A method and device for noise reduction of photovoltaic DC fault arc signal

Abstract

This invention provides a method and apparatus for denoising photovoltaic (PV) DC fault arc signals, relating to the field of PV DC fault arc diagnosis technology. The method includes: decomposing the PV DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the intrinsic mode components (EMCs) of the PV DC fault arc; calculating multivariate EMC indices, and classifying the PV DC fault arc EMCs into high-frequency noise components, low-frequency signal components, and noisy signal components based on the multivariate EMC indices; denoising the noisy signal components using an improved wavelet threshold to obtain an improved wavelet threshold denoised signal; and reconstructing the low-frequency signal components and the improved wavelet threshold denoised signal to obtain the denoised signal. This invention's denoising method effectively improves the denoising effect of the fault arc signal while preserving its key characteristics, thus significantly improving the accuracy of fault arc identification.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic DC fault arc diagnosis technology, and in particular to a method and device for denoising photovoltaic DC fault arc signals. Background Technology

[0002] In recent years, the country has vigorously supported the development of new energy sources, and my country's photovoltaic power generation capacity has grown rapidly. As of the end of June 2024, the national photovoltaic power generation reached 391.4 billion kilowatt-hours, a year-on-year increase of 47%, and the national photovoltaic power generation utilization rate reached 97%. With the popularization of photovoltaic systems, fire accidents caused by photovoltaic DC fault arcing have been increasing year by year, accounting for 52% of photovoltaic fires. Therefore, effective detection and identification of photovoltaic DC fault arcing is crucial for preventing photovoltaic system fires.

[0003] However, in real-world photovoltaic (PV) scenarios, current output is affected by environmental factors, causing oscillations. Increased cable length and current also increase noise, weakening the characteristics of fault arc signals and making it difficult to accurately identify PV DC fault arc signals. Therefore, noise reduction processing of PV DC fault arc signals is necessary. Existing signal denoising methods, such as EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), and VMD (Variational Mode Decomposition), all have certain drawbacks. For example, they suffer from mode aliasing, unstable number of IMF (Intrinsic Mode Functions) components, and reliance on empirical parameters. Furthermore, the decomposed IMF components lack scientific screening criteria to distinguish between noise and signal components, potentially leading to the removal of IMF components containing useful information or the retention of noisy IMF components, resulting in unsatisfactory noise reduction effects. Summary of the Invention

[0004] To address the unsatisfactory noise reduction effects of existing signal denoising methods, this invention provides a method and apparatus for denoising photovoltaic DC fault arc signals. The technical solution is as follows:

[0005] On the one hand, a method for denoising photovoltaic DC fault arc signals is provided. This method is implemented by a photovoltaic DC fault arc signal denoising device and includes:

[0006] S1. Obtain the photovoltaic DC fault arc signal.

[0007] S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the intrinsic mode components of the photovoltaic DC fault arc.

[0008] S3. Calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc. Based on the multivariate intrinsic mode index, the intrinsic mode components of the photovoltaic DC fault arc are divided into high-frequency noise components, low-frequency signal components, and noisy signal components.

[0009] S4. For noisy signal components that are less than the threshold of the multivariate intrinsic mode index, noise reduction is performed using an improved wavelet threshold to obtain an improved wavelet threshold denoised signal.

[0010] S5. The low-frequency signal components and the improved wavelet threshold denoising signal are reconstructed to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening index.

[0011] Optionally, the IMGO-ICEEMDAN decomposition method in S2 decomposes the photovoltaic DC fault arc signal to obtain the intrinsic mode components of the photovoltaic DC fault arc, including:

[0012] S21. Determine the white noise amplitude weight and the number of noise additions based on the improved moss growth algorithm IMGO. Add white noise to the photovoltaic DC fault arc signal based on the white noise amplitude weight and the number of noise additions to obtain the added fault arc signal.

[0013] S22. Based on the added fault arc signal, the first-order residual component r1 is calculated.

[0014] S23. Calculate the first-order modal component c1 based on the first-order residual component r1.

