A DC electric energy metering noise reduction method, device, storage medium and system

Abstract

The present invention discloses a method, device, storage medium and system for DC electric energy metering noise reduction. The method calculates the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, realizes adaptive variational modal decomposition of the original sampling data sequence of DC voltage and the original sampling data sequence, and realizes reliable distinction and screening of the effective component and noise component of each decomposition mode by calculating the maximum mutual information coefficient between each decomposition modal sequence and the original sampling data sequence. The de-noised DC voltage sequence and the de-noised DC current sequence are reconstructed based on the effective components obtained by screening. The noise reduction method, device, storage medium and system improve the measurement accuracy of DC electric energy.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current (DC) electric energy metering noise reduction, and in particular to a method, device, computer-readable storage medium, and system for DC electric energy metering noise reduction. Background Art

[0002] Electric vehicles, a typical distributed new energy source, have seen rapid growth in recent years. Electric vehicle charging stations are categorized into AC and DC charging stations. DC charging stations offer advantages such as high charging power and fast charging speeds, meeting users' needs for fast charging. DC charging stations have built-in rectifier modules that convert AC power from the grid into DC. Currently, three common DC energy measurement algorithms exist: the average value method, the effective value method, and the time-domain integration method. The average value method calculates energy values based on the average value of the sampled voltage and current over a period of time, multiplied by the time length. The effective value method calculates energy values based on the effective value of the sampled voltage and current over a period of time, multiplied by the time length. The time-domain integration method, also known as the instantaneous power integration method, uses a sufficiently small sampling interval to calculate the instantaneous power value multiple times within a unit sampling period, integrating the energy value in the time domain. Ideally, the three methods provide accurate and consistent results. However, in reality, due to the significant nonlinear characteristics of power electronic components in DC charging stations, the voltage and current signals at the output of the DC charging station contain significant ripple, i.e., high-order AC harmonic components. In this situation, the average and effective value methods will suffer from measurement deviations. The greater the ripple content, the greater the measurement error, leading to inaccurate measurement and compromising the fairness and rationality of electricity billing. The time-domain integration method can reduce the impact of ripple on measurement by increasing the sampling frequency. However, the computational performance of the metering chip directly limits the increase in sampling frequency. Therefore, high-quality noise reduction of the output DC voltage and current signals is crucial. Although the rectifier module of a DC charging pile includes a filter, which can reduce the ripple content of the output signal to a certain extent at the hardware level, the size of the inductor and capacitor in the filter circuit affects the filtering effect. When the inductor and capacitor are configured small, the filtering effect is poor, resulting in high ripple content in the output signal, affecting measurement accuracy. When the inductor and capacitor are configured large, the filtering effect is good, but it can cause output signal distortion, affecting measurement accuracy. Therefore, digital filtering has emerged.

[0003] In the prior art, digital filtering is usually performed using classical filtering methods such as median filtering, limit filtering, and sliding average filtering.

[0004] However, the existing technology still has the following defects: However, the median filter method is not effective in processing rapidly changing DC voltage signals; the limiting filter method is not effective in suppressing periodic interference; and the sliding average method is not effective in suppressing sporadic interference. Compared with classical filtering methods, the overall performance of modern filtering methods has been significantly improved, including wavelet denoising, wavelet threshold denoising, classical modal decomposition, variational modal decomposition, etc. Among them, the wavelet threshold denoising method requires the pre-setting of parameters such as the threshold function, wavelet basis, and number of decomposition layers. The denoising effect varies greatly depending on the function selection and parameter setting; the classical modal decomposition method is prone to modal aliasing problems; the variational modal decomposition method is an improved method of the classical modal decomposition method, which can overcome the modal aliasing problem in the classical modal decomposition method, has obvious advantages in processing nonlinear and non-stationary signals, and has high computational efficiency. However, this method also requires the pre-setting of two parameters: the quadratic penalty factor and the modal decomposition number.

[0005] Therefore, there is a need for a direct current power metering noise reduction method, device, computer-readable storage medium, and system to overcome the above-mentioned defects in the prior art. Summary of the Invention

[0006] Embodiments of the present invention provide a method, device, computer-readable storage medium, and system for reducing noise in direct current (DC) power metering, thereby improving the accuracy of DC power metering.

[0007] An embodiment of the present invention provides a DC electric energy metering noise reduction method, which includes: collecting a DC voltage original sampling data sequence and a current original sampling data sequence of a DC device to be measured; performing adaptive variational modal decomposition and iterative optimization on the DC voltage original sampling data sequence and the current original sampling data sequence according to a preset local signal envelope entropy calculation formula and a preset improved harmony search intelligent optimization algorithm to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence; obtaining a DC voltage effective component and a DC current effective component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold; and reconstructing a denoised DC voltage sequence and a denoised DC current sequence according to the DC voltage effective component and the DC current effective component, respectively.

[0008] As an improvement to the above-mentioned scheme, according to a preset local signal envelope entropy calculation formula and a preset improved harmonic search intelligent optimization algorithm, adaptive variational modal decomposition and iterative optimization are performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically including: establishing a variational modal decomposition optimization model according to a preset local signal envelope entropy calculation formula and a preset model constraint condition group; iteratively optimizing a preset initial decomposition parameter group according to a preset improved harmonic search intelligent optimization algorithm and the variational modal decomposition optimization model until an optimal decomposition parameter group is output; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; according to the optimal decomposition parameter group, adaptive variational modal decomposition is performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence.

