阅读说明：本技术 提高谐波检测精度的自适应vmd检测方法、装置及存储介质 (Adaptive VMD detection method and device for improving harmonic detection precision and storage medium ) 是由 周金 雷平 牛晖 张建涛 邓丽娜 于 2021-06-30 设计创作，主要内容包括：本发明提供了一种提高谐波检测精度的自适应VMD检测方法、装置及存储介质,该方法包括以下步骤：对谐波信号P(t)进行归一化处理；将信号P(t)进行k层变分模态分解,k＝2,3…n+1,求取最优分解层数K-(best)；利用最优分解层数K-(best)对信号P(t)进行分解,得到K-(best)个本征模态分量c-(i)(t),i＝1,2…K-(best)；对本征模态分量c-(i)(t)进行Hilbert变换,得到瞬时幅值a-(i)(t)和瞬时频率f-(i)(t)；从瞬时幅值a-(i)(t)和瞬时频率f-(i)(t)中找出最符合信号趋势的波形,对其两端进行处理,端点处理后得到的信号为a-(i)(t)'和f-(i)(t)'；求取a-(i)(t)'和f-(i)(t)'的均值,得到各谐波分量的幅值及频率。本发明具有良好的噪声鲁棒性,能够自适应选取最优分解层数,还能有效抑制瞬时幅值和频率的端点结果失真的问题,能够准确检测电网谐波信号。(The invention provides a self-adaptive VMD detection method, a device and a storage medium for improving harmonic detection precision, wherein the method comprises the following steps: normalizing the harmonic signal P (t); performing K-layer metamorphic modal decomposition on the signal P (t), wherein K is 2,3 … n +1, and obtaining the optimal decomposition layer number K best (ii) a Using the optimal number of decomposition layers K best Decomposing the signal P (t) to obtain K best An intrinsic mode component c i (t)，i＝1,2…K best (ii) a For the eigenmode component c i (t) carrying out Hilbert transformation to obtain an instantaneous amplitude a i (t) and instantaneous frequency f i (t); from the instantaneous amplitude a i (t) and instantaneous frequency f i (t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing i (t)' and f i (t)'; find a i (t)' and f i And (t)' obtaining the amplitude and frequency of each harmonic component. The invention has good noise robustness and can self-adaptively select the optimal decompositionThe number of layers can effectively inhibit the problem of end point result distortion of instantaneous amplitude and frequency, and can accurately detect the harmonic signals of the power grid.)

1. A self-adaptive VMD detection method for improving harmonic detection precision is characterized by comprising the following steps:

normalizing the harmonic signal P (t);

performing K-layer metamorphic modal decomposition on the signal P (t), wherein K is 2,3 … n +1, and obtaining the optimal decomposition layer number K_best；

Using the optimal number of decomposition layers K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best；

For the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t)；

From the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'；

Find a_i(t)' and f_iAnd (t)' obtaining the amplitude and frequency of each harmonic component.

2. The improved harmonic detection of claim 1The precision adaptive VMD detection method is characterized in that the signal P (t) is subjected to K-layer variation modal decomposition, K is 2,3 … n +1, and the optimal decomposition layer number K is obtained_bestThe method specifically comprises the following steps:

performing k-layer variation mode decomposition on the signal P (t), wherein k is 2,3 … n +1, and calculating the shape factor SFR of each intrinsic mode function IMF of different decomposition layer numbers_IMFnDetermining the shape factor SFR of the signal P (t)_P(t)Defining a form factorMean (SFR)/var (SFR), minimum MEVAR_minNumber of layers of decompositionNumber of decomposition layers K optimized for harmonic signals_best。