[0015] S24. Add white noise to the first-order residual component r1 and take the average value to calculate the second-order modal component c2.

[0016] S25. Based on the first-order residual component r1, the second-order residual component r2 is calculated; based on the second-order residual component r2, the third-order modal component c3 is calculated; and then the intrinsic modal components of the photovoltaic DC fault arc are obtained.

[0017] Optionally, in S21, determining the white noise amplitude weight and the number of noise additions based on the improved moss growth algorithm IMGO includes:

[0018] Based on the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm, and dual propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions, and determine the optimal white noise amplitude weight and the number of noise additions.

[0019] Optionally, the dynamic weight optimization and particle swarm optimization algorithms are as shown in equations (1) and (2) below:

[0020] v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i (1)

[0021]

[0022] In the formula, v i Let w represent the velocity of particle i, w represent the inertial weight, c1 represent the personal experience factor, r1 represent the random number, and gbest represent the velocity of particle i. i M represents the historical best position of particle i. i c2 represents the randomly generated position of the particle, c2 represents the global empirical factor, r2 represents the random number, and gbest i w represents the historical best position of all particles. max w represents the maximum value of the inertia weight. min This represents the minimum value of the inertia weight, it represents the current iteration number, and Maxits represents the maximum iteration number.

[0023] Optionally, the multivariate intrinsic modal indices for calculating the intrinsic modal components of photovoltaic DC fault arcs in S3 include:

[0024] The correlation coefficient, kurtosis, and variance contribution rate of each component in the intrinsic mode components of photovoltaic DC fault arc were calculated separately. The correlation coefficient, kurtosis, and variance contribution rate were normalized. The weighting method of the coefficient of variation was used to weight the normalized data, and the multivariate intrinsic mode index was obtained based on the weighted data.

[0025] Optionally, the improved wavelet threshold in S4 includes:

[0026] The db4 wavelet basis function was selected as the discrete wavelet transform wavelet basis function. A three-level wavelet decomposition was performed on the low-frequency signal component to obtain the high-frequency wavelet coefficients W. j,k According to the high-frequency wavelet coefficients W j,k The threshold t is calculated.

[0027] Based on the high-frequency wavelet coefficients W j,k And the threshold t, processed by an improved wavelet threshold function, yields the low-frequency wavelet coefficients.

[0028] Alternatively, the improved wavelet threshold function is shown in equation (3) below:

[0029]

[0030] In the formula, denoted as low-frequency wavelet coefficients, sign(·) represents the sign function, t represents the threshold, and α, β, and γ represent the adjustment factors of the threshold function.

[0031] On the other hand, a photovoltaic DC fault arc signal noise reduction device is provided, which is applied to the photovoltaic DC fault arc signal noise reduction method. The device includes:

[0032] The acquisition module is used to acquire photovoltaic DC fault arc signals.

[0033] The decomposition module is used to decompose the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the intrinsic mode components of the photovoltaic DC fault arc.

[0034] The calculation module is used to calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc. Based on the multivariate intrinsic mode index, the intrinsic mode components of the photovoltaic DC fault arc are divided into high-frequency noise components, low-frequency signal components, and noisy signal components.

[0035] The noise reduction module is used to reduce the noise of noisy signal components that are less than the threshold of the multivariate intrinsic mode index by using an improved wavelet threshold, so as to obtain an improved wavelet threshold denoised signal.

[0036] The output module is used to reconstruct the low-frequency signal components and the improved wavelet threshold denoising signal to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening index.

[0037] On the other hand, a photovoltaic DC fault arc signal noise reduction device is provided, the photovoltaic DC fault arc signal noise reduction device comprising: a processor; a memory, the memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods described above for photovoltaic DC fault arc signal noise reduction.

[0038] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described photovoltaic DC fault arc signal noise reduction methods.

