A DC charging pile electric energy metering method, system, device and readable storage medium

Abstract

The present invention discloses a DC charging pile electric energy metering method, system, device and readable storage medium. The method decomposes a charging power signal into a DC signal and an asymetric signal by using empirical wavelet transform and Hilbert transform. The method can effectively realize the effective analysis of short-time and distorted signals, and can decompose the decomposed DC signal and the asymetric signal by K-RV interconvolution window FFT calculation to obtain the harmonic signal of the DC signal and the asymetric signal, so that the charging power signal is effectively decomposed, and then the electric energy of the DC signal, the asymetric signal and their respective harmonic signals are calculated respectively according to the DC signal, the asymetric signal and the harmonic signal. The sum of the electric energy of the DC signal, the asymetric signal and their respective harmonics is the total electric energy, thereby improving the accuracy of DC charging pile electric energy calculation and providing effective support for DC charging pile electric energy metering.

Description

Technical Field

[0001] The present invention relates to the electric energy metering accuracy of a DC charging pile on-site detection device under on-site working conditions, and in particular to a DC charging pile electric energy metering method, system, device and readable storage medium. Background Art

[0002] With the rapid development of new energy electric vehicles, a large number of DC charging piles have been built. However, due to the complex on-site environment of DC charging piles, temperature, humidity, and non-steady-state waves such as ripple and distortion output by the charging piles will have a great impact on the accuracy of electric energy measurement. Traditional electric energy measurement mostly uses the method of discretizing and summing voltage and current signals, and the most commonly used methods are the average value method and the effective value method. However, although these two methods can accurately calculate the electric energy of DC steady-state signals, when the signal contains a large number of harmonics and non-steady-state signals, this method will produce large calculation errors. Therefore, it is of great practical significance to study the precise algorithm for electric energy measurement of DC charging piles. Summary of the invention

[0003] The purpose of the present invention is to provide a DC charging pile electric energy metering method and system to overcome the shortcomings of the prior art. The present invention calculates DC, harmonic, non-steady-state signal electric energy and total electric energy respectively; finally, through example verification, the electric energy metering error can be reduced to within 0.05%, which provides an effective method for accurate calculation of DC charging pile on-site electric energy.

[0004] A method for measuring electric energy of a DC charging pile comprises the following steps:

[0005] S1, decompose the charging power signal into DC signal and non-steady-state signal by using empirical wavelet transform and Hilbert transform;

[0006] S2, decomposing the decomposed DC signal and the non-steady-state signal by K-RV cross-convolution window FFT calculation to decompose the harmonic signal of the DC signal and the non-steady-state signal;

[0007] S3, then according to the DC signal, the non-steady-state signal and the harmonic signal, the electric energy of the DC signal, the non-steady-state signal and their respective harmonic signals are calculated respectively, and the sum of the electric energy of the DC signal, the non-steady-state signal and their respective harmonics is the total electric energy.

[0008] Further, specifically, for the signal to be decomposed f(t), assume that it contains Q modal AM-FM components; perform Fourier transform on the signal to be decomposed f(t), normalize the spectrum to [0,π], obtain the maximum values ​​in the spectrum sequence, and form a maximum value sequence Arrange them in descending order by magnitude.

[0009] Furthermore, the values ​​in the maximum sequence that are greater than the set threshold σ are recorded as the main maximums of the original signal, and the number of main maximums is the mode number Q.

[0010] Furthermore, the frequency interval of 5 Hz is taken as the boundary of the DC signal spectrum, and Q+1 subintervals and Q+2 boundaries are formed in the entire spectrum interval [0,π]. The frequency of two adjacent main maxima is Ω q and Ω q+1 , then the boundary ω of the qth spectrum q for:

[0011]

[0012] Where N is the number of sampling points, ω0=0, ω Q+2 =π, and the DC signal is obtained according to the DC signal spectrum boundary.

[0013] Furthermore, the original signal is subjected to inner product operations with the empirical scaling function and the empirical wavelet function to obtain the approximate coefficients and detail coefficients, according to the approximation coefficients And the detail coefficients reconstruct the original signal:

[0014]

[0015] Decompose f(t) to get each modal component

[0016]

[0017]

[0018] Furthermore, the derivatives of each modal component are

[0019]

[0020] When A>σ1 (σ1 is the threshold), the mode is a harmonic, non-steady-state signal; when A<σ1, the mode is a steady-state DC signal.

[0021] Furthermore, the K-RV interconvolution window is used to perform convolution window correction on the desired signal c(l)

[0022] y(l)=c(l)×w K-RV (l) (19)

[0023] Perform fast Fourier transform on y(l) to get the spectrum function

[0024]

[0025] In the formula, f s is the sampling frequency, Δf=f s / L is the frequency resolution.