3. The adaptive VMD detection method for improving harmonic detection accuracy of claim 2, wherein the signal P (t) is subjected to K-layer metamorphic mode decomposition, K is 2,3 … n +1, and the optimal decomposition layer number K is obtained_bestThe specific process is as follows:

a) initializing the decomposition layer number K to 2, and defining the maximum decomposition layer number K_maxInitialization parametersn；

b) According to the formulaUpdating u_k；

c) According to the formulaUpdating

d) According toUpdating the lambda;

e) if it isIf yes, stopping iteration, and if not, returning to the step b);

f) calculating the form factor of the k-layer intrinsic mode function IMFAnd a MEVAR, wherein

g) Judging whether K is equal to K_maxIf the sum is equal to the minimum mean value, the program is terminated, if the sum is not equal to the minimum mean value, k +1, the step b) is returned, and finally the minimum mean value of the mean is obtained_minNumber of layers of decompositionFor optimal number of decomposition layers K_best。

4. The adaptive VMD detection method for improving harmonic detection accuracy of claim 1, wherein: the pair of eigenmode components c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) specifically includes:

for the eigenmode component c_i(t) performing Hilbert transform to construct an analytic signal:

wherein the content of the first and second substances,denotes the ith c_iOf (t) componentThe Hilbert transform, τ expressed as time,is denoted by c_i(t) an instantaneous amplitude function of the component,is denoted by c_i(t) instantaneous frequency.

5. The adaptive VMD detection method for improving harmonic detection accuracy of claim 1, wherein: the slave instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, and processing the two ends of the waveform to obtain the instantaneous amplitude a_i(t) the specific process of the left-end treatment is as follows:

a) finding out the starting point values a of all triangular waveforms except the characteristic waveform_i(i) The corresponding time points are as follows:

wherein m'_iAnd n'_iAre respectively a signal a_i(t) maximum and minimum values, respectively, corresponding to times ofAnda_i(t) has a left end point of_i(1)，a_i(1)-m′₁-n′₁Is a characteristic waveform, a_i(i)-m′_i-n′_iFor the best matching waveform, when t_a(i)When the sampling point is not in the sampling point, processing the sampling point by adopting a cubic spline interpolation value;

b) and (3) solving the matching errors of all the characteristic waveforms and the triangular waveforms, wherein the error formula is as follows:

e(i)＝|m′_i-m′₁|+|n′_i-n′₁|+|a_i(i)-a_i(1)|

c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to a_iLeft side of (t), analogously to obtain a_i(t) matching waveform on right side of signal, signal obtained after end point processing is a_i(t)' and f_i(t)'。

6. The utility model provides an improve self-adaptation VMD detection device of harmonic detection precision which characterized in that includes:

the normalization processing module is used for performing normalization processing on the harmonic signal P (t);

an optimal decomposition layer number calculation module, configured to perform K-layer metamorphic modal decomposition on the signal p (t), where K is 2,3 … n +1, and calculate an optimal decomposition layer number K_best；

A variation modal decomposition module for utilizing the optimal decomposition layer number K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best；

A Hilbert transform module for transforming the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t)；

An end-point processing module for processing the amplitude from the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'；

Amplitude and frequency acquisition module for calculating a_i(t)' and f_iAnd (t)' obtaining the amplitude and frequency of each harmonic component.

7. An adaptive VMD detection apparatus for improving accuracy of harmonic detection, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.

Technical Field

The invention relates to the field of power grids, in particular to a self-adaptive VMD detection method and device for improving harmonic detection precision and a storage medium.

Background

In recent years, the development of renewable energy sources such as wind energy and photovoltaic systems, the widespread use of power electronic devices, the charging of transformers, the instantaneous switching of capacitor banks and nonlinear loads, and the like, as well as the change of environmental factors, have led to the harmonic problem of power grids. The harmonic problem not only can cause the instability and the failure of equipment, but also can easily cause unsafe electricity utilization of users, and can cause accidents. Therefore, in order to ensure the reliability, safety and high quality of power supply, it is important to continuously and accurately monitor the harmonic signals of the power grid. In addition, in recent years, the operation management mode of the power grid gradually changes from traditional to intelligent mode, and the comprehensive starting of the smart power grid worldwide puts higher requirements on accurate detection of harmonic signals of the power grid.

The common methods for detecting the abnormality of the harmonic signal and extracting the features are as follows: fast fourier transform, short-time fourier transform, wavelet transform, empirical mode decomposition, S-transform, and variational mode decomposition, etc.

