power p0(n) of the input signal and the active power p(n) containing radio frequency signals:

[0114] Accumulate the active power respectively to obtain the electric energy E0 of the corresponding input signal and the electric energy E containing radio frequency signals;

[0115] Determine the measurement error of the radio frequency electromagnetic signal as:

[0116]

[0117] Based on the above m-sequence modulation principle, a steady-state signal can be converted into a dynamic signal, and an m-sequence test current signal model is constructed as Figure 6 shown.

[0118] Modulate the sinusoidal steady-state current signal i0(t) using the m-sequence binary waveform to establish a current signal i1(t) with random dynamic characteristics. Among them, let the sinusoidal steady-state voltage signal and the sinusoidal steady-state current signal be respectively:

[0119]

[0120] Among them, I is the amplitude of the sinusoidal steady-state current; is the phase of the sinusoidal steady-state current; U is the amplitude of the sinusoidal steady-state voltage; is the phase of the sinusoidal steady-state voltage; ω is the angular frequency.

[0121] The binary waveform function m(t) is expressed in the form of multiplying a matrix window function by a numerical m-sequence:

[0122]

[0123] Among them, N is 2 l -1, l is the order of the m-sequence; the g(t) function is the window function, T is the period of the sinusoidal steady-state current, and k = 1, 2, 3,..., N.

[0124] Amplitude modulation can be obtained by multiplying the m(t) function by the sinusoidal steady-state current i0(t):

[0125]

[0126] Among them, I is the maximum amplitude of the modulated current i d (t), and |m(t)| ≤ 1.

[0127] According to the aforementioned m-sequence generation principle, the m(k) sequence can be obtained as:

[0128]

[0129] Substituting it into the above, we can get:

[0130]

[0131] To sum up, the parameter model of the test current signal modulated by the m-sequence can be given by the above formula, where the model parameters of i1(t) are determined by C i (i = 1, 2, 3,..., n), I, U, the value of ω.

[0132] Since radio frequency interference is an approximately uniformly distributed noise, this noise is independent of the input current and voltage of the watt-hour meter, and can be superimposed on the input current and voltage, thereby affecting the metering accuracy of the watt-hour meter [18-19] . Based on this, this application will establish a mathematical model for the influence of radio frequency electromagnetic signals on the metering error of smart watt-hour meters based on m-sequence modulation.

[0133] Establish the input sine signals u0(t) and i0(t) respectively as:

[0134]

[0135] Among them, U0 and I0 respectively represent the amplitudes of the input voltage signal and the input current signal of the watt-hour meter, are the phase differences between the input voltage signal and the input current signal of the watt-hour meter respectively, and ω0 is the angular frequency of the input signal.

[0136] Establish the mathematical models of the unmodulated radio frequency current signal and the radio frequency voltage signal as follows:

[0137] ei i(t) = I e cos(ω1t + ψ i ) - I e sin(ω1t + ψ i ) + σ i

[0138] e u u(t) = U e cos(ω1t + ψ u ) - U e sin(ω1t + ψ u ) + σ u

[0139] Wherein, U e and I e are the amplitudes of the RF voltage signal and the RF current signal respectively; ω1 = 2πf1 is the angular frequency of the RF signal; ψ i and ψ u are the phases of the RF current signal and the RF voltage signal respectively; σ i and σ u are the noise errors of the RF current signal and the RF voltage signal respectively; ψ i , ψ u , σ i and σ u all follow a Gaussian distribution with a mean of 0 and a variance of 1; f1 = 1.84 GHz is the angular frequency of the RF signal.

[0140] To simulate the randomness characteristics of the RF signal in a complex electromagnetic environment, the RF current signal can be obtained as e id (t) after being modulated by the m-sequence:

[0141]

[0142] Wherein, N is the period length of the m-sequence, l is the number of stages of the m-sequence, and N = 2 l - 1.

[0143] Adding the input current and the modulated RF current gives i e (t):

[0144] i e (t) = i0(t) + e id (t)

[0145] Adding the input voltage and the RF voltage gives u e (t):

[0146] u e (t) = u0(t) + e u (t)

[0147] Adding ie (t) is multiplied by the gain coefficient k i , and then an analog-to-digital conversion is performed. After discretization, i e (n) can be obtained. Similarly, multiplying u e (t) by the gain coefficient k u and then performing discretization to obtain u e (n):

[0148]

[0149] where t n is the chip width of the m-sequence, t s is the time interval of the sampling points, β is the zero-crossing radian of the first sampling point, and l is the order of the m-sequence.

[0150] Multiplying the discrete signals i e (n) and u e (n) can obtain the instantaneous power signal p i (n) containing radio frequency interference:

[0151] p i (n) = u e (n)i e (n)

[0152] After passing through a low-pass filter, the instantaneous power signal can effectively filter out high-order harmonics. Suppose the effective sampling response of the active power low-pass filter is {r(n) = 1 / L: 0 ≤ n ≤ L - 1}. The discretized instantaneous active power signal p i (n) can be decomposed into the active power p0(n) of the input signal and the active power p(n) of the signal containing radio frequency electromagnetic interference:

[0153]

[0154] By performing cumulative summation on the active power, the cumulative electric energy can be obtained. Therefore, the electric energy of the input signal is E 0， The electric energy of the signal containing radio frequency electromagnetic signals is E:

[0155]

[0156] The measurement error of the electric energy calculation formula for the signal containing radio frequency electromagnetic signals is:

[0157]

[0158] In order to better obtain the influence result of electromagnetic signals on the metering error of electric energy meters, first, based on the mathematical model of the influence of radio frequency electromagnetic signals on the electric energy metering error of smart electric energy meters established in the foregoing embodiments, a program is written using MATLAB to analyze the magnitude of the error. Secondly, by building a dynamic error test system for electric energy meters, dynamic error experiments are respectively carried out on three different brands of electric energy meters, and the experimental data are analyzed to further determine the influence of the metering error.

[0159] Based on the mathematical model established in the foregoing embodiments, the influence of radio frequency electromagnetic signals on the metering of electric energy meters is studied from the following three aspects.

[0160] (1) When phase noise and amplitude noise exist simultaneously, compare the influence of the radio frequency signal obtained by m-sequence modulation on the metering error of the electric energy meter and the influence of the radio frequency signal not obtained by m-sequence modulation on the metering error of the electric energy meter. The simulation results are as Figure 7 shown.

[0161] (2) When ensuring that phase noise and amplitude noise exist simultaneously, set the order of the m-sequence to increase from 1 to 12, and set 20 simulation experiments for each order. Analyze and compare the scatter plots of the metering errors of the electric energy meters at different orders. The results are as Figure 8 shown.

[0162] (3) When the power factors are 0.5, 0.8, and 1.0 respectively, after m-sequence modulation, analyze and compare the influence of the input current amplitude on the metering error of the electric energy meter. The results are as Figure 9 shown.

[0163] It can be seen from Figure 7 that when ensuring the existence of both phase noise and amplitude noise, the metering error of the mathematical model obtained after m-sequence modulation is about 50% of that in the unmodulated case; it can be seen from Figure 8 that as the order of the m-sequence increases, the change amplitude of the metering error is not obvious. Generally, the metering error is less than 0.3%, and in the worst case, it is 0.79%, meeting the metering standard of the electric energy meter; it can be seen from Figure 9 that as the input current amplitude increases, the metering error of the electric energy meter gradually decreases. When the input current is greater than 5 A, the metering error is lower than 0.3%. When the input current is greater than 15 A, the metering error is lower than 0.1%, and this law exists under different power factors.

[0164] The smart electric meter is divided as shown in Figure 10The 12 interference regions shown. Use a tuned dipole antenna in the electromagnetic field frequency range of 400 MHz to 1 GHz (8 frequency points: 400 MHz, 450 MHz, 500 MHz, 600 MHz, 700 MHz, 800 MHz, 900 MHz, 1 GHz), and use a horn antenna in the high-frequency range of 1.1 GHz to 2.5 GHz (4 frequency points: 1 GHz, 1.5 GHz, 2 GHz, 2.5 GHz). Approximately 20 minutes of testing are required for each frequency point, each position, and each polarization direction. Interfere for 60 s, pause for 20 s, and continue to interfere. 100 tests are carried out for each frequency band.

[0165] Conduct dynamic error tests on the electricity meters of three different manufacturers according to the above experimental design.

[0166] Due to the large amount of data, huge errors are inevitable during the testing process. To reduce the impact of this phenomenon on the experimental conclusions, this application uses the three-sigma criterion to clean the power values of each interference group to ensure the reliability and accuracy of subsequent data analysis. The calculation formulas for the mean and standard deviation are as follows:

[0167]

[0168] Among them, μ is the mean of the measurement errors of each interference group; σ is the standard deviation of the measurement errors of each interference group. Then use the following rules to eliminate outliers from the interference group errors:

[0169]

[0170] After the above processing, from Figure 11 It can be seen that the probability of the numerical distribution within 3σ is as high as 99.73%. Those outside this range can be regarded as outliers and eliminated, which can effectively reduce the impact of gross errors on data analysis.

[0171] According to the measured experimental data, use MATLAB software to analyze the measurement errors of the three different electricity meters respectively, and study the relationship between the signal source power and the dynamic error when the signal source frequencies are 400 MHz, 450 MHz, 500 MHz, 600 MHz, 700 MHz, 800 MHz, 900 MHz, 1000 MHz, 1500 MHz, 2000 MHz, 2500 MHz; and the transmitting source powers are -30 dB, -12 dB, -10 dB, -8 dB, -5 dB. The test change curves of electricity meters A, B, and C are as Figure 12 shown.

[0172] From Figure 12It can be seen that the data analysis results of the three different electric meters are similar. When the frequency of the signal source is constant, the power measurement error increases with the increase of the signal source power. When it increases to a certain extent, the error change range decreases. Since the magnitude of the signal source power mainly depends on the amplitude of the RF signal, it can be considered that when the frequency of the signal source is constant, the power measurement error increases with the increase of the signal source amplitude.

[0173] When the signal source power is constant, the power measurement error will increase slowly with the increase of the signal source frequency. When it reaches a certain extent, it will show a decreasing trend, and the turning frequency is similar under different transmission powers.

[0174] Based on the above simulation process, in the embodiments of the present application, according to the input voltage of the smart electric meter, the input current of the smart electric meter, and the input signal frequency of the smart electric meter, the simulation test data is classified, and the measurement error analysis model is trained based on the classified data set corresponding to different working conditions of the signal source. Among them, the simulation test data includes the current amplitude, voltage amplitude, and frequency of the external RF signal of the smart electric meter. In addition, it also includes the measurement error of the electric meter containing RF electromagnetic signals.

[0175] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0176] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0177] Figure 13 The structural schematic diagram of the monitoring device for the measurement error of the smart electric meter caused by electromagnetic signals provided by the embodiments of the present invention is shown. For the convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows:

[0178] As Figure 13 shown, the monitoring device 13 for the measurement error of the smart electric meter caused by electromagnetic signals includes:

[0179] An acquisition module 1301, configured to acquire real-time electromagnetic interference parameters and acquire a measurement error analysis model corresponding to the smart electric meter;

[0180] An error determination module 1302, configured to input the real-time electromagnetic interference parameters into the measurement error analysis model and output the power meter measurement error value;

[0181] A correction module 1303, configured to correct the measurement data according to the power meter measurement error value when the power meter measurement error value is greater than a set threshold;

[0182] Among them, the measurement error analysis model is obtained by training according to the test data after testing the intelligent electric energy meter based on the m-sequence dynamic test signal.

[0183] In a possible implementation manner, the obtaining module 1301 is specifically configured to obtain the input parameters of the intelligent electric energy meter, where the input parameters include: the input voltage of the intelligent electric energy meter, the input current of the intelligent electric energy meter, and the input signal frequency of the intelligent electric energy meter;

[0184] Determine the stability of the corresponding input signal according to the input parameters of the intelligent electric energy meter, and determine the measurement error analysis model corresponding to the intelligent electric energy meter according to the input signal stability.

[0185] In a possible implementation manner, the device further includes: an analog module, configured to obtain a sinusoidal steady-state current signal and a sinusoidal steady-state voltage signal before obtaining the real-time electromagnetic interference parameter, and generate an m-sequence based on a shift register with linear feedback;

[0186] Modulate the steady-state current signal and the sinusoidal steady-state voltage signal respectively according to the m-sequence to obtain a dynamic test current signal and a dynamic test voltage signal.