[0026] A DC charging pile electric energy metering system, comprising:

[0027] A signal preprocessing module, used to decompose the charging power signal into a DC signal and a non-steady-state signal by using empirical wavelet transform and Hilbert transform;

[0028] A signal decomposition module is used to decompose the decomposed DC signal and the non-steady-state signal into harmonics of the DC signal and the non-steady-state signal by K-RV cross-convolution window FFT calculation;

[0029] The metrology module is used to perform optimization training according to the feature image until the network loss function value tends to be stable to obtain a classification model, and output the corresponding bearing fault result according to the input spindle bearing vibration signal.

[0030] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned DC charging pile electric energy metering method when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned DC charging pile electric energy metering method are implemented.

[0032] Compared with the prior art, the present invention has the following beneficial technical effects:

[0033] The present invention discloses a DC charging pile electric energy metering method. The method adopts empirical wavelet transform and Hilbert transform to decompose a charging power signal into a DC signal and an unstable signal. The method can effectively realize the effective analysis of short-time and distorted signals, and can decompose the decomposed DC signal and the unstable signal into harmonic signals of the DC signal and the unstable signal by K-RV interconvolution window FFT calculation, so that the charging power signal is effectively decomposed, and then the electric energy of the DC signal, the unstable signal and their respective harmonic signals is calculated respectively according to the DC signal, the unstable signal and the harmonic signal. The sum of the electric energy of the DC signal, the unstable signal and their respective harmonics is the total electric energy, thereby improving the accuracy of DC charging pile electric energy calculation and providing effective support for DC charging pile electric energy metering.

[0034] The charging power is effectively decomposed into DC, harmonics and non-steady-state power, and then the charging power is accurately calculated. During the decomposition process, the threshold is set to avoid the influence of noise signals on the maximum value, thereby improving the calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a block diagram of a DC charging pile electric energy metering system in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] According to electrical engineering theory, the field signal is accurately identified and decomposed into DC signal, ripple signal and distortion signal, and then the electric energy is measured for them respectively, which greatly improves the accuracy of electric energy calculation.

[0039] The present invention proposes an electric energy calculation method combining EWT (Empirical Wavelet Transform) and K-RV (Kaiser and Rife-Vincent window function) interconvolution window. First, the signal is modally decomposed by EWT and Hilbert transform to accurately decompose the DC signal and non-steady-state signal; the respective harmonics are accurately decomposed by K-RV interconvolution window FFT (Fast Fourier Transform) calculation, and then the DC, harmonic, non-steady-state signal electric energy and total electric energy are calculated respectively; finally, through example verification, the results show that the present invention can reduce the electric energy measurement error to within 0.05%, providing an effective method for accurate calculation of on-site electric energy of DC charging piles.

[0040] In the DC charging pile electric energy metering method, according to the DC charging pile on-site voltage and current signals:

[0041]

[0042] Where u(t) is the voltage signal, i(t) is the current signal; U dis a DC voltage signal; I d DC current signal; u k (t), i k (t) are the kth harmonic voltage component and kth harmonic current component, k = 1, 2, ...; u s (t), i s (t) is the non-steady-state voltage component and non-steady-state current component.

[0043] Charging power:

[0044]

[0045] Where P d is the DC power, P k is the kth harmonic power, P s Unsteady-state power.

[0046] From equation (2), we can see that the charging power includes DC, harmonics and non-steady-state power. To accurately calculate the charging power, we first need to accurately identify and decompose the DC, harmonics and non-steady-state signals.

[0047] Non-stationary signal recognition based on EWT (Empirical Wavelet Transform):

[0048] Signal spectrum division:

[0049] For the signal f(t) to be decomposed, it is assumed to contain Q modal AM-FM components;

[0050] First, perform Fourier transform on the decomposed signal f(t), normalize the spectrum to [0,π], find the maximum values ​​in the spectrum sequence, and form a maximum sequence Arrange them in descending order by magnitude.

[0051] To avoid the influence of noise signals on the maximum value, the threshold is set as:

[0052] σ=M M +α(M1-M M ) (3)

[0053] Where α∈(0,1) represents the relative amplitude ratio.

[0054] The values ​​greater than σ in the maximum sequence are recorded as the main maximum of the original signal, and the number of main maximums is the mode number Q.

[0055] In order to identify the DC signal, the boundary is set to obtain the corresponding DC signal, and the interval with a frequency of 5 Hz is taken as the boundary of the DC signal spectrum. Q+1 sub-intervals and Q+2 boundaries are formed in the entire spectrum interval [0,π].

[0056] Assume the frequency of two adjacent main maxima is Ω q and Ω q+1 , then the boundary ω of the qth spectrum q for:

[0057]

[0058] Where N is the number of sampling points, ω0=0, ω Q+2 =π.