[0039] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0040] This invention discloses a method for denoising photovoltaic DC fault arc signals based on IMGO-ICEEMDAN and multivariate intrinsic mode indexes. Because the output current of photovoltaic DC power supplies oscillates continuously with environmental changes, and the noise in the current signal increases with line length and current, fault characteristics are difficult to identify when a fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) decomposes the fault arc signal into k intrinsic mode components (IMFs) from high to low frequency. However, two key parameters (white noise amplitude weight (Nstd) and noise addition count (ME)) are difficult to determine accurately based on experience during the decomposition process. Therefore, this invention applies an improved moss growth algorithm (IMGO) to achieve intelligent optimization of these two key parameters. The IMGO algorithm improves global search capability and overall optimization effect, solving the problems of high computational complexity, slow convergence speed, and susceptibility to local optima in the MGO algorithm. Furthermore, IMGO-ICEEMDAN also solves the problems of mode aliasing, endpoint effects, and difficulty in parameter selection found in existing methods such as EMD, EEMD, and VMD. Based on this, the IMF components obtained from the decomposition are divided into high-frequency noise components and low-frequency signal components using multivariate intrinsic mode indicators. The noisy low-frequency signal components with values ​​below a threshold are then denoised using a wavelet denoising method with an improved threshold. Finally, the denoised signal is reconstructed from the processed IMF components. Compared to existing methods, the denoising method disclosed in this invention effectively improves the denoising effect of the fault arc signal while preserving the key features of the photovoltaic DC fault arc signal, thus significantly improving the accuracy of fault arc identification. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a photovoltaic DC fault arc signal noise reduction method provided by an embodiment of the present invention;

[0043] Figure 2 This is a diagram showing the decomposition effect of IMGO-ICEEMDAN provided in an embodiment of the present invention;

[0044] Figure 3 This is a D-curve diagram of the multivariate intrinsic modal indexes for different IMF components provided in the embodiments of the present invention;

[0045] Figure 4 This is a graph showing the variation of hyperparameters with the number of iterations provided in an embodiment of the present invention;

[0046] Figure 5 This is a graph showing the change of fitness value with the number of iterations provided in the embodiments of the present invention;

[0047] Figure 6 These are the soft thresholding function, hard thresholding function, and improved wavelet thresholding function provided in the embodiments of the present invention;

[0048] Figure 7 This is a diagram showing the noise reduction effect of the noise reduction method provided in this embodiment of the invention after noise reduction;

[0049] Figure 8 This is the original signal provided in the embodiments of the present invention;

[0050] Figure 9 This is a diagram showing the noise reduction effect of the EMD noise reduction method provided in this embodiment of the invention.

[0051] Figure 10 This is a diagram showing the noise reduction effect of the EEMD noise reduction method provided in this embodiment of the invention.

[0052] Figure 11 This is a diagram showing the noise reduction effect of the VMD noise reduction method provided in this embodiment of the invention.

[0053] Figure 12 This is a block diagram of a photovoltaic DC fault arc signal noise reduction device provided in an embodiment of the present invention;

[0054] Figure 13 This is a schematic diagram of the structure of a photovoltaic DC fault arc signal noise reduction device provided in an embodiment of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0056] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0057] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0058] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0059] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0060] This invention provides a method for denoising photovoltaic (PV) DC fault arc signal. This method can be implemented using a PV DC fault arc signal denoising device, which can be a terminal or a server. Figure 1 The flowchart shown is for a photovoltaic DC fault arc signal noise reduction method. The processing flow of this method may include the following steps:

[0061] S1. Obtain the photovoltaic DC fault arc signal.

[0062] In one feasible implementation, a real-world photovoltaic (PV) DC fault arc test platform is constructed to acquire real-world PV DC fault arc signals. The PV power supply consists of two strings of 32 PV power generation units. The single-string current output range is 3A-8A; the dual-string current output range is 8A-14A, and the voltage output range is 500V-510V. The load is a three-phase PV inverter of model R3-50k, with a maximum input voltage of 1100V, a maximum input current of 50A, and an MPPT operating voltage range of 180-1000V. The fault arc generator is controlled by a KH-01 stepper motor controller, which controls the stepper motor and thus the step distance when the fault arc occurs. The electrode material is a flat-headed tungsten rod with a diameter of 6.4mm. The data acquisition equipment uses a Tektronix MSO4 4-channel oscilloscope with a maximum ADC sampling rate of 6.25GS / s, a maximum recording length of 31.25M, and a maximum waveform capture rate >500kwfms / s, meeting the experimental data acquisition requirements. To simulate the circuit environment, fault and normal signals were collected at different time periods, with different cable lengths, different current levels, different inverters, and different arc locations.