[0187] In a possible implementation manner, the device further includes: an analog module, configured to obtain the input voltage signal of the electric energy meter, the input current signal of the electric energy meter, the radio frequency current signal, and the radio frequency voltage signal before obtaining the real-time electromagnetic interference parameter;

[0188] Modulate the radio frequency current signal according to the m-sequence to obtain an interference test current signal;

[0189] Add the interference test current signal and the input current signal of the electric energy meter to obtain a measurement error current test signal, and add the radio frequency voltage signal and the input voltage signal of the electric energy meter to obtain a measurement error voltage test signal;

[0190] Multiply the measurement error current test signal by the gain coefficient k i , multiply the measurement error voltage test signal by the gain coefficient k u ;

[0191] Discretize and multiply the calculation results respectively to obtain an instantaneous power signal p i (n) containing radio frequency interference;

[0192] Decompose the instantaneous power signal p i (n) containing radio frequency interference into the active power p0(n) of the input signal and the active power p(n) containing radio frequency signals:

[0193] Accumulate the active power respectively to obtain the electrical energy of the corresponding input signal as E0 and the electrical energy containing the radio frequency signal as E;

[0194] Determine the measurement error of the radio frequency electromagnetic signal as:

[0195]

[0196] In a possible implementation manner, the binary waveform function corresponding to the m-sequence is:

[0197]

[0198] where N is 2 l -1, l is the order of the m-sequence; the g(t) function is a window function; T is the period of the sinusoidal steady-state current; k = 1, 2, 3,..., N;

[0199] In this embodiment, by obtaining real-time electromagnetic interference parameters and obtaining a measurement error analysis model corresponding to the smart energy meter, it can adapt to different electromagnetic interference environments and improve the measurement error analysis efficiency. Input the real-time electromagnetic interference parameters into the measurement error analysis model, and output the measurement error value of the energy meter. When the measurement error value of the energy meter is greater than the set threshold corresponding to the measurement error analysis model, an error warning message is generated. Among them, the measurement error analysis model is obtained by training according to the test data after performing error tests on the smart energy meter based on the m-sequence dynamic test signal. Since in the actual working environment of the energy meter, the regional electromagnetic signal will undergo abnormal changes in terms of time, frequency domain, energy, etc., and the surrounding electromagnetic signals have characteristics such as randomness and time-variation, performing error tests on the smart energy meter based on the m-sequence dynamic test signal can improve the measurement error analysis efficiency. The present invention takes into account the influence of electromagnetic signal randomness on the measurement error of the smart energy meter and improves the measurement error monitoring accuracy.

[0200] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0201] Those of ordinary skill in the art can realize that, in combination with the templates, units, and algorithm steps of the examples described in the embodiments disclosed in the present application, they can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraint conditions of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0202] If the above-mentioned module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned embodiments of the method for monitoring the measurement error of an intelligent electric energy meter by various electromagnetic signals can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0203] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A monitoring method for the metering error of an intelligent electricity meter by electromagnetic signals, characterized in that, Including: Obtain real-time electromagnetic interference parameters, and obtain a measurement error analysis model corresponding to the smart energy meter; Input the real-time electromagnetic interference parameters into the measurement error analysis model, and output the measurement error value of the energy meter; When the measurement error value of the energy meter is greater than the set threshold corresponding to the measurement error analysis model, generate an error warning message; Wherein, the measurement error analysis model is obtained by training according to test data after performing error tests on the smart energy meter based on the m-sequence dynamic test signal.

2. The monitoring method for the metering error of an intelligent electric energy meter by an electromagnetic signal according to claim 1, characterized in that, The real-time electromagnetic interference parameters include: the current amplitude, voltage amplitude, and frequency of the external radio frequency signal of the smart energy meter within a set time period.

3. The monitoring method for the metering error of an intelligent electricity meter by electromagnetic signals according to claim 1, characterized in that The obtaining of the measurement error analysis model corresponding to the smart energy meter includes: Obtain the input parameters of the smart energy meter, wherein the input parameters include: the input voltage of the smart energy meter, the input current of the smart energy meter, and the input signal frequency of the smart energy meter; Determine the stability of the corresponding input signal according to the input parameters of the smart energy meter, and determine the measurement error analysis model corresponding to the smart energy meter according to the input signal stability.

4. The method for monitoring the metering error of an intelligent electricity meter by an electromagnetic signal according to claim 1, characterized in that Before obtaining the real-time electromagnetic interference parameters, the method further includes: Obtain a sinusoidal steady-state current signal and a sinusoidal steady-state voltage signal, and generate an m-sequence based on a shift register with linear feedback; Modulate the steady-state current signal and the sinusoidal steady-state voltage signal respectively according to the m-sequence to obtain a dynamic test current signal and a dynamic test voltage signal.

