DC signal ripple detection method based on GMPSO-VMD-HT

Abstract

The invention discloses a direct current signal ripple detection method based on a GMPSO-VMD-HT algorithm, and belongs to the field of direct current metering, and the method comprises the following steps: 1, sampling to obtain a direct current power signal sequence containing noise and ripple components, and carrying out the wavelet denoising of the sequence; 2, determining an optimal decomposition scale K and a penalty factor alpha of variational mode decomposition (VMD) by taking a minimum orthogonal index (IO) of an intrinsic mode function as a target function and adopting a gene mutation particle swarm optimization (GMPSO); step 3, decomposing the de-noised power signal into a series of intrinsic mode functions (IMFs) through a VMD algorithm after optimal parameter determination; and step 4, performing Hilbert transform (HT) on each intrinsic mode function to obtain amplitude and frequency information of each ripple component. According to the invention, Hilbert transform (HT) is carried out on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component, and the method has high accuracy and strong anti-interference capability.

Description

Technical Field

[0001] The present invention relates to the field of direct current measurement, and in particular to a direct current signal ripple detection method based on GMPSO-VMD-HT. Background Art

[0002] With the development of distributed energy sources such as photovoltaic systems, wind power generation and battery energy storage, as well as the increase of DC loads on the user side, DC transmission and distribution systems have attracted widespread attention due to their advantages such as low transmission line cost and high efficiency. However, the multi-source and multi-conversion characteristics of DC transmission and distribution systems will lead to various forms of ripple sources in the system. Existing DC energy meters mostly measure DC steady-state energy, ignoring the power generated by high-frequency ripples, thereby reducing the accuracy of electricity billing. Therefore, whether the frequency and amplitude of the ripple can be accurately detected is very important for the optimization of DC metering algorithms and the accurate measurement of DC energy.

[0003] At present, many ripple detection methods for AC signals have been proposed, such as fast Fourier transform (FFT), empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and variational mode decomposition (VMD). FFT and its improved algorithms cannot adapt to non-stationary signals. The EMD algorithm is sensitive to noise and is easily affected by modal aliasing. The HHT algorithm has problems with modal aliasing and false IMF components. The VMD algorithm was widely used in rotating machinery fault diagnosis, transformer fault diagnosis, lightning fault location, etc. in high-voltage transmission lines in the early days. The algorithm improves the modal aliasing and noise robustness of the EMD algorithm, but the decomposition effect depends on the selection of decomposition parameters, such as the decomposition scale K and the penalty factor α.

[0004] In order to solve the above problem, the present invention proposes a DC signal ripple detection method. Summary of the invention

[0005] The purpose of the present invention is to overcome the problems of low precision and weak anti-interference ability of the DC signal ripple detection method in the prior art, to meet the actual needs, and to provide a DC signal ripple detection method based on the GMPSO-VMD-HT algorithm to solve the above technical problems.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A DC signal ripple detection method based on GMPSO-VMD-HT algorithm comprises the following steps:

[0008] Step 1: Sampling to obtain a DC power signal sequence containing noise and ripple components, and performing wavelet denoising on the sequence;

[0009] Step 2: Taking the minimum orthogonal index (IO) of the intrinsic mode function as the objective function, the genetic mutation particle swarm optimization algorithm (GMPSO) is used to determine the optimal decomposition scale K and penalty factor α of variational mode decomposition (VMD);

[0010] Step 3, decomposing the power signal after noise removal into a series of intrinsic mode functions (IMFs) by using the VMD algorithm after determining the optimal parameters;

[0011] Step 4: Perform Hilbert transform (HT) on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component.

[0012] The present invention has the following beneficial effects: the present invention provides a DC signal ripple detection method based on the GMPSO-VMD-HT algorithm, which takes the minimum orthogonal index (IO) of the intrinsic mode function as the objective function, adopts the genetic mutation particle swarm algorithm (GMPSO) to determine the optimal decomposition scale K and penalty factor α of the variational mode decomposition (VMD), and decomposes the noise-removed power signal into a series of intrinsic mode functions (IMFs) by using the VMD algorithm after determining the optimal parameters, and performs Hilbert transform (HT) on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component, which has high accuracy and strong anti-interference ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is the overall flow chart of the DC signal ripple detection of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0016] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0017] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0018] A DC signal ripple detection method based on GMPSO-VMD-HT algorithm comprises the following steps:

[0019] Step 1: Sampling to obtain a DC power signal sequence containing noise and ripple components, and performing wavelet denoising on the sequence:

[0020] Step 1.1, select appropriate wavelet basis function and decomposition layer number N, perform wavelet decomposition on the noisy signal sequence to obtain wavelet coefficients;

[0021] Step 1.2, determine the threshold of each layer and perform threshold quantization processing on the high-frequency coefficients of the N layers;

[0022] Step 1.3: Wavelet reconstruction to obtain the signal after noise removal.