[0009] As an improvement to the above scheme, according to a preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, a preset initial decomposition parameter group is iteratively optimized until an optimal decomposition parameter group is output, specifically comprising: obtaining preset algorithm parameter values, and establishing an initial harmony memory bank according to the memory bank capacity and the variational modal decomposition optimization model; the initial harmony memory bank includes a number of first harmonics corresponding to the memory bank capacity; each group of first harmonics includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include the memory bank capacity, the memory bank consideration probability, the dynamic adjustment upper limit of the adjustment probability, the dynamic adjustment lower limit of the adjustment probability, the dynamic adjustment upper limit of the bandwidth, and the dynamic adjustment lower limit of the bandwidth; according to a preset dynamic adjustment strategy, the variational modal decomposition optimization model, the memory bank consideration probability, the dynamic adjustment upper limit of the adjustment probability, the dynamic adjustment lower limit of the adjustment probability, the dynamic adjustment upper limit of the bandwidth, and the dynamic adjustment lower limit of the bandwidth, the first harmonics in the initial harmony memory bank are iteratively optimized, and it is determined whether a preset convergence condition group is met; when the preset convergence condition group is met, the corresponding quadratic penalty factor and modal decomposition number are output as the optimal decomposition parameter group.

[0010] As an improvement to the above solution, adaptive variational modal decomposition is performed on the DC voltage raw sampling data sequence and the current raw sampling data sequence according to the optimal decomposition parameter group to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically comprising: obtaining the optimal decomposition parameter group; and iteratively calculating the Fourier transform sequence of each modal component in the DC voltage raw sampling data sequence and the Fourier transform sequence of each modal component in the current raw sampling data sequence according to the optimal decomposition parameter group, a preset Fourier transform sequence iterative calculation formula, a center frequency iterative calculation formula, the DC voltage raw sampling data sequence and the current raw sampling data sequence, using a first operator Fourier transform sequence as an iterative parameter, until a preset iterative convergence condition is met, and outputting the DC voltage decomposition modal sequence and the current decomposition modal sequence.

[0011] As an improvement to the above scheme, according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold, a DC voltage effective component and a DC current effective component are obtained, specifically comprising: according to the preset maximum mutual information coefficient calculation formula, calculating the voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and the current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence; according to the voltage maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold, filtering out the DC voltage effective component from the DC voltage decomposition modal sequence; according to the current maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold, filtering out the DC current effective component from the current decomposition modal sequence.

[0012] As an improvement to the above scheme, a denoised DC voltage sequence and a denoised DC current sequence are reconstructed according to the effective components of the DC voltage and the effective components of the DC current, respectively. Specifically, the denoised DC voltage sequence is obtained by superimposing all the effective components of the DC voltage, and the denoised DC current sequence is obtained by superimposing all the effective components of the DC current.

[0013] As an improvement to the above solution, the local signal envelope entropy calculation formula is specifically: Where, E min is the local signal envelope entropy, E i is the signal envelope entropy value of the i-th modal component after the variational modal decomposition of the original sampling data sequence of DC voltage / current at the output end; p i,t is the proportional coefficient of the t-th data point in the sequence after the Hilbert transform of the i-th modal component; T is the total number of points in the new sequence formed after the Hilbert transform of each modal component; K is the modal decomposition number.

[0014] As an improvement to the above solution, the maximum mutual information coefficient calculation formula is specifically: Where N is the number of sample points of each decomposed mode sequence and the original sampling data sequence; p(x i ,o) is the joint probability density of the i-th decomposition mode sequence and the original sampling data sequence; p(x i ) and p(o) are the marginal probability densities of the ith decomposition mode sequence and the original sampling data sequence respectively; a and b are the number of rows and columns of the grid respectively.

[0015] Another embodiment of the present invention provides a DC electric energy metering noise reduction device, which includes a sequence acquisition unit, a modal decomposition unit, an electric energy denoising unit, and a sequence reconstruction unit, wherein the sequence acquisition unit is used to acquire a DC voltage original sampling data sequence and a current original sampling data sequence of the DC device to be measured; the modal decomposition unit is used to perform adaptive variational modal decomposition and iterative optimization on the DC voltage original sampling data sequence and the current original sampling data sequence according to a preset local signal envelope entropy calculation formula and a preset improved harmony search intelligent optimization algorithm to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence; the electric energy denoising unit is used to screen and obtain a DC voltage effective component and a DC current effective component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold; the sequence reconstruction unit is used to reconstruct a denoised DC voltage sequence and a denoised DC current sequence according to the DC voltage effective component and the DC current effective component, respectively.

[0016] As an improvement to the above-mentioned scheme, the modal decomposition unit is further used to: establish a variational modal decomposition optimization model based on a preset local signal envelope entropy calculation formula and a preset model constraint condition group; iteratively optimize the preset initial decomposition parameter group according to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model until the optimal decomposition parameter group is output; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; based on the optimal decomposition parameter group, perform adaptive variational modal decomposition on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence.