The detection accuracy of the fast fourier transform depends on the choice of the window function and is prone to problems of detection delay and loss of detection information due to non-integer periodic truncation and non-synchronous sampling of the signal. The short-time fourier transform frequency and time resolution cannot change with the frequency change of the signal, which causes a large signal detection error. The wavelet transform method for detecting harmonic signals depends on the mother wavelet and the choice of the number of decomposition layers. Although a number of algorithms have been developed to improve upon the above problem, improved wavelet transforms (e.g., wavelet packet transforms and discrete wavelet packet transforms) still suffer from spectral leakage problems to varying degrees. The intrinsic mode components obtained by decomposing the signals through empirical mode decomposition are easy to generate mode aliasing and end point effects. The amplitude spectrum result obtained by the detection algorithm based on the S transformation needs coefficient correction, and the algorithm has large calculation amount.

Variational Modal Decomposition (VMD) is a non-recursive decomposition technique for adaptive and quasi-orthogonal signal decomposition that can decompose a multi-component signal into K eigenmode functions (IMFs). The value of the decomposition scale K directly influences the detection effect of the signal, and the smaller value of the K can cause the larger bandwidth of the modal function, so that the extraction of disturbance information is incomplete. On the contrary, the larger K value and the smaller bandwidth of the modal function lead the center frequencies of the modal components to be overlapped, and the false components are generated. At present, related researches on combination of a VMD algorithm and power grid harmonic detection are few, most researches adopt artificial preset decomposition layer number K values and instantaneous amplitude and frequency obtained by Hilbert conversion without any treatment, and the defects of strong subjectivity, lack of scientific basis, low parameter detection precision and the like exist.

Disclosure of Invention

The invention aims to provide a self-adaptive VMD detection method, a self-adaptive VMD detection device and a storage medium for improving harmonic detection precision, and aims to improve the detection precision of a power grid harmonic signal.

The invention is realized by the following steps:

in a first aspect, the present invention provides an adaptive VMD detection method for improving harmonic detection accuracy, comprising the following steps:

normalizing the harmonic signal P (t);

performing K-layer metamorphic modal decomposition on the signal P (t), wherein K is 2,3 … n +1, and obtaining the optimal decomposition layer number K_best；

Using the optimal number of decomposition layers K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best；

For the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t)；

From the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'；

Find a_i(t)' and f_i(t)' ofAnd obtaining the amplitude and the frequency of each harmonic component by averaging.

Further, the signal p (t) is subjected to K-layer metamorphic modal decomposition, K is 2,3 … n +1, and the optimal decomposition layer number K is obtained_bestThe method specifically comprises the following steps:

performing k-layer metamorphic mode decomposition on the signal P (t), wherein k is 2,3 … n +1, and calculating the form factor of each intrinsic mode function IMF of different decomposition layer numbersDetermining the shape factor SFR of the signal P (t)_P(t)Defining a form factorMean (SFR)/var (SFR), minimum MEVAR_minNumber of layers of decompositionNumber of decomposition layers K optimized for harmonic signals_best。

Further, the signal p (t) is subjected to K-layer metamorphic modal decomposition, K is 2,3 … n +1, and the optimal decomposition layer number K is obtained_bestThe specific process is as follows:

a) initializing the decomposition layer number K to 2, and defining the maximum decomposition layer number K_maxInitialization parameters

b) According to the formulaUpdating u_k；

c) According to the formulaUpdating

d) According toUpdating the lambda;

e) if it isIf yes, stopping iteration, and if not, returning to the step b);

f) calculating the form factor of the k-layer intrinsic mode function IMFAnd a MEVAR, wherein

g) Judging whether K is equal to K_maxIf the sum is equal to the minimum mean value, the program is terminated, if the sum is not equal to the minimum mean value, k +1, the step b) is returned, and finally the minimum mean value of the mean is obtained_minNumber of layers of decompositionFor optimal number of decomposition layers K_best。

Further, the pair of eigenmode components c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) specifically includes:

for the eigenmode component c_i(t) performing Hilbert transform to construct an analytic signal:

wherein the content of the first and second substances,denotes the ith c_i(t) Hilbert transform of the component, τ expressed as time,is denoted by c_i(t) an instantaneous amplitude function of the component,is denoted by c_i(t) instantaneous frequency.