[0059] Empirical wavelet function and the empirical scaling function Determination of:

[0060] According to Meyer's principle:

[0061]

[0062]

[0063] In the formula,

[0064] Empirical wavelet transform:

[0065] Perform inner product operations on the original signal, the empirical scaling function and the empirical wavelet function to obtain the approximate coefficients And the detail factor

[0066]

[0067]

[0068] In the formula, They are φ1(t), ψ q The complex conjugate function of (t), and

[0069] Signal reconstruction:

[0070] Reconstruct the original signal by equation (8)

[0071]

[0072] Decompose f(t) to get each modal component

[0073]

[0074]

[0075] c0(t),c k (t) are the modal components of f(t).

[0076] Non-stationary signal identification:

[0077] Hilbert Transform:

[0078] Hilbert transform is an effective method for analyzing the instantaneous characteristics of signals:

[0079] For any signal x(t), the Hilbert transform and its inverse transform are:

[0080]

[0081]

[0082] Construct the complex analytic signal Z(t) and modal signal a(t) from x(t) and y(t):

[0083] Z(t)=x(t)+jy(t)=a(t)e jθ(t) (14)

[0084]

[0085] Signal mutation judgment:

[0086] For the modal component a i (t) (i is the modal number of EWT decomposition). When the signal contains distortion or non-steady-state signals, its amplitude will undergo a step change. Therefore, the derivative of each modal component is

[0087]

[0088] When A>σ1 (σ1 is the threshold), the mode is a harmonic, non-steady-state signal; when A<σ1, the mode is a steady-state DC signal.

[0089] Steady-state signal recognition based on improved function window:

[0090] Window function characteristics analysis, using Kaiser window function:

[0091]

[0092] Where β is the adjustable Kaiser window function parameter; I0(β) is the first kind of modified 0th order Bessel function,

[0093] Spectral function of Kaiser window function

[0094]

[0095] It can be seen from formula (18) that by selecting different β, Kasier window functions with different sidelobe characteristics and mainlobe characteristics can be obtained.

[0096] K-RV interconvolution window function FFT algorithm:

[0097] Use the K-RV interconvolution window to perform convolution window correction on the desired signal c(l)

[0098] y(l)=c(l)×w K-RV (l) (19)

[0099] Perform fast Fourier transform on y(l) to get the spectrum function

[0100]

[0101] In the formula, f s is the sampling frequency, Δf=f s / L is the frequency resolution.

[0102] Let the peak point of the signal be k i The maximum spectral line amplitude near this point is k c , k c The left and right spectral lines are k c-1 , k c+1 At this time, the amplitudes of the three spectral lines are x c =|Y(k c Δf)|、x c-1 =|Y(k c-1 Δf)| and x c+1 =|Y(k c+1 Δf|). Let ε = k i -k c ε∈[-0.5,0.5], introducing the parameter

[0103]

[0104] From formula (21) and parameter ε, we get

[0105]

[0106] Amplitude of the signal

[0107]

[0108] Where G(ε) is a polynomial of ε.

[0109] Signal frequency correction

[0110] f i =(k c +ε)Δf (24)

[0111] Initial phase correction

[0112]

[0113] Polynomial fitting is performed on equations (23) and (24) to obtain the expressions of ε and G(ε):

[0114] ε=2.45214255γ-0.16262866γ 3 +0.02257032γ 5 -0.00389398γ 7 (26)

[0115] G(ε)=2.74346286+0.28017218ε 2 +0.01470535ε 4 +0.00053617ε 6 (27)

[0116] Substituting equations (26) and (27) into equations (23) to (25), we can obtain the amplitude, phase and initial phase correction formula of the steady-state signal:

[0117] A i =(2x c +x c-1 +x c+1 )L -1 ×(2.47611497+0.30845615ε 2 +0.0198985ε 4 +0.00090164ε 6 ) (28)

[0118] f i =(k c +2.01233693γ-0.13903832γ 3 +0.01987763γ 5 -0.00349827γ 7 )Δf(29)

[0119]

[0120] For the above-calculated electric energies of the DC signal, the non-steady-state signal and their respective harmonics, the sum of the electric energies of the DC signal, the non-steady-state signal and their respective harmonics is the total electric energy.

[0121] In one embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor adopts a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the DC charging pile electric energy metering method.

[0122] A DC charging pile electric energy metering system, comprising:

[0123] A signal preprocessing module, used to decompose the charging power signal into a DC signal and a non-steady-state signal by using empirical wavelet transform and Hilbert transform;

[0124] A signal decomposition module is used to decompose the decomposed DC signal and the non-steady-state signal into harmonics of the DC signal and the non-steady-state signal by K-RV cross-convolution window FFT calculation;

[0125] The metrology module is used to perform optimization training according to the feature image until the network loss function value tends to be stable to obtain a classification model, and output the corresponding bearing fault result according to the input spindle bearing vibration signal.

[0126] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. The computer-readable storage medium includes a built-in storage medium in the terminal device, which provides a storage space and stores the operating system of the terminal, and may also include an extended storage medium supported by the terminal device. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-volatile memory), such as at least one disk storage. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for measuring electric energy of a DC charging pile in the above embodiment.

[0127] Assume that the voltage and current of the DC charging pile are as shown in formula (1), where

[0128] U d =900V, I d =200A

[0129]

[0130]

[0131]

[0132]

[0133] The electric energy calculation results of the effective value method, the average value method and the present invention are shown in Table 1.

[0134] Table 1 Electric energy calculation results of the conventional method and the present invention (sampling time T = 0.5s)

[0135]

[0136] As shown in Table 1, compared with the theoretical value, the error of the average value method is -0.0982%, the error of the effective value method is 0.0278%, and the error of the present invention is 0.0057%, which is much smaller than the traditional average value method and effective value method. It can be seen that the present invention has a high calculation accuracy and is an effective method for calculating the electric energy of a DC charging pile.

[0137] The present invention performs modal decomposition of the signal through EWT and Hilbert transform, accurately decomposes the DC signal and the non-steady-state signal, and then accurately decomposes the respective harmonics through K-RV interconvolution window FFT (Fast Fourier Transform) calculation; then respectively calculates the DC, harmonic, non-steady-state signal electric energy and the total electric energy; finally, through example verification, the results show that the present invention can reduce the electric energy measurement error to within 0.05%.

Claims

1. A DC charging pile electric energy metering method, characterized in that: The following steps are involved: S1, decompose the charging power signal into DC signal and non-steady-state signal by using empirical wavelet transform and Hilbert transform; S2, decomposing the decomposed DC signal and the non-steady-state signal by K-RV cross-convolution window FFT calculation to decompose the harmonic signal of the DC signal and the non-steady-state signal; S3, then calculating the electric energy of the DC signal, the non-steady-state signal and their respective harmonic signals according to the DC signal, the non-steady-state signal and the harmonic signal, and the sum of the electric energy of the DC signal, the non-steady-state signal and their respective harmonics is the total electric energy; For the signal f(t) to be decomposed, assume that it contains Q modal AM-FM components; perform Fourier transform on the signal f(t) to be decomposed, normalize the spectrum to [0,π], obtain the maximum values ​​in the spectrum sequence, and form a maximum value sequence Arrange them in descending order according to the amplitude; record the value in the maximum value sequence that is greater than the set threshold σ as the main maximum value of the original signal, and the number of main maximum values ​​is the number of modes Q; take the interval with a frequency of 5Hz as the boundary of the DC signal spectrum, and form Q+1 sub-intervals and Q+2 boundaries in the entire spectrum interval [0,π], and set the frequency of two adjacent main maximum values ​​to Ω q and Ω q+1 , then the boundary ω of the qth spectrum q for: Where N is the number of sampling points, ω0=0, ω Q+2 =π, and the DC signal is obtained according to the DC signal spectrum boundary.

2. A DC charging pile electric energy metering method according to claim 1, characterized in that: Perform inner product operations on the original signal, the empirical scaling function and the empirical wavelet function to obtain the approximate coefficients and detail coefficients, according to the approximation coefficients And the detail factor Reconstruct the original signal: Decompose f(t) to get each modal component 3. A DC charging pile electric energy metering method according to claim 2, characterized in that: Derivatives of each modal component When A>σ1, σ1 is the threshold value, and the mode is a harmonic, non-steady-state signal; when A<σ1, the mode is a steady-state DC signal; a i (t) is the modal component, and i is the mode number decomposed by EWT.

4. A DC charging pile electric energy metering method according to claim 1, characterized in that: Use the K-RV interconvolution window to perform convolution window correction on the desired signal c(l) y(l)=c(l)×w K-RV (l) (19) Perform fast Fourier transform on y(l) to get the spectrum function In the formula, f s is the sampling frequency, Δf=f s / L is the frequency resolution.

5. A DC charging pile power metering system based on the DC charging pile power metering method according to claim 1, characterized in that: include: A signal preprocessing module, used to decompose the charging power signal into a DC signal and a non-steady-state signal by using empirical wavelet transform and Hilbert transform; A signal decomposition module is used to decompose the decomposed DC signal and the non-steady-state signal through K-RV cross-convolution window FFT calculation to obtain the harmonics of the DC signal and the non-steady-state signal; The metering module calculates the electric energy of the DC signal, the non-steady-state signal and their respective harmonic signals according to the DC signal, the non-steady-state signal and the harmonic signal, and the sum of the electric energy of the DC signal, the non-steady-state signal and their respective harmonics is the total electric energy.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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