[0017] As an improvement to the above scheme, the modal decomposition unit is also used to: obtain preset algorithm parameter values, and establish an initial harmonic memory bank based on the memory bank capacity and the variational modal decomposition optimization model; the initial harmonic memory bank includes a number of first harmonies corresponding to the memory bank capacity; each group of first harmonies includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include memory bank capacity, memory bank consideration probability, dynamic adjustment upper limit of adjustment probability, dynamic adjustment lower limit of adjustment probability, dynamic adjustment upper limit of bandwidth, and dynamic adjustment lower limit of bandwidth; according to the preset dynamic adjustment strategy, the variational modal decomposition optimization model, memory bank consideration probability, dynamic adjustment upper limit of adjustment probability, dynamic adjustment lower limit of adjustment probability, dynamic adjustment upper limit of bandwidth, and dynamic adjustment lower limit of bandwidth, the first harmonics in the initial harmonic memory bank are iteratively optimized, and it is determined whether the preset convergence condition group is met; when the preset convergence condition group is met, the corresponding quadratic penalty factor and modal decomposition number are output as the optimal decomposition parameter group.

[0018] As an improvement to the above-mentioned solution, the modal decomposition unit is further used to: obtain the optimal decomposition parameter group; based on the optimal decomposition parameter group, a preset Fourier transform sequence iterative calculation formula, a center frequency iterative calculation formula, the DC voltage original sampling data sequence, and the current original sampling data sequence, with a first operator Fourier transform sequence as an iterative parameter, perform iterative calculation on the Fourier transform sequence of each modal component in the DC voltage original sampling data sequence and the Fourier transform sequence of each modal component in the current original sampling data sequence until a preset iterative convergence condition is met, and output the DC voltage decomposition modal sequence and the current decomposition modal sequence.

[0019] As an improvement to the above-mentioned scheme, the electric energy denoising unit is further used to: calculate the voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and the current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence according to a preset maximum mutual information coefficient calculation formula; filter out the DC voltage effective component from the DC voltage decomposition modal sequence according to the voltage maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold, and filter out the DC current effective component from the current decomposition modal sequence according to the current maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold.

[0020] As an improvement of the above solution, the sequence reconstruction unit is further used to: superimpose all effective components of DC voltage to obtain a de-noised DC voltage sequence, and superimpose all effective components of DC current to obtain a de-noised DC current sequence.

[0021] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program is executed, the device containing the computer-readable storage medium is controlled to execute the DC power metering noise reduction method as described above.

[0022] Another embodiment of the present invention provides a DC power metering noise reduction system, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the DC power metering noise reduction method as described above is implemented.

[0023] Compared with the existing technology, this technical solution has the following beneficial effects:

[0024] The present invention provides a method, device, computer-readable storage medium and system for DC electric energy metering noise reduction. The method calculates the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, realizes adaptive variational modal decomposition of the original sampling data sequence of DC voltage and the original sampling data sequence, and realizes reliable distinction and screening of the effective component and noise component of each decomposition mode by calculating the maximum mutual information coefficient between each decomposition modal sequence and the original sampling data sequence. The de-noised DC voltage sequence and the de-noised DC current sequence are reconstructed based on the effective components obtained by screening. The noise reduction method, device, computer-readable storage medium and system improve the metering accuracy of DC electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a DC power metering noise reduction method provided by one embodiment of the present invention;

[0026] Figure 2 The figure is a schematic structural diagram of a DC power metering noise reduction device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Specific embodiment 1

[0029] The embodiment of the present invention first describes a method for reducing noise in direct current electric energy measurement. Figure 1 The present invention provides a flow chart of a method for reducing noise in DC power metering.

[0030] like Figure 1 As shown, the DC electric energy metering noise reduction method includes:

[0031] S1: Collect the original sampling data sequence of DC voltage and current of the DC device to be measured.

[0032] S2: According to the preset local signal envelope entropy calculation formula and the preset improved harmony search intelligent optimization algorithm, adaptive variational modal decomposition and iterative optimization are performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain the DC voltage decomposition modal sequence and the current decomposition modal sequence.

[0033] In one embodiment, according to a preset local signal envelope entropy calculation formula and a preset improved harmonic search intelligent optimization algorithm, adaptive variational modal decomposition and iterative optimization are performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically including: establishing a variational modal decomposition optimization model according to a preset local signal envelope entropy calculation formula and a preset model constraint condition group; iteratively optimizing a preset initial decomposition parameter group according to a preset improved harmonic search intelligent optimization algorithm and the variational modal decomposition optimization model until an optimal decomposition parameter group is output; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; according to the optimal decomposition parameter group, adaptive variational modal decomposition is performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence.

[0034] In specific applications, we must first establish the objective function of the model and the model constraint condition group (the quadratic penalty factor and the modal decomposition number in the model are the variables to be optimized). The minimum value of the signal envelope entropy of all modal components after the variational modal decomposition of the output DC voltage / current original sampling data sequence is defined as the local signal envelope entropy. The objective function of the model is to minimize the local signal envelope entropy of the variational modal decomposition of the output DC voltage / current original sampling data sequence, as shown in the following formula:

[0035]

[0036]

[0037]

[0038]

[0039] Where, E min is the local signal envelope entropy, x i(τ) is the i-th modal component sequence; E i is the signal envelope entropy value of the i-th modal component after the variational modal decomposition of the original sampling data sequence of DC voltage / current at the output end; p i,t is the proportional coefficient of the tth data point in the new sequence after the Hilbert transform of the i-th modal component; y i (t) is the new sequence of the i-th modal component after Hilbert transform; T is the total number of points in the new sequence formed by each modal component after Hilbert transform; K is the modal decomposition number.

[0040] In one embodiment, the model constraint condition group is specifically:

[0041] K min ≤K≤K max ,K∈N +；

[0042] α min ≤α<α max ,α∈R + ;

[0043] In the formula, α and K are the two variables to be optimized, namely the quadratic penalty factor and the modal decomposition number; K max , K min are the upper and lower limits of the modal decomposition number respectively; α max , α min are the upper and lower limits of the quadratic penalty factor respectively; N + is a set of positive integers; R + is the set of positive real numbers.

[0044] In one embodiment, adaptive variational modal decomposition is performed on the DC voltage raw sampling data sequence and the current raw sampling data sequence according to the optimal decomposition parameter group to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically comprising: obtaining the optimal decomposition parameter group; iteratively calculating the Fourier transform sequence of each modal component in the DC voltage raw sampling data sequence and the Fourier transform sequence of each modal component in the current raw sampling data sequence according to the optimal decomposition parameter group, a preset Fourier transform sequence iterative calculation formula, a center frequency iterative calculation formula, the DC voltage raw sampling data sequence and the current raw sampling data sequence, using a first operator Fourier transform sequence as an iterative parameter, until a preset iterative convergence condition is met, and outputting the DC voltage decomposition modal sequence and the current decomposition modal sequence.

[0045] In practical applications, the preset initial decomposition parameter group is first obtained, which includes but is not limited to: the number of iterations n, the quadratic penalty factor α, the modal decomposition number K, the Fourier transform sequence of each modal component in the first round of iteration The center frequency of each modal component in the first iteration The Fourier transform sequence of the Lagrange multiplication operator in the first iteration Then, let n = n + 1, and iteratively calculate the Fourier transform sequence of each modal component according to the preset Fourier transform sequence iterative calculation formula and the center frequency iterative calculation formula Center frequency Finally, let k = k + 1, and iterate the previous step until k = K, that is, complete the iterative calculation of the Fourier transform sequence and the center frequency of all modal components in the nth round, and judge whether the iterative calculation results of the current round meet the preset iterative convergence conditions. If they meet, the iteration ends and the first DC voltage decomposition modal sequence and the first current decomposition modal sequence are output; otherwise, the Fourier transform sequence of the Lagrange multiplication operator is updated, and the aforementioned iterative calculation steps are iteratively performed.

[0046] In one embodiment, the Fourier transform sequence iterative calculation formula is specifically:

[0047]

[0048] Where, Calculate the Fourier transform sequence of the kth modal component for the (n-1)th and nth rounds of iterations; is the Fourier transform sequence of the original sampling data sequence of the DC voltage / current at the output end; Calculate the Fourier transform sequence of the Lagrange multiplication operator for the (n-1)th iteration (described herein as the "first operator Fourier transform sequence"); Calculate the center frequency of the kth modal component for the (n-1)th and nth iterations.

[0049] In one embodiment, the center frequency iterative calculation formula is specifically:

[0050]

[0051] Where, Calculate the Fourier transform sequence of the kth modal component for the nth iteration; Calculate the center frequency of the kth modal component for the (n-1)th and nth iterations.

[0052] In one embodiment, the calculation formula of the first operator Fourier transform sequence is specifically:

[0053]

[0054] Where ε is the iterative convergence accuracy; ||g||2 is the two-norm; β is the fidelity parameter.

[0055] In one embodiment, the iterative convergence condition is:

[0056]

[0057] Where, is the Fourier transform sequence of the kth modal component calculated in the (n-1)th and nth rounds of iteration; ε is the iterative convergence accuracy.

[0058] In one embodiment, according to a preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, a preset initial decomposition parameter group is iteratively optimized until an optimal decomposition parameter group is output. Specifically, the optimization method includes: obtaining preset algorithm parameter values, and establishing an initial harmony memory bank according to a memory bank capacity and the variational modal decomposition optimization model; the initial harmony memory bank includes a number of first harmonics corresponding to the memory bank capacity; each group of first harmonics includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include a memory bank capacity, a memory bank consideration probability, a dynamic adjustment upper limit of an adjustment probability, a dynamic adjustment lower limit of an adjustment probability, a dynamic adjustment upper limit of a bandwidth, and a dynamic adjustment lower limit of a bandwidth; according to a preset dynamic adjustment strategy, the variational modal decomposition optimization model, the memory bank consideration probability, the dynamic adjustment upper limit of an adjustment probability, the dynamic adjustment lower limit of an adjustment probability, the dynamic adjustment upper limit of a bandwidth, and the dynamic adjustment lower limit of a bandwidth, iteratively optimizing the first harmonics in the initial harmony memory bank, and determining whether a preset convergence condition group is satisfied; when the preset convergence condition group is satisfied, outputting the corresponding quadratic penalty factor and modal decomposition number as the optimal decomposition parameter group.

[0059] Specifically, step 1 is to obtain the preset algorithm parameter values, including the harmony memory sample capacity HM, the harmony memory consideration probability HMCR, the pitch adjustment probability dynamic adjustment upper limit PAR max , dynamic adjustment lower limit value PAR of pitch adjustment probability min , dynamic adjustment upper limit value bw of tone adjustment bandwidth max And the lower limit value bw of the dynamic adjustment bandwidth of the tone adjustment min and other algorithm-related parameter values; Step 2, establish an initial harmony memory library with a sample capacity of HM, each group of harmony in the initial harmony memory library includes an initial decomposition parameter group and its corresponding local signal envelope entropy value, randomly extract and generate an initial decomposition parameter group within the preset quadratic penalty factor and modal decomposition number variable value range, and obtain each decomposition mode through variational modal decomposition, and calculate the local envelope entropy according to the local envelope entropy calculation formula, wherein the preset quadratic penalty factor variable value range is [α min ,α max ], the preset range of the modal decomposition number variable is [K min ,K max]; Step three, according to the preset dynamic adjustment strategy, update the harmony in the initial harmony memory library, specifically, generate a random number [0,1], if the random number is greater than HMCR, then regenerate a quadratic penalty factor and modal decomposition number that meet the preset quadratic penalty factor and modal decomposition number variable range requirements from outside the initial harmony memory library to form a new decomposition parameter group, and obtain each decomposition mode through variational modal decomposition, calculate the local envelope entropy according to the local envelope entropy calculation formula, and form a new harmony (described as the "first harmony" in this article), otherwise select a harmony from the initial harmony memory library as the first harmony; if the first harmony comes from the initial harmony memory library, then regenerate a random number [0,1], if the random number is less than the pitch adjustment probability PAR , then the quadratic penalty factor and the modal decomposition number in the first harmony are adjusted according to the pitch adjustment bandwidth bw, otherwise no adjustment is required to form a second harmony; step four, according to the quadratic penalty factor and the modal decomposition number of the second harmony, each decomposition mode is obtained by variational modal decomposition, and the local envelope entropy is calculated according to the local envelope entropy calculation formula to obtain the objective function value corresponding to the second harmony. If the corresponding objective function value is better than the worst harmony in the initial harmony memory bank, the second harmony replaces the worst harmony to update the initial harmony memory bank; step five, repeat steps three and four, iteratively optimize, and determine whether the preset convergence condition group is met. When the preset convergence condition group is met, the quadratic penalty factor and modal decomposition number corresponding to the optimal harmony are output, that is, the optimal decomposition parameter group is output.

[0060] The secondary penalty factor and the modal decomposition number in the first harmony are adjusted according to the pitch adjustment bandwidth bw, specifically through the following strategy formula:

[0061]

[0062]

[0063] K2=K1+bw(g);

[0064] α2=α1+bw(g);

[0065] Where g is the current iteration number; g max is the maximum number of iterations; K1 and K2 are the modal decomposition numbers of the first and second harmonics respectively; α1 and α2 are the quadratic penalty factors of the first and second harmonics respectively.

[0066] In one embodiment, the preset convergence conditions include but are not limited to: the number of iterations reaches the upper limit g max , and the algebraic g that the objective function value corresponding to the optimal harmony in the harmony memory remains unchanged con Reach the set value.

[0067] S3: Obtain the effective DC voltage component and the effective DC current component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, the preset maximum mutual information coefficient calculation formula, and the preset maximum mutual information coefficient threshold.

[0068] In one embodiment, a denoised DC voltage sequence and a denoised DC current sequence are obtained based on the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold. The method specifically includes: calculating, based on the preset maximum mutual information coefficient calculation formula, a voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and a current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence; screening out a DC voltage effective component from the DC voltage decomposition modal sequence based on the voltage maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold; and screening out a DC current effective component from the current decomposition modal sequence based on the current maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold.

[0069] In one embodiment, the maximum mutual information coefficient calculation formula is specifically:

[0070]

[0071] Where N is the number of sample points of each decomposed mode sequence and the original sampling data sequence; p(x i ,o) is the joint probability density of the i-th decomposition mode sequence and the original sampling data sequence; p(x i ) and p(o) are the marginal probability densities of the i-th decomposed modal sequence and the original sampled data sequence, respectively; a and b are the number of rows and columns of the grid, respectively. It should be noted that the grid method can be used, but is not limited to, to calculate the marginal and joint probability densities of each decomposed modal sequence and the original sampled data sequence.

[0072] After calculating and obtaining the maximum mutual information coefficient sequence for voltage and the maximum mutual information coefficient sequence for current, the components in the original DC voltage sampling data sequence can be divided into valid components and invalid components based on a preset maximum mutual information coefficient threshold, thereby obtaining a denoised DC voltage sequence. Correspondingly, the components in the denoised DC current sequence obtained by screening out the original current sampling data sequence can be divided into valid components and invalid components based on a preset maximum mutual information coefficient threshold, thereby subsequently reconstructing the denoised DC voltage sequence and the denoised DC current sequence based on the valid components obtained by the division.

[0073] The division process can be expressed as follows:

[0074]

[0075] Where γ0 is the maximum mutual information coefficient threshold.

[0076] S4: reconstructing a denoised DC voltage sequence and a denoised DC current sequence according to the effective DC voltage component and the effective DC current component, respectively.

[0077] In one embodiment, reconstructing the de-noised DC voltage sequence and the de-noised DC current sequence according to the effective DC voltage component and the effective DC current component respectively includes: reconstructing the de-noised DC voltage sequence and the de-noised DC current sequence according to the effective DC voltage component and the effective DC current component respectively.

[0078] An embodiment of the present invention describes a method for reducing noise in DC electric energy metering. By calculating the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, adaptive variational modal decomposition of the original DC voltage sampling data sequence and the original current sampling data sequence is achieved. By calculating the maximum mutual information coefficient of each decomposed modal sequence and the original sampling data sequence, reliable distinction and screening of the effective components and noise components of each decomposed mode are achieved. Effective noise reduction of the original DC voltage and DC current sampling data sequences is achieved by reconstructing the effective components. This noise reduction method improves the metering accuracy of DC electric energy. Specific embodiment 2

[0080] In addition to the above method, an embodiment of the present invention also discloses a DC electric energy metering noise reduction device. Figure 2 The figure is a schematic structural diagram of a DC power metering noise reduction device provided by one embodiment of the present invention.

[0081] like Figure 2 As shown, the DC electric energy metering noise reduction device includes a sequence acquisition unit 11 , a modal decomposition unit 12 , an electric energy denoising unit 13 and a sequence reconstruction unit 14 .

[0082] The sequence acquisition unit 11 is used to acquire a sequence of original sampling data of DC voltage and a sequence of original sampling data of current of the DC device to be measured.

[0083] The modal decomposition unit 12 is used to perform adaptive variational modal decomposition and iterative optimization on the DC voltage original sampling data sequence and the current original sampling data sequence according to a preset local signal envelope entropy calculation formula and a preset improved harmony search intelligent optimization algorithm to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence.

[0084] In one embodiment, the modal decomposition unit 12 is further used to: establish a variational modal decomposition optimization model based on a preset local signal envelope entropy calculation formula and a preset model constraint condition group; iteratively optimize the preset initial decomposition parameter group according to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model until an optimal decomposition parameter group is output; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; based on the optimal decomposition parameter group, perform adaptive variational modal decomposition on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence.

[0085] In one embodiment, the modal decomposition unit 12 is further used to: obtain preset algorithm parameter values, and establish an initial harmonic memory bank based on the memory bank capacity and the variational modal decomposition optimization model; the initial harmonic memory bank includes a number of first harmonies corresponding to the memory bank capacity; each group of first harmonies includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include memory bank capacity, memory bank consideration probability, dynamic adjustment upper limit of adjustment probability, dynamic adjustment lower limit of adjustment probability, dynamic adjustment upper limit of bandwidth, and dynamic adjustment lower limit of bandwidth; according to the preset dynamic adjustment strategy, the variational modal decomposition optimization model, memory bank consideration probability, dynamic adjustment upper limit of adjustment probability, dynamic adjustment lower limit of adjustment probability, dynamic adjustment upper limit of bandwidth, and dynamic adjustment lower limit of bandwidth, iteratively optimize the first harmonics in the initial harmonic memory bank, and determine whether the preset convergence condition group is met; when the preset convergence condition group is met, output the corresponding quadratic penalty factor and modal decomposition number as the optimal decomposition parameter group.

[0086] In actual implementation, the modal decomposition unit 12 first obtains preset algorithm parameter values; the algorithm parameter values include memory bank capacity, memory bank consideration probability, dynamic adjustment upper limit of adjustment probability, dynamic adjustment lower limit of adjustment probability, dynamic adjustment upper limit of bandwidth, and dynamic adjustment lower limit of bandwidth; then, an initial harmony memory bank is established, and a corresponding number of harmonies are generated according to the preset memory bank capacity, each group of harmonies including an initial decomposition parameter group and its corresponding local signal envelope entropy value, wherein the initial decomposition parameter group is randomly extracted and generated within the preset quadratic penalty factor and modal decomposition number variable value range, and each decomposition mode is obtained by variational modal decomposition, and the local envelope entropy is calculated according to the local envelope entropy calculation formula; according to the preset dynamic adjustment strategy, the harmonies in the initial harmony memory bank are updated; finally, through iterative optimization, it is determined whether the preset convergence condition group is met; when the preset convergence condition group is met, the quadratic penalty factor and modal decomposition number corresponding to the optimal harmony are output, that is, the optimal decomposition parameter group is output.

[0087] In one embodiment, the modal decomposition unit 12 is further used to: obtain the optimal decomposition parameter group; according to a preset Fourier transform sequence iterative calculation formula, a center frequency iterative calculation formula, the DC voltage original sampling data sequence and the current original sampling data sequence, with a first operator Fourier transform sequence as an iterative parameter, perform iterative calculation on the Fourier transform sequence of each modal component in the DC voltage original sampling data sequence and the Fourier transform sequence of each modal component in the current original sampling data sequence until a preset iterative convergence condition is met, and output the DC voltage decomposition modal sequence and the current decomposition modal sequence.

[0088] The power denoising unit 13 is configured to obtain a DC voltage effective component and a DC current effective component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold.

[0089] In one embodiment, the electric energy denoising unit 13 is further used to: calculate the voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and the current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence according to a preset maximum mutual information coefficient calculation formula; filter out the DC voltage effective component from the DC voltage decomposition modal sequence according to the voltage maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold, and filter out the DC current effective component from the current decomposition modal sequence according to the current maximum mutual information coefficient sequence and the preset maximum mutual information coefficient threshold.

[0090] The sequence reconstruction unit 14 is used to reconstruct a de-noised DC voltage sequence and a de-noised DC current sequence according to the effective DC voltage component and the effective DC current component, respectively.

[0091] In one embodiment, the sequence reconstruction unit is further configured to: superimpose all effective components of DC voltage to obtain a de-noised DC voltage sequence, and superimpose all effective components of DC current to obtain a de-noised DC current sequence.

[0092] Among them, if the integrated unit of the DC electric energy metering noise reduction device 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. That is, another embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the DC electric energy metering noise reduction method as described above.

[0093] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0094] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the units indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0095] The embodiment of the present invention describes a DC electric energy metering noise reduction device and a computer-readable storage medium. The device calculates the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, realizes adaptive variational modal decomposition of the DC voltage original sampling data sequence and the current original sampling data sequence, and realizes reliable distinction and screening of the effective component and noise component of each decomposed mode by calculating the maximum mutual information coefficient between each decomposed modal sequence and the original sampling data sequence. The device and the computer-readable storage medium improve the measurement accuracy of DC electric energy by reconstructing the effective component. Specific embodiment three

[0097] In addition to the above-mentioned method and apparatus, the embodiment of the present invention also describes a DC power metering noise reduction system.

[0098] The DC power metering noise reduction system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the DC power metering noise reduction method as described above is implemented.

[0099] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device and connects various parts of the entire device using various interfaces and lines.

[0100] The memory can be used to store the computer programs and / or modules, and the processor realizes the various functions of the device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0101] An embodiment of the present invention describes a DC electric energy metering noise reduction system, which calculates the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, realizes adaptive variational modal decomposition of the DC voltage original sampling data sequence and the current original sampling data sequence, and realizes reliable distinction and screening of the effective component and noise component of each decomposition mode by calculating the maximum mutual information coefficient between each decomposition modal sequence and the original sampling data sequence, and realizes effective noise reduction of the DC voltage and DC current original sampling data sequences by reconstructing the effective components. The noise reduction system improves the metering accuracy of DC electric energy.

[0102] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A DC electric energy metering noise reduction method, characterized in that: The DC electric energy metering noise reduction method comprises: Collecting the original sampling data sequence of DC voltage and current of the DC device to be measured; According to a preset local signal envelope entropy calculation formula and a preset improved harmony search intelligent optimization algorithm, adaptive variational modal decomposition and iterative optimization are performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically including: A variational mode decomposition optimization model is established based on a preset local signal envelope entropy calculation formula and a preset model constraint condition group; According to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, the preset initial decomposition parameter group is iteratively optimized until the optimal decomposition parameter group is output; the initial decomposition parameter group includes an initial quadratic penalty factor and an initial modal decomposition number; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; performing adaptive variational modal decomposition on the DC voltage original sampling data sequence and the current original sampling data sequence according to the optimal decomposition parameter group to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence; According to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, the preset initial decomposition parameter group is iteratively optimized until the optimal decomposition parameter group is output, specifically including: Obtaining preset algorithm parameter values, and establishing an initial harmony memory bank based on the memory bank capacity and the variational modal decomposition optimization model; the initial harmony memory bank includes a number of first harmonics corresponding to the memory bank capacity; each group of first harmonics includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include the memory bank capacity, the memory bank consideration probability, the upper limit of the dynamic adjustment of the adjustment probability, the lower limit of the dynamic adjustment of the adjustment probability, the upper limit of the dynamic adjustment of the bandwidth, and the lower limit of the dynamic adjustment of the bandwidth; Iteratively optimize the first harmony in the initial harmony memory according to a preset dynamic adjustment strategy, the variational modal decomposition optimization model, the memory bank consideration probability, the upper limit of the dynamic adjustment of the adjustment probability, the lower limit of the dynamic adjustment of the adjustment probability, the upper limit of the dynamic adjustment of the bandwidth, and the lower limit of the dynamic adjustment of the bandwidth, and determine whether a preset convergence condition set is met; when the preset convergence condition set is met, output the corresponding quadratic penalty factor and modal decomposition number as the optimal decomposition parameter set; The preset dynamic adjustment strategies include: The secondary penalty factor and the modal decomposition number in the first harmony are adjusted according to the pitch adjustment bandwidth bw, specifically through the following strategy formula: ； ； K2=K1+bw(g); α2=α1+bw(g); Where g is the current iteration number; g max is the maximum number of iterations; K1 and K2 are the modal decomposition numbers of the first and second harmonics respectively; α1 and α2 are the quadratic penalty factors of the first and second harmonics respectively; Obtaining a DC voltage effective component and a DC current effective component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold, specifically including: Calculating, according to a preset maximum mutual information coefficient calculation formula, a voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and a current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence; Filtering out a DC voltage effective component from the DC voltage decomposition modal sequence based on the voltage maximum mutual information coefficient sequence and a preset maximum mutual information coefficient threshold, and filtering out a DC current effective component from the current decomposition modal sequence based on the current maximum mutual information coefficient sequence and a preset maximum mutual information coefficient threshold; The denoised DC voltage sequence and the denoised DC current sequence are reconstructed according to the effective DC voltage component and the effective DC current component, respectively.

2. The DC electric energy metering noise reduction method according to claim 1, characterized in that: According to the optimal decomposition parameter group, adaptive variational modal decomposition is performed on the DC voltage original sampling data sequence and the current original sampling data sequence to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically comprising: Obtaining the optimal decomposition parameter group; According to the optimal decomposition parameter group, a preset Fourier transform sequence iterative calculation formula, a center frequency iterative calculation formula, the DC voltage original sampling data sequence and the current original sampling data sequence, and with a first operator Fourier transform sequence as an iterative parameter, an iterative calculation is performed on the Fourier transform sequence of each modal component in the DC voltage original sampling data sequence and the Fourier transform sequence of each modal component in the current original sampling data sequence until a preset iterative convergence condition is met, and a DC voltage decomposition modal sequence and a current decomposition modal sequence are output.

3. The DC electric energy metering noise reduction method according to claim 1, characterized in that: The local signal envelope entropy calculation formula is specifically: ； Where, E min is the local signal envelope entropy, E i is the signal envelope entropy value of the i-th modal component after the variational modal decomposition of the original sampling data sequence of DC voltage / current at the output end; p i,t is the proportional coefficient of the t-th data point in the sequence after the Hilbert transform of the i-th modal component; T is the total number of points in the new sequence formed after the Hilbert transform of each modal component; K is the modal decomposition number.

4. The DC electric energy metering noise reduction method according to claim 1, characterized in that: The maximum mutual information coefficient calculation formula is specifically: ； Where N is the number of sample points of each decomposed mode sequence and the original sampling data sequence; p(x i ,o) is the joint probability density of the i-th decomposition mode sequence and the original sampling data sequence; p(x i ) and p(o) are the marginal probability densities of the i-th decomposition mode sequence and the original sampling data sequence respectively; a and b are the number of rows and columns of the grid respectively.

5. A DC electric energy metering noise reduction device, characterized in that: The DC electric energy metering noise reduction device includes a sequence acquisition unit, a modal decomposition unit, an electric energy denoising unit and a sequence reconstruction unit, wherein: The sequence acquisition unit is used to acquire the original sampling data sequence of DC voltage and the original sampling data sequence of current of the DC device to be measured; The modal decomposition unit is configured to perform adaptive variational modal decomposition and iterative optimization on the DC voltage original sampling data sequence and the current original sampling data sequence according to a preset local signal envelope entropy calculation formula and a preset improved harmony search intelligent optimization algorithm to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence, specifically including: A variational mode decomposition optimization model is established based on a preset local signal envelope entropy calculation formula and a preset model constraint condition group; According to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, the preset initial decomposition parameter group is iteratively optimized until the optimal decomposition parameter group is output; the initial decomposition parameter group includes an initial quadratic penalty factor and an initial modal decomposition number; the optimal decomposition parameter group includes an optimal quadratic penalty factor and an optimal modal decomposition number; performing adaptive variational modal decomposition on the DC voltage original sampling data sequence and the current original sampling data sequence according to the optimal decomposition parameter group to obtain a DC voltage decomposition modal sequence and a current decomposition modal sequence; According to the preset improved harmony search intelligent optimization algorithm and the variational modal decomposition optimization model, the preset initial decomposition parameter group is iteratively optimized until the optimal decomposition parameter group is output, specifically including: Obtaining preset algorithm parameter values, and establishing an initial harmony memory bank based on the memory bank capacity and the variational modal decomposition optimization model; the initial harmony memory bank includes a number of first harmonics corresponding to the memory bank capacity; each group of first harmonics includes an initial decomposition parameter group and its corresponding local signal envelope entropy value; the algorithm parameter values include the memory bank capacity, the memory bank consideration probability, the upper limit of the dynamic adjustment of the adjustment probability, the lower limit of the dynamic adjustment of the adjustment probability, the upper limit of the dynamic adjustment of the bandwidth, and the lower limit of the dynamic adjustment of the bandwidth; Iteratively optimize the first harmony in the initial harmony memory according to a preset dynamic adjustment strategy, the variational modal decomposition optimization model, the memory bank consideration probability, the upper limit of the dynamic adjustment of the adjustment probability, the lower limit of the dynamic adjustment of the adjustment probability, the upper limit of the dynamic adjustment of the bandwidth, and the lower limit of the dynamic adjustment of the bandwidth, and determine whether a preset convergence condition set is met; when the preset convergence condition set is met, output the corresponding quadratic penalty factor and modal decomposition number as the optimal decomposition parameter set; The preset dynamic adjustment strategies include: The secondary penalty factor and the modal decomposition number in the first harmony are adjusted according to the pitch adjustment bandwidth bw, specifically through the following strategy formula: ； ； K2=K1+bw(g); α2=α1+bw(g); Where g is the current iteration number; g max is the maximum number of iterations; K1 and K2 are the modal decomposition numbers of the first and second harmonics respectively; α1 and α2 are the quadratic penalty factors of the first and second harmonics respectively; The power denoising unit is configured to screen and obtain the effective DC voltage component and the effective DC current component according to the DC voltage decomposition modal sequence, the current decomposition modal sequence, a preset maximum mutual information coefficient calculation formula, and a preset maximum mutual information coefficient threshold, specifically including: Calculating, according to a preset maximum mutual information coefficient calculation formula, a voltage maximum mutual information coefficient sequence between the DC voltage original sampling data sequence and the DC voltage decomposition modal sequence, and a current maximum mutual information coefficient sequence between the current original sampling data sequence and the current decomposition modal sequence; Filtering out a DC voltage effective component from the DC voltage decomposition modal sequence based on the voltage maximum mutual information coefficient sequence and a preset maximum mutual information coefficient threshold, and filtering out a DC current effective component from the current decomposition modal sequence based on the current maximum mutual information coefficient sequence and a preset maximum mutual information coefficient threshold; The sequence reconstruction unit is used to reconstruct a de-noised DC voltage sequence and a de-noised DC current sequence according to the effective component of the DC voltage and the effective component of the DC current, respectively.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the DC power metering noise reduction method according to any one of claims 1 to 4.

7. A DC power metering noise reduction system, characterized in that: The DC power metering noise reduction system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the DC power metering noise reduction method according to any one of claims 1 to 4 is implemented.

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