Further, the slave instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, and processing the two ends of the waveform to obtain the instantaneous amplitude a_i(t) the specific process of the left-end treatment is as follows:

a) finding out the starting point values a of all triangular waveforms except the characteristic waveform_i(i) The corresponding time points are as follows:

wherein m'_iAnd n'_iAre respectively a signal a_i(t) maximum and minimum values, respectively, corresponding to times ofAnda_i(t) has a left end point of_i(1)，a_i(1)-m′₁-n′₁Is a characteristic waveform, a_i(i)-m′_i-n′_iFor the best matching waveform, when t_a(i)When the sampling point is not in the sampling point, processing the sampling point by adopting a cubic spline interpolation value;

b) and (3) solving the matching errors of all the characteristic waveforms and the triangular waveforms, wherein the error formula is as follows:

e(i)＝|m′_i-m′₁|+|n′_i-n′₁|+|a_i(i)-a_i(1)|

c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to a_iLeft side of (t), analogously to obtain a_i(t) matching waveform on right side of signal, signal obtained after end point processing is a_i(t)' and f_i(t)'。

In a second aspect, the present invention further provides an adaptive VMD detection apparatus for improving harmonic detection accuracy, including:

the normalization processing module is used for performing normalization processing on the harmonic signal P (t);

an optimal decomposition layer number calculation module, configured to perform K-layer metamorphic modal decomposition on the signal p (t), where K is 2,3 … n +1, and calculate an optimal decomposition layer number K_best；

A variation modal decomposition module for utilizing the optimal decomposition layer number K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best；

A Hilbert transform module for transforming the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t)；

An end-point processing module for processing the amplitude from the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'；

Amplitude and frequency acquisition module for calculating a_i(t)' and f_iAnd (t)' obtaining the amplitude and frequency of each harmonic component.

In a third aspect, the present invention further provides an adaptive VMD detection apparatus for improving harmonic detection accuracy, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the computer program.

In a fourth aspect, the present invention also provides a computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method as set forth in any of the above.

Compared with the prior art, the invention has the following beneficial effects:

the self-adaptive VMD detection method, the device and the storage medium for improving the harmonic detection precision provided by the invention carry out self-adaptive parameter optimization selection on the VMD based on the ratio of the mean value and the variance of the shape parameters, select the optimal decomposition layer number K, and carry out endpoint processing on the amplitude and frequency information of the harmonic signal by utilizing self-adaptive waveform matching. Simulation comparison experiments carried out by the invention show that the lowest energy of harmonic component amplitude error reaches 0%, the lowest energy of frequency error reaches 0.49%, the amplitude precision is improved by 6.67% to the maximum extent compared with the amplitude precision of the harmonic component obtained without processing, and the frequency precision is improved by 1.02% to the maximum extent. Therefore, the method has good noise robustness, can self-adaptively select the optimal decomposition layer number, can effectively inhibit the problem of end point result distortion of instantaneous amplitude and frequency, and can accurately detect the harmonic signals of the power grid.

Drawings

Fig. 1 is a flowchart of an adaptive VMD detection method for improving harmonic detection accuracy according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an exemplary simulation signal, determination of an optimal decomposition level number, and a VDM decomposition component according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an instantaneous amplitude and an instantaneous frequency according to an embodiment of the present invention;

fig. 4 is a block diagram of an adaptive VMD detection apparatus for improving harmonic detection accuracy according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, an embodiment of the present invention provides an adaptive VMD detection method for improving harmonic detection accuracy, including the following steps:

1) the harmonic signal p (t) is normalized as follows:

where k is the number of samples and N is the total number of samples.

2) Performing K-layer metamorphic modal decomposition on the signal P (t), wherein K is 2,3 … n +1, and obtaining the optimal decomposition layer number K_best(ii) a The method specifically comprises the following steps:

performing k-layer metamorphic mode decomposition on the signal P (t), wherein k is 2,3 … n +1, and calculating the form factor of each intrinsic mode function IMF of different decomposition layer numbersDetermining the shape factor SFR of the signal P (t)_P(t)Defining a form factorThe larger var (sfr), the smaller mean (sfr), the smaller MEVAR, and the smaller IMF, the smaller the correlation between IMFs, and the effective separation of IMFs. Minimum MEVAR value MEVAR_minNumber of layers of decompositionNumber of decomposition layers K optimized for harmonic signals_best. The decomposition process and the solving formula are as follows:

a) initializing the decomposition layer number K to 2, and defining the maximum decomposition layer number K_maxInitialization parameters

b) According to the formulaUpdating u_k；

c) According to the formulaUpdating

d) According toUpdating the lambda;

e) if it isIf yes, stopping iteration, and if not, returning to the step b);

f) calculating the form factor of the k-layer intrinsic mode function IMFAnd a MEVAR_minWherein

g) Judging whether K is equal to K_maxIf the sum is equal to the minimum mean value, the program is terminated, if the sum is not equal to the minimum mean value, k +1, the step b) is returned, and finally the minimum mean value of the mean is obtained_minNumber of layers of decompositionFor optimal number of decomposition layers K_best。

3) Using the optimal number of decomposition layers K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best(ii) a The specific decomposition is as follows:

4) for the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t); the method specifically comprises the following steps:

for the eigenmode component c_i(t) performing Hilbert transform to construct an analytic signal:

wherein the content of the first and second substances,denotes the ith c_i(t) Hilbert transform of the component, τ expressed as time,is denoted by c_i(t) an instantaneous amplitude function of the component,is denoted by c_i(t) instantaneous frequency.

5) From the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'; this embodiment is implemented to measure the instantaneous amplitude a_i(t) the left end is processed as an example to describe the adaptive waveform matching algorithm in detail, and the specific process is as follows:

a) finding out the starting point values a of all triangular waveforms except the characteristic waveform_i(i) The corresponding time points are as follows:

wherein m'_iAnd n'_iAre respectively a signal a_i(t) maximum and minimum values, respectively, corresponding to times ofAnda_i(t) has a left end point of_i(1)，a_i(1)-m′₁-n′₁Is a characteristic waveform, a_i(i)-m′_i-n′_iFor the best matching waveform, when t_a(i)When not at the sampling point, three are adoptedProcessing the secondary spline interpolation value;

b) and (3) solving the matching errors of all the characteristic waveforms and the triangular waveforms, wherein the error formula is as follows:

e(i)＝|m′_i-m′₁|+|n′_i-n′₁|+|a_i(i)-a_i(1)|

c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to a_iLeft side of (t), analogously to obtain a_i(t) matching waveform on right side of signal, signal obtained after end point processing is a_i(t)' and f_i(t)'。

6) Find a_i(t)' and f_iAnd (t)' obtaining the characteristic quantities such as the amplitude, the frequency and the like of each harmonic component.

The process according to the invention is described in detail below with reference to a specific example:

1) firstly, a signal model of a power grid harmonic signal is constructed

P(t)＝sin(ωt)+a_k1sin(3ωt)+a_k2sin(5ωt)++a_k3sin(7ωt)

In the formula, ω is 2 π f₀，f₀50Hz, t is the sampling duration 0.2s, and the sampling frequency f_s3200Hz, co-sampling 640 points, a_k1＝0.3，a_k2＝0.2，a_k2To better simulate the actual grid signal and to check the immunity of the algorithm, 35dB of white noise was added to the signal model.

2) Normalizing the signal P (t) to obtain a signal P_g(t) for P_g(t) performing VMD decomposition, wherein α is 1500, τ is 0, and ∈ is 1 × 10^-6，K_maxObtaining MEVAR 15%_min. As shown in fig. 2, MEVAR_minThe minimum number of decomposition layers is 4, whose value is 2476.9911, fig. 2c) is the first component (IMF1) obtained by VMD decomposition when the optimal number of decomposition layers Kbest is 4, IMF1 is the fundamental frequency component, the second component IMF2 is the 5-fold frequency component, the third component IMF3 is the 3-fold frequency component, and the fourth component IMF4 is the 7-fold frequency componentAnd K, the interference of noise can be effectively inhibited, and each harmonic component can be accurately extracted.

3) Instantaneous amplitude and instantaneous frequency are obtained for each modal component by using Hilbert transform, and fig. 3a) and 3c) are unprocessed frequency and amplitude, so that the graph shows that endpoint data are seriously distorted and have a large influence on the detection effect. Fig. 3b) and fig. 3d) are instantaneous amplitudes and frequencies obtained by processing the end points by using an adaptive waveform matching algorithm, and it can be known from the graphs that the processed signal waveforms conform to the natural trend of the original signals and are smoothly connected.

4) And solving the average value of the instantaneous amplitude and the frequency to obtain the amplitude and frequency information of the harmonic component. As can be seen from Table 1, the adaptive waveform matching algorithm better suppresses the problem of harmonic frequency and amplitude endpoint distortion, and improves the accuracy of harmonic component detection.

TABLE 1

Based on the same inventive concept, the embodiment of the present invention further provides an adaptive VMD detection apparatus for improving the harmonic detection accuracy, and as the principle of the problem solved by the apparatus is similar to the method of the foregoing embodiment, the implementation of the apparatus may refer to the implementation of the foregoing method, and repeated details are not repeated.

As shown in fig. 4, an adaptive VMD detection apparatus for improving harmonic detection accuracy according to an embodiment of the present invention may be configured to perform the foregoing method embodiment, where the apparatus includes:

the normalization processing module is used for performing normalization processing on the harmonic signal P (t);

an optimal decomposition layer number calculation module, configured to perform K-layer metamorphic modal decomposition on the signal p (t), where K is 2,3 … n +1, and calculate an optimal decomposition layer number K_best；

A variation modal decomposition module for utilizing the optimal decomposition layer number K_bestDecomposing the signal P (t) to obtain K_bestAn intrinsic mode component c_i(t)，i＝1,2…K_best；

A Hilbert transform module for transforming the eigenmode component c_i(t) carrying out Hilbert transformation to obtain an instantaneous amplitude a_i(t) and instantaneous frequency f_i(t)；

An end-point processing module for processing the amplitude from the instantaneous amplitude a_i(t) and instantaneous frequency f_i(t) finding out the waveform which best meets the signal trend, processing two ends of the waveform, and obtaining a signal a after end point processing_i(t)' and f_i(t)'；

Amplitude and frequency acquisition module for calculating a_i(t)' and f_iAnd (t)' obtaining the amplitude and frequency of each harmonic component.

An embodiment of the present invention further provides an adaptive VMD detection apparatus for improving harmonic detection accuracy, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the computer program.

An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of any one of the above methods.

In summary, the adaptive VMD detection method, apparatus and storage medium for improving harmonic detection accuracy provided by the present invention perform adaptive parameter optimization selection on the VMD based on the ratio of the shape parameter mean to the variance, select the optimal number of decomposition layers K, and perform endpoint processing on the amplitude and frequency information of the harmonic signal by using adaptive waveform matching. Simulation comparison experiments carried out by the invention show that the lowest energy of harmonic component amplitude error reaches 0%, the lowest energy of frequency error reaches 0.49%, the amplitude precision is improved by 6.67% to the maximum extent compared with the amplitude precision of the harmonic component obtained without processing, and the frequency precision is improved by 1.02% to the maximum extent. Therefore, the method has good noise robustness, can self-adaptively select the optimal decomposition layer number, can effectively inhibit the problem of end point result distortion of instantaneous amplitude and frequency, and can accurately detect the harmonic signals of the power grid.

Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the embodiments may be implemented by associated hardware as instructed by a program, which may be stored on a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.