5. The method for monitoring the metering error of an intelligent electric energy meter by an electromagnetic signal according to claim 4, characterized in that Before obtaining the real-time electromagnetic interference parameters, the method further includes: Obtain the input voltage signal of the energy meter, the input current signal of the energy meter, the radio frequency current signal, and the radio frequency voltage signal; Modulate the radio frequency current signal according to the m-sequence to obtain an interference test current signal; Add the interference test current signal and the input current signal of the energy meter to obtain a measurement error current test signal, and add the radio frequency voltage signal and the input voltage signal of the energy meter to obtain a measurement error voltage test signal; Multiply the metering error current test signal by the gain coefficient k i , multiply the metering error voltage test signal by the gain coefficient k u ; Discretize the calculation results separately and multiply them to obtain the instantaneous power signal p with radio frequency interference i (n); Decompose the instantaneous power signal p i (n) containing radio frequency interference into the active power p0(n) of the input signal and the active power p(n) containing radio frequency signals: Accumulate the active power respectively to obtain the electrical energy of the corresponding input signal as E0 and the electrical energy containing the radio frequency signal as E; Determine the measurement error of the radio frequency electromagnetic signal as:

6. The monitoring method for the metering error of an intelligent electricity meter by an electromagnetic signal according to claim 5, characterized in that, The binary waveform function corresponding to the m-sequence is: Among them, N is 2 l -1, l is the order of the m-sequence; the g(t) function is the window function; T is the period of the sinusoidal steady-state current; k = 1, 2, 3..., N; 7. The method for monitoring the metering error of an intelligent electricity meter by an electromagnetic signal according to claim 5, characterized in that, The sinusoidal steady-state current signal and the sinusoidal steady-state voltage signal are respectively: where I is the amplitude of the sinusoidal steady-state current; is the phase of the sinusoidal steady-state current; U is the amplitude of the sinusoidal steady-state voltage; is the phase of the sinusoidal steady-state voltage; ω is the angular frequency; The dynamic test current signal is: where I is the maximum amplitude of the modulated current i d (t), and |m(t)| ≤ 1.

8. The method for monitoring the measurement error of a smart energy meter by an electromagnetic signal according to claim 5, characterized in that The mathematical models of the radio frequency current signal and the radio frequency voltage signal are respectively: e i (t) = I e cos(ω1t + ψ i ) - I e sin(ω1t + ψ i ) + σ i e u (t) = U e cos(ω1t + ψ u ) - U e sin(ω1t + ψ u ) + σ u Among them, U e and I e are the amplitudes of the radio frequency voltage signal and the radio frequency current signal respectively; ω1 = 2πf1 is the angular frequency of the radio frequency signal; ψ i and ψ u are the phases of the radio frequency current signal and the radio frequency voltage signal respectively; σ i and σ u are the noise errors of the radio frequency current signal and the radio frequency voltage signal respectively; ψ i 、ψ u 、σ i and σ u all follow a Gaussian distribution with a mean of 0 and a variance of 1; The interference test current signal is: Among them, N is the period length of the m-sequence, l is the order of the m-sequence, and N = 2 l - 1.

9. The method for monitoring the metering error of an intelligent electricity meter by an electromagnetic signal according to claim 8, wherein The calculation results are discretized and multiplied respectively to obtain an instantaneous power signal p i (n) containing radio frequency interference Including: Multiply i e (t) by the gain coefficient k i Perform modulo conversion on the calculation result, and discretize it to obtain i e (n). Discretize the calculation result of multiplying u e (t) by the gain coefficient k u to obtain u e (n): where t n is the chip width of the m-sequence, t s is the time interval of the sampling points, β is the zero-crossing radian of the first sampling point, l is the order of the m-sequence; Discrete signal i e (n), u e Multiplying (n) and u i (n) can obtain the instantaneous power signal p containing radio frequency interference: p i (n) = u e (n)i e (n).

10. A monitoring device for the metering error of an intelligent electric energy meter caused by electromagnetic signals, characterized in that, Including: An acquisition module, configured to acquire real-time electromagnetic interference parameters and acquire a measurement error analysis model corresponding to the smart energy meter; An error determination module, configured to input the real-time electromagnetic interference parameters into the measurement error analysis model and output the measurement error value of the energy meter; A correction module, configured to perform measurement data correction according to the measurement error value of the energy meter when the measurement error value of the energy meter is greater than the set threshold; Wherein, the measurement error analysis model is obtained by training according to test data after performing error tests on the smart energy meter based on the m-sequence dynamic test signal.

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