[0063] S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the intrinsic mode components of the photovoltaic DC fault arc.

[0064] Optionally, step S2 above may include the following steps S21-S25:

[0065] S21. Determine the white noise amplitude weight and the number of noise additions based on the improved moss growth algorithm IMGO. Add white noise to the photovoltaic DC fault arc signal based on the white noise amplitude weight and the number of noise additions to obtain the added fault arc signal.

[0066] Optionally, in S21, determining the white noise amplitude weight and the number of noise additions based on the improved moss growth algorithm IMGO includes:

[0067] Based on the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm, and dual propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions, and determine the optimal white noise amplitude weight and the number of noise additions.

[0068] In one feasible implementation, the ICEEMDAN decomposition method is as follows:

[0069] (1) By adding Gaussian white noise to the photovoltaic DC fault arc signal to be decomposed, we can obtain:

[0070] x (i) =x+e1E1(ω (i) (i = 1, 2, ..., N) (1)

[0071] Where x (i) ω is the fault arc signal to be decomposed, x represents the original signal, e1 is the expected signal-to-noise ratio between the fault arc signal to be decomposed and the noise signal added during the first decomposition, and E1(.) is the first modal component after EMD decomposition; (i) This is the first Gaussian white noise added, and N represents the number of data points in the original signal.

[0072] (2) Calculate the first residual component:

[0073]

[0074] Where M(.) is the local mean function and N is the data length of the photovoltaic DC signal.

[0075] (3) Calculate the first mode component:

[0076] c1=x-r1 (3)

[0077] (4) Add a series of white noises to the residual components and take the average value to obtain the second-order modal components, as shown in the equation:

[0078]

[0079] (5) Calculate the residual components r of order k (k = 3, 4, 5, ..., N) according to equations (2) and (3). k and k-th order modal components c k :

[0080]

[0081] c k =r k-1 -r k (6)

[0082] (6) Repeat (5) until all modal components are obtained to complete the signal decomposition.

[0083] Furthermore, the mathematical model used in the IMGO intelligent parameter optimization algorithm is as follows:

[0084] (1) Mechanism for determining wind direction:

[0085] IMGO developed a creative mechanism for determining wind direction based on the following assumptions:

[0086] 1) The wind direction remains unchanged throughout the entire iteration process.

[0087] 2) Assume that the moss individual represents a position in the solution space, and the current best candidate position corresponds to the current moss individual in the optimal solution.

[0088] 3) The wind always blows from the area with more moss towards the individual moss in the most favorable growing environment.

[0089] The most unique individual in the population is called Mbest. The j-dimensional value of Mbest is used as a threshold and compared with the j-dimensional values ​​of all individuals:

[0090]

[0091] The function `count(.)` calculates the number of moss individuals in a given set. After multiple calculations, the following set can be obtained:

[0092]

[0093] In the formula, dn represents the number of times the data will be split, and the value of dn is set to [dim / 4] and not less than 1. j Let j represent the j-th random number, which satisfies the range (1,2,…,dim).

[0094] Since the wind always blows from the region divX to the specific individual Mbest, the wind direction calculation formula is as follows:

[0095]

[0096] Where D_wind represents the calculated wind direction, which has the same dimension as the individual. The variable num represents the total number of individuals in dirX. dirX can be calculated from equation (11). By calculating the average distance between the main individual and Mbest, the path of an individual approaching Mbest can be smoothed, thereby enhancing the optimization capability of IMGO.

[0097] dirX={M best -M i |M i ∈divX} (11)

[0098] (2) Dynamic weight optimization and particle swarm optimization:

[0099] By leveraging the global optimum and individual optimum guidance in the particle swarm optimization algorithm, the global search capability is improved. Through dynamic adjustment of inertia weights, a broad search is performed in the early stages of the algorithm, followed by a refined local search in the later stages, thus avoiding getting trapped in local optima. The specific formula is as follows:

[0100] v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i (12)

[0101]

[0102] In the formula, v i Let w represent the velocity of particle i, w represent the inertial weight, c1 represent the personal experience factor, and r1 represent a random number in the range [0,1]. pbest i M represents the historical best position of particle i. i c2 represents the randomly generated position of the particle, c2 represents the global empirical factor, and r2 represents a random number in the range [0,1]. gbest i w represents the historical best position of all particles. max w represents the maximum value of the inertia weight. min This represents the minimum value of the inertia weight, it represents the current iteration number, and Maxits represents the maximum iteration number.

[0103] (3) Spore dispersal search:

[0104] Most spores disperse under stable wind conditions, while a small fraction disperse under turbulent conditions. Eventually, as the wind weakens, the spores begin to settle closer to the moss. The location of the spores is determined by equation (13). The size difference between the two steps is significant. This allows individuals to make random selections to prevent a fixed step size from leading to slow convergence in the early stages and failure to converge in the later stages, thus ensuring population diversity.

[0105]

[0106] In the formula, This represents a new moss obtained by spreading spores from i moss individuals mi. r1 is a random number in the range (0,1). When r1>0.2, the spores disperse under stable wind conditions and under turbulent wind conditions. step1 is the spore dispersal distance under stable wind conditions, and step2 is the spore dispersal distance under turbulent wind conditions, as shown in the following formula:

[0107] step1 = 2·(r² - 0.5)·E (15)

[0108]

[0109] r2 is a random vector in the range (0,1) with the same dimension as D_wind. E is the wind intensity, which decreases with each iteration. r3 is a random vector in the range (0,1) with the same dimension as D_wind, as shown in the following formula:

[0110]

[0111] Where FEs represents the current number of calculations, MaxFEs represents the maximum number of iterations, and β represents the proportion of the total number in divX to the total number in X.

[0112] (4) Double propagation search:

[0113] Dual propagation search simulates both sexual and vegetative reproduction in mosses. Unlike traditional Mas search, it increases the proportion of methods that only change a single dimension, thus enhancing the overall local exploration capability.

[0114]

[0115] In the formula, This represents the i-th new individual. express The j-th particle in M, where j is a random number not exceeding the maximum dimension of the individual. best This represents the current optimal individual. Where j represents M. best The j-th particle in D_wind. jIt is the j-th particle in D_wind. r4 is a random number in the range (0,1). When r4>0.5, the dual reproduction search is simulated in the sexual reproduction stage, while a different calculation method is used to simulate the vegetative reproduction stage.

[0116]

[0117] Where r5 is a random vector in the range (0,1), and M best They have the same dimension. r6 is a random number in the range (0,1), and E is the wind force.

[0118] Using the aforementioned intelligent optimization algorithm, the white noise amplitude weight (Nstd) and noise addition frequency (ME) of ICEEMDAN are optimized and updated with the minimum envelope entropy function as the fitness function to determine the optimal parameter combination.

[0119] S3. Calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc. Based on the multivariate intrinsic mode index, the intrinsic mode components of the photovoltaic DC fault arc are divided into high-frequency noise components, low-frequency signal components, and noisy signal components.

[0120] In one feasible implementation, most existing intrinsic mode screening methods are based on a single evaluation index and experience. The multivariate intrinsic mode screening index proposed in this invention integrates three indicators that can reflect the difference between noise and signal: correlation coefficient ρ, kurtosis γ, and variance contribution rate λ, to distinguish between high-frequency noise and low-frequency signals.

[0121] Furthermore, based on the IMF components obtained from the IMGO-ICEEMDAN decomposition, the correlation coefficient ρ, kurtosis γ, and variance contribution rate λ of each IMF component are calculated. The correlation coefficient ρ reflects the linear correlation between the intrinsic mode component and the signal; the kurtosis γ reflects the sharpness of the signal and is sensitive to high-frequency noise; and the variance contribution rate λ can effectively identify noise in low-frequency signals. The weight of each index is determined by the coefficient of variation weighting method, and a comprehensive multivariate intrinsic mode index is obtained to screen high-frequency noise and low-frequency signals in the IMF components.

[0122] The specific calculation method is as follows:

[0123] 1. The correlation coefficient ρ, kurtosis γ, and variance contribution rate λ are calculated as follows:

[0124]

[0125] Where X and Y are the signals to be calculated and the original signals, respectively; μ is the mean of signal X, and σX and σY are the standard deviations of signals X and Y, respectively. After normalizing the correlation coefficient ρ, kurtosis γ, and variance contribution rate λ, ρ′, γ′, and λ′ are obtained. The coefficient of variation weighting method is used to weight the three normalized indicators:

[0126]

[0127] The multivariate intrinsic modal index D can be expressed as:

[0128] D=W ρ ×ρ′+W γ ×γ′+W λ ×λ′ (25)

[0129] The correlation coefficient ρ, kurtosis γ, and variance contribution rate λ are positively, negatively, and positively correlated with the signal's trend, respectively. To unify the trend, kurtosis γ is taken as negative. The multivariate intrinsic mode index D reaches a minimum point as the number of reconstruction layers increases, and this minimum point defines the boundary between noise and signal information.

[0130] First, the IMF components preceding the first minimum value of the multivariate intrinsic mode index D are determined to be pure noise components and should be discarded. Then, for the other retained IMF components, the threshold of the multivariate intrinsic mode index D is set to 0.2. If the multivariate intrinsic mode index D ≥ 0.2, the IMF component is considered to contain valid information and should be retained; if the multivariate intrinsic mode index D < 0.2, the IMF component is considered to contain some noise and requires further processing.

[0131] S4. For noisy signal components that are less than the threshold of the multivariate intrinsic mode index, noise reduction is performed using an improved wavelet threshold to obtain an improved wavelet threshold denoised signal.

[0132] Optionally, the improved wavelet threshold in S4 includes:

[0133] The db4 wavelet basis function was selected as the discrete wavelet transform wavelet basis function. A three-level wavelet decomposition was performed on the IMF components with a multivariate intrinsic mode index D < 0.2 to obtain the high-frequency wavelet coefficients W. j,k W j,k This represents the k-th wavelet coefficient in the detail coefficient group of the j-th layer of the wavelet decomposition.

[0134] Furthermore, using the universal threshold VisuShrink threshold, the threshold t is calculated:

[0135]

[0136] Where σ is the standard deviation of the noise, N is the signal length, and media(abs(W)) j,k )) represents the median amplitude of the high-frequency subband wavelet coefficients of the j-level wavelet decomposition, and 0.6745 is the adjustment coefficient for the noise standard deviation.

[0137] Furthermore, the wavelet coefficients W obtained based on the j-level wavelet decomposition j,k Low-frequency wavelet coefficients are obtained by processing with an improved wavelet threshold function. The improved wavelet threshold function calculation formula is as follows:

[0138]

[0139] Wherein, α, β, and γ are adjustment factors of the threshold function, α, β>0, 0<γ<1. By adjusting the adjustment factors, the problems of noise processing and signal distortion of the original soft and hard threshold functions are solved, thereby retaining more effective signals and improving the wavelet threshold noise reduction performance.

[0140] S5. The low-frequency signal components and the improved wavelet threshold denoising signal are reconstructed to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening index.

[0141] In one feasible implementation, the photovoltaic DC fault arc signal is decomposed using IMGO-ICEEMDAN, and the retained signal obtained through multivariate intrinsic mode indices is reconstructed into an IMF. s The signal obtained by wavelet denoising with improved threshold function is reconstructed into IMF. p , IMF s IMF p The final denoised signal S is obtained by reconstructing the signal from the residual R:

[0142] S = IMF s +IMF p +R (29)

[0143] To measure the noise reduction effect, three metrics are introduced: mean square error (MSE), signal-to-noise ratio (SNR), and waveform similarity coefficient (NCC). The smaller the MSE, the larger the SNR, and the closer the NCC is to 1, the better the noise reduction effect. The calculation formula is as follows:

[0144]

[0145] Where, x i The original signal, x′ i The noise-reduced signal, where N is the signal length.

[0146] The effectiveness of this method is verified through an example below, using a real photovoltaic scenario fault arc signal for noise reduction verification.

[0147] The photovoltaic DC fault arc signal collected in step S1 is input into IMGO-ICEEMDAN with a sample size of 2ms, and each sample contains 1000 points. According to step S2, the optimization range of noise amplitude weight is set to [0.15, 0.6], the optimization range of noise addition number is set to [50, 600], the maximum number of iterations is set to 10, and the population size is set to 4. The photovoltaic DC fault arc signal is then decomposed using IMGO-ICEEMDAN, and the decomposition results are as follows: Figure 2 As shown.

[0148] It can be clearly seen that this method decomposes the photovoltaic DC fault signal into eight IMF components and a residual R. The high-frequency and low-frequency components are clearly separated, and there is no mode mixing phenomenon. According to step S3, the multivariate intrinsic mode index D of each IMF is calculated, and the calculation results are as follows: Figure 3 , Figure 4 As shown, the hyperparameters and fitness values ​​of the algorithm optimization change with the number of iterations. Figure 5 As shown.

[0149] According to the screening criteria, IMF1-2 are considered pure noise components and should be removed. Of the remaining IMF components, IMF4 (with D ≥ 0.2) is retained. For IMF3 and IMF5-8 (with D < 0.2), improved wavelet thresholding denoising is performed according to step S4. The function graph based on the improved thresholding method in this method is shown below. Figure 6 As shown in Table 1, the evaluation indicators for noise reduction processing of different IMF components are as follows:

[0150] Table 1

[0151]

[0152]

[0153] According to step S5, IMF s IMF p Reconstructing with the residual R, we obtain Figure 7 , Figure 8 The original signal, Figure 9 , Figure 10 , Figure 11 The figures show the noise reduction effects of EMD, EEMD, and VMD methods, respectively. Table 2 shows the noise reduction evaluation indicators of photovoltaic DC fault arc signals based on different noise reduction methods.

[0154] Table 2

[0155] Noise reduction methods SNR MSE NCC EMD 42.70 0.001296 1 EEMD 51.61 0.00016 1 VMD 52.57 0.000134 1 This invention 53.33 0.000112 1

[0156] This invention discloses a method for denoising photovoltaic DC fault arc signals based on IMGO-ICEEMDAN and multivariate intrinsic mode indexes. Because the output current of photovoltaic DC power supplies oscillates continuously with environmental changes, and the noise in the current signal increases with line length and current, fault characteristics are difficult to identify when a fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) decomposes the fault arc signal into k intrinsic mode components (IMFs) from high to low frequency. However, two key parameters (white noise amplitude weight (Nstd) and noise addition count (ME)) are difficult to determine accurately based on experience during the decomposition process. Therefore, this invention applies an improved moss growth algorithm (IMGO) to achieve intelligent optimization of these two key parameters. The IMGO algorithm improves global search capability and overall optimization effect, solving the problems of high computational complexity, slow convergence speed, and susceptibility to local optima in the MGO algorithm. Furthermore, IMGO-ICEEMDAN also solves the problems of mode aliasing, endpoint effects, and difficulty in parameter selection found in existing methods such as EMD, EEMD, and VMD. Based on this, the IMF components obtained from the decomposition are divided into high-frequency noise components and low-frequency signal components using multivariate intrinsic mode indicators. The noisy low-frequency signal components with values ​​below a threshold are then denoised using a wavelet denoising method with an improved threshold. Finally, the denoised signal is reconstructed from the processed IMF components. Compared to existing methods, the denoising method disclosed in this invention effectively improves the denoising effect of the fault arc signal while preserving the key features of the photovoltaic DC fault arc signal, thus significantly improving the accuracy of fault arc identification.

[0157] Figure 12 This is a block diagram illustrating a photovoltaic DC fault arc signal noise reduction device according to an exemplary embodiment. The device is used in a photovoltaic DC fault arc signal noise reduction method. (Refer to...) Figure 12 The device includes an acquisition module 310, a decomposition module 320, a calculation module 330, a noise reduction module 340, and an output module 350.

[0158] in:

[0159] The acquisition module 310 is used to acquire photovoltaic DC fault arc signals.

[0160] The decomposition module 320 is used to decompose the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the intrinsic mode components of the photovoltaic DC fault arc.

[0161] The calculation module 330 is used to calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc. Based on the multivariate intrinsic mode index, the intrinsic mode components of the photovoltaic DC fault arc are divided into high-frequency noise components, low-frequency signal components and noisy signal components.

[0162] The noise reduction module 340 is used to reduce the noise of noisy signal components that are less than the threshold of the multivariate intrinsic mode index by using an improved wavelet threshold, so as to obtain an improved wavelet threshold denoised signal.

[0163] Output module 350 is used to reconstruct low-frequency signal components and improved wavelet threshold denoising signals to obtain denoised signals based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indexes.

[0164] This invention discloses a method for denoising photovoltaic DC fault arc signals based on IMGO-ICEEMDAN and multivariate intrinsic mode indexes. Because the output current of photovoltaic DC power supplies oscillates continuously with environmental changes, and the noise in the current signal increases with line length and current, fault characteristics are difficult to identify when a fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) decomposes the fault arc signal into k intrinsic mode components (IMFs) from high to low frequency. However, two key parameters (white noise amplitude weight (Nstd) and noise addition count (ME)) are difficult to determine accurately based on experience during the decomposition process. Therefore, this invention applies an improved moss growth algorithm (IMGO) to achieve intelligent optimization of these two key parameters. The IMGO algorithm improves global search capability and overall optimization effect, solving the problems of high computational complexity, slow convergence speed, and susceptibility to local optima in the MGO algorithm. Furthermore, IMGO-ICEEMDAN also solves the problems of mode aliasing, endpoint effects, and difficulty in parameter selection found in existing methods such as EMD, EEMD, and VMD. Based on this, the IMF components obtained from the decomposition are divided into high-frequency noise components and low-frequency signal components using multivariate intrinsic mode indicators. The noisy low-frequency signal components with values ​​below a threshold are then denoised using a wavelet denoising method with an improved threshold. Finally, the denoised signal is reconstructed from the processed IMF components. Compared to existing methods, the denoising method disclosed in this invention effectively improves the denoising effect of the fault arc signal while preserving the key features of the photovoltaic DC fault arc signal, thus significantly improving the accuracy of fault arc identification.

[0165] Figure 13 This is a schematic diagram of the structure of a photovoltaic DC fault arc signal noise reduction device provided in an embodiment of the present invention, as shown below. Figure 13 As shown, the photovoltaic DC fault arc signal noise reduction device may include the above-mentioned Figure 12 The photovoltaic DC fault arc signal noise reduction device shown is optionally included in the photovoltaic DC fault arc signal noise reduction device 410, which may include a first processor 2001.

[0166] Optionally, the photovoltaic DC fault arc signal noise reduction device 410 may also include a memory 2002 and a transceiver 2003.

[0167] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0168] The following is combined Figure 13 A detailed introduction to each component of the photovoltaic DC fault arc signal noise reduction device 410 is provided below:

[0169] The first processor 2001 is the control center of the photovoltaic DC fault arc signal noise reduction device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0170] Optionally, the first processor 2001 can perform various functions of the photovoltaic DC fault arc signal noