CN113608018A - Adaptive VMD detection method and device for improving harmonic detection precision and storage medium - Google Patents

Adaptive VMD detection method and device for improving harmonic detection precision and storage medium Download PDF

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CN113608018A
CN113608018A CN202110737569.6A CN202110737569A CN113608018A CN 113608018 A CN113608018 A CN 113608018A CN 202110737569 A CN202110737569 A CN 202110737569A CN 113608018 A CN113608018 A CN 113608018A
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周金
雷平
牛晖
张建涛
邓丽娜
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China City Environment Protection Engineering Ltd
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Abstract

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 Kbest(ii) a Using the optimal number of decomposition layers KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest(ii) a For the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t); from the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'; find ai(t)' and fiAnd (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.

Description

Adaptive VMD detection method and device for improving harmonic detection precision and storage medium
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 Kbest；
Using the optimal number of decomposition layers KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest；
For the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t)；
From the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'；
Find ai(t)' and fi(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 obtainedbestThe 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 numbers
Figure BDA00031421241200000312
Determining the shape factor SFR of the signal P (t)P(t)Defining a form factor
Figure BDA0003142124120000031
Mean (SFR)/var (SFR), minimum MEVARminNumber of layers of decomposition
Figure BDA00031421241200000313
Number of decomposition layers K optimized for harmonic signalsbest。
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 obtainedbestThe specific process is as follows:
a) initializing the decomposition layer number K to 2, and defining the maximum decomposition layer number KmaxInitialization parameters
Figure BDA0003142124120000032
b) According to the formula
Figure BDA0003142124120000033
Updating uk；
c) According to the formula
Figure BDA0003142124120000034
Updating
Figure BDA0003142124120000035
d) According to
Figure BDA0003142124120000036
Updating the lambda;
e) if it is
Figure BDA0003142124120000037
If yes, stopping iteration, and if not, returning to the step b);
f) calculating the form factor of the k-layer intrinsic mode function IMF
Figure BDA0003142124120000038
And a MEVAR, wherein
Figure BDA0003142124120000039
g) Judging whether K is equal to KmaxIf 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 obtainedminNumber of layers of decomposition
Figure BDA00031421241200000311
For optimal number of decomposition layers Kbest。
Further, the pair of eigenmode components ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t) specifically includes:
for the eigenmode component ci(t) performing Hilbert transform to construct an analytic signal:
Figure BDA00031421241200000310
wherein,
Figure BDA0003142124120000041
denotes the ith ci(t) Hilbert transform of the component, τ expressed as time,
Figure BDA0003142124120000042
is denoted by ci(t) an instantaneous amplitude function of the component,
Figure BDA0003142124120000043
is denoted by ci(t) instantaneous frequency.
Further, the slave instantaneous amplitude ai(t) and instantaneous frequency fi(t) finding out the waveform which best meets the signal trend, and processing the two ends of the waveform to obtain the instantaneous amplitude ai(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 waveformi(i) The corresponding time points are as follows:
Figure BDA0003142124120000044
wherein m'iAnd n'iAre respectively a signal ai(t) maximum and minimum values, respectively, corresponding to times of
Figure BDA0003142124120000045
And
Figure BDA0003142124120000046
ai(t) has a left end point ofi(1)，ai(1)-m′1-n′1Is a characteristic waveform, ai(i)-m′i-n′iFor the best matching waveform, when ta(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|+|ai(i)-ai(1)|
c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to aiLeft side of (t), analogously to obtain ai(t) matching waveform on right side of signal, signal obtained after end point processing is ai(t)' and fi(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 Kbest；
A variation modal decomposition module for utilizing the optimal decomposition layer number KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest；
A Hilbert transform module for transforming the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t)；
An end-point processing module for processing the amplitude from the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'；
Amplitude and frequency acquisition module for calculating ai(t)' and fiAnd (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.
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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:
Figure BDA0003142124120000061
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 Kbest(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 numbers
Figure BDA0003142124120000062
Determining the shape factor SFR of the signal P (t)P(t)Defining a form factor
Figure BDA0003142124120000071
The 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 MEVARminNumber of layers of decomposition
Figure BDA0003142124120000072
Number of decomposition layers K optimized for harmonic signalsbest. 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 KmaxInitialization parameters
Figure BDA0003142124120000073
b) According to the formula
Figure BDA0003142124120000074
Updating uk；
c) According to the formula
Figure BDA0003142124120000075
Updating
Figure BDA0003142124120000076
d) According to
Figure BDA0003142124120000077
Updating the lambda;
e) if it is
Figure BDA0003142124120000078
If yes, stopping iteration, and if not, returning to the step b);
f) calculating the form factor of the k-layer intrinsic mode function IMF
Figure BDA0003142124120000079
And a MEVARminWherein
Figure BDA00031421241200000710
g) Judging whether K is equal to KmaxIf 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 obtainedminNumber of layers of decomposition
Figure BDA00031421241200000712
For optimal number of decomposition layers Kbest。
3) Using the optimal number of decomposition layers KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest(ii) a The specific decomposition is as follows:
Figure BDA00031421241200000711
4) for the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t); the method specifically comprises the following steps:
for the eigenmode component ci(t) performing Hilbert transform to construct an analytic signal:
Figure BDA0003142124120000081
wherein,
Figure BDA0003142124120000082
denotes the ith ci(t) Hilbert transform of the component, τ expressed as time,
Figure BDA0003142124120000083
is denoted by ci(t) an instantaneous amplitude function of the component,
Figure BDA0003142124120000084
is denoted by ci(t) instantaneous frequency.
5) From the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'; this embodiment is implemented to measure the instantaneous amplitude ai(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 waveformi(i) The corresponding time points are as follows:
Figure BDA0003142124120000085
wherein m'iAnd n'iAre respectively a signal ai(t) maximum and minimum values, respectively, corresponding to times of
Figure BDA0003142124120000086
And
Figure BDA0003142124120000087
ai(t) has a left end point ofi(1)，ai(1)-m′1-n′1Is a characteristic waveform, ai(i)-m′i-n′iFor the best matching waveform, when ta(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|+|ai(i)-ai(1)|
c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to aiLeft side of (t), analogously to obtain ai(t) matching waveform on right side of signal, signal obtained after end point processing is ai(t)' and fi(t)'。
6) Find ai(t)' and fiAnd (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)+ak1sin(3ωt)+ak2sin(5ωt)++ak3sin(7ωt)
In the formula, ω is 2 π f0，f050Hz, t is the sampling duration 0.2s, and the sampling frequency fs3200Hz, co-sampling 640 points, ak1＝0.3，ak2＝0.2，ak2To 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 Pg(t) for Pg(t) performing VMD decomposition, wherein α is 1500, τ is 0, and ∈ is 1 × 10-6，KmaxObtaining MEVAR 15%min. As shown in fig. 2, MEVARminThe 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.
Figure BDA0003142124120000091
Figure BDA0003142124120000101
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 Kbest；
A variation modal decomposition module for utilizing the optimal decomposition layer number KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest；
A Hilbert transform module for transforming the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t)；
An end-point processing module for processing the amplitude from the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'；
Amplitude and frequency acquisition module for calculating ai(t)' and fiAnd (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.

Claims (8)

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 Kbest；
Using the optimal number of decomposition layers KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest；
For the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t)；
From the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'；
Find ai(t)' and fiAnd (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 obtainedbestThe 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 numbersIMFnDetermining the shape factor SFR of the signal P (t)P(t)Defining a form factor
Figure FDA0003142124110000011
Mean (SFR)/var (SFR), minimum MEVARminNumber of layers of decomposition
Figure FDA0003142124110000012
Number of decomposition layers K optimized for harmonic signalsbest。
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 obtainedbestThe specific process is as follows:
a) initializing the decomposition layer number K to 2, and defining the maximum decomposition layer number KmaxInitialization parameters
Figure FDA0003142124110000013
n；
b) According to the formula
Figure FDA0003142124110000014
Updating uk；
c) According to the formula
Figure FDA0003142124110000021
Updating
Figure FDA0003142124110000022
d) According to
Figure FDA0003142124110000023
Updating the lambda;
e) if it is
Figure FDA0003142124110000024
If yes, stopping iteration, and if not, returning to the step b);
f) calculating the form factor of the k-layer intrinsic mode function IMF
Figure FDA0003142124110000025
And a MEVAR, wherein
Figure FDA0003142124110000026
g) Judging whether K is equal to KmaxIf 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 obtainedminNumber of layers of decomposition
Figure FDA0003142124110000027
For optimal number of decomposition layers Kbest。
4. The adaptive VMD detection method for improving harmonic detection accuracy of claim 1, wherein: the pair of eigenmode components ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t) specifically includes:
for the eigenmode component ci(t) performing Hilbert transform to construct an analytic signal:
Figure FDA0003142124110000028
wherein,
Figure FDA0003142124110000029
denotes the ith ciOf (t) componentThe Hilbert transform, τ expressed as time,
Figure FDA00031421241100000210
is denoted by ci(t) an instantaneous amplitude function of the component,
Figure FDA00031421241100000211
is denoted by ci(t) instantaneous frequency.
5. The adaptive VMD detection method for improving harmonic detection accuracy of claim 1, wherein: the slave instantaneous amplitude ai(t) and instantaneous frequency fi(t) finding out the waveform which best meets the signal trend, and processing the two ends of the waveform to obtain the instantaneous amplitude ai(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 waveformi(i) The corresponding time points are as follows:
Figure FDA0003142124110000031
wherein m'iAnd n'iAre respectively a signal ai(t) maximum and minimum values, respectively, corresponding to times of
Figure FDA0003142124110000032
And
Figure FDA0003142124110000033
ai(t) has a left end point ofi(1)，ai(1)-m′1-n′1Is a characteristic waveform, ai(i)-m′i-n′iFor the best matching waveform, when ta(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|+|ai(i)-ai(1)|
c) obtaining the minimum matching error mine (i), using the triangular waveform corresponding to mine (i) as the matching waveform, extending to aiLeft side of (t), analogously to obtain ai(t) matching waveform on right side of signal, signal obtained after end point processing is ai(t)' and fi(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 Kbest；
A variation modal decomposition module for utilizing the optimal decomposition layer number KbestDecomposing the signal P (t) to obtain KbestAn intrinsic mode component ci(t)，i＝1,2…Kbest；
A Hilbert transform module for transforming the eigenmode component ci(t) carrying out Hilbert transformation to obtain an instantaneous amplitude ai(t) and instantaneous frequency fi(t)；
An end-point processing module for processing the amplitude from the instantaneous amplitude ai(t) and instantaneous frequency fi(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 processingi(t)' and fi(t)'；
Amplitude and frequency acquisition module for calculating ai(t)' and fiAnd (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.
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