[0023] Step 2: Taking the minimum orthogonal index (IO) of the intrinsic mode function as the objective function, the genetic mutation particle swarm optimization algorithm (GMPSO) is used to determine the optimal decomposition scale K and penalty factor α of the variational mode decomposition (VMD):

[0024] Step 2.1, the minimum orthogonal index of the intrinsic mode function is used as the objective function of GMPSO, and the orthogonal index (IO) is defined as:

[0025]

[0026] The detection signal is decomposed by VMD to obtain K intrinsic mode functions (IMFs):

[0027]

[0028] Step 2.2, take parameter K as the particle position in the GMPSO algorithm and parameter α as the particle velocity in the GMPSO algorithm, construct the GMPSO optimization algorithm, and the algorithm model is:

[0029]

[0030] Among them, F represents the objective function of the GMPSO algorithm, IO IMFs represents the orthogonal index of the IMFs after the original signal is decomposed by VMD, β = [K, α] is the optimization parameter combination;

[0031] The steps of the GMPSO algorithm are as follows:

[0032] (1) Initialize parameters such as particle position and velocity;

[0033] (2) The particle position and velocity in the GMPSO algorithm are used as the parameter combination [K, α] in the VMD algorithm;

[0034] (3) Use the GMPSO algorithm to find the optimal combination of VMD parameter combinations [K, α];

[0035] (4) Comparison of objective functions Update local and global extrema;

[0036] (5) When the number of iterations does not reach the maximum number, the position of the particle reaches a local extreme value and does not meet the requirements. The GMPSO algorithm generates the next generation of particle positions and velocities with a mutation probability q, avoiding the occurrence of local extreme values ​​in the PSO algorithm.

[0037] (6) When the maximum number of iterations is reached, stop the iteration and output the optimal parameter combination [K, α] of the VMD algorithm.

[0039] Step 3, decomposing the power signal after noise removal into a series of intrinsic mode functions (IMFs) by using the VMD algorithm after determining the optimal parameter combination;

[0040] Step 3.1: Construction of variational model:

[0041] The original signal x(t) is decomposed into u k , and the decomposition sequence is guaranteed to be an intrinsic mode function (IMFs) with limited bandwidth at the center frequency, and the sum of the estimated bandwidths of the K IMF components is minimized. The constraint condition is that the sum of all IMF modal components is equal to the original signal x(t). The constructed variational model with constraint model is:

[0042]

[0043] where u k (t) is each mode, ω k (t) is the center frequency of each mode;

[0044] Step 3.2: Solve the variational constraint problem:

[0045] In order to solve the variational constraint problem, the quadratic penalty factor α to ensure the accuracy of signal reconstruction and the Lagrangian operator λ(t) to ensure the precise execution of the constraint are introduced, and the augmented Lagrangian function L is constructed to transform the constrained variational problem into an unconstrained variational problem. The augmented Lagrangian function is:

[0046]

[0047] The alternating vector multiplier method is used to solve the modal component u. k , center frequency ω k :

[0048]

[0049] Step 4: Perform Hilbert transform (HT) on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component:

[0050] Based on a series of intrinsic mode functions (IMFs) obtained in step 3, they are expressed as:

[0051] x(t)=u1(t)+u2(t)+···+u K (t)

[0052] Through Hilbert transform, we can get each modal function u k (t) corresponds to the instantaneous amplitude a k (t) and frequency f k (t), its expression is:

[0053]

[0054] The corresponding analytical signal is constructed as follows:

[0055]

[0056] The amplitude function and phase function of the mode are:

[0057]

[0058] The instantaneous frequency of the mode f k (t) can be expressed by the phase function ψ k (t) Take the derivative and get:

[0059]

[0060] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A DC signal ripple detection method based on GMPSO-VMD-HT algorithm, characterized in that: The steps include: Step 1: Sampling to obtain a DC power signal sequence containing noise and ripple components, and performing wavelet denoising on the sequence; Step 2: Taking the minimum orthogonal index (IO) of the intrinsic mode function as the objective function, the genetic mutation particle swarm optimization algorithm (GMPSO) is used to determine the optimal decomposition scale K and penalty factor α of variational mode decomposition (VMD); Step 3, decomposing the power signal after noise removal into a series of intrinsic mode functions (IMFs) by using the VMD algorithm after determining the optimal parameters; Step 4: Perform Hilbert transform (HT) on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component.

2. A DC signal ripple detection method based on GMPSO-VMD-HT algorithm according to claim 1, characterized in that: The DC power signal sequence containing noise and ripple components is sampled and the sequence is subjected to wavelet denoising, which includes: Step 1.1, select appropriate wavelet basis function and decomposition layer number N, perform wavelet decomposition on the noisy signal sequence to obtain wavelet coefficients; Step 1.2, determine the threshold of each layer and perform threshold quantization processing on the high-frequency coefficients of the N layers; Step 1.3: Wavelet reconstruction to obtain the signal after noise removal.

3. The DC signal ripple detection method based on the GMPSO-VMD-HT algorithm according to claim 1, characterized in that: Taking the minimum orthogonal index (IO) of the intrinsic mode function as the objective function, the genetic mutation particle swarm optimization (GMPSO) is used to determine the optimal decomposition scale K and penalty factor α of variational mode decomposition (VMD), including: Step 2.1, the minimum orthogonal index of the intrinsic mode function is used as the objective function of GMPSO, and the orthogonal index (IO) is defined as: After VMD decomposition, the detection signal is decomposed into K intrinsic mode functions (IMFs), as follows: Step 2.2, take parameter K as the particle position in the GMPSO algorithm and parameter α as the particle velocity in the GMPSO algorithm, construct the GMPSO optimization algorithm, and the algorithm model is: Among them, F represents the objective function of the GMPSO algorithm, IO IMFs represents the orthogonal index of the IMFs after the original signal is decomposed by VMD, β = [K, α] is the optimization parameter combination; The steps of the GMPSO algorithm are as follows: (1) Initialize parameters such as particle position and velocity; (2) The particle position and velocity in the GMPSO algorithm are used as the parameter combination [K, α] in the VMD algorithm; (3) Use the GMPSO algorithm to find the optimal combination of VMD parameter combinations [K, α]; (4) Comparison of objective functions Update local and global extrema; (5) When the number of iterations does not reach the maximum number, the position of the particle reaches a local extreme value and does not meet the requirements. The GMPSO algorithm generates the next generation of particle positions and velocities with a mutation probability q, avoiding the occurrence of local extreme values ​​in the PSO algorithm. (6) When the maximum number of iterations is reached, stop the iteration and output the optimal parameter combination [K, α] of the VMD algorithm.

4. The DC signal ripple detection method based on the GMPSO-VMD-HT algorithm according to claim 1, characterized in that: The VMD algorithm after determining the optimal parameter combination decomposes the noise-removed power signal into a series of intrinsic mode functions (IMFs), including: Step 3.1: Construction of variational model: The original signal x(t) is decomposed into u k , and the decomposition sequence is guaranteed to be an intrinsic mode function (IMFs) with limited bandwidth at the center frequency, and the sum of the estimated bandwidths of the K IMF components is minimized. The constraint condition is that the sum of all IMF modal components is equal to the original signal x(t). The constructed variational model with constraint model is: where u k (t) is each mode, ω k (t) is the center frequency of each mode; Step 3.2: Solve the variational constraint problem: In order to solve the variational constraint problem, the quadratic penalty factor α to ensure the accuracy of signal reconstruction and the Lagrangian operator λ(t) to ensure the precise execution of the constraint are introduced, and the augmented Lagrangian function L is constructed to transform the constrained variational problem into an unconstrained variational problem. The augmented Lagrangian function is: The alternating vector multiplier method is used to solve the modal component u. k , center frequency ω k :

5. The DC signal ripple detection method based on the GMPSO-VMD-HT algorithm according to claim 1, characterized in that: Perform Hilbert transform (HT) on each intrinsic mode function to obtain the amplitude and frequency information of each ripple component, including: Based on a series of intrinsic mode functions (IMFs) obtained in step 3, they are expressed as: x(t)=u1(t)+u2(t)+···+u K (t) Through Hilbert transform, we can get each modal function u k (t) corresponds to the instantaneous amplitude a k (t) and frequency f k (t), its expression is: The corresponding analytical signal is constructed as follows: The amplitude function and phase function of the mode are: The instantaneous frequency of the mode f k (t) can be expressed by the phase function ψ k (t) Take the derivative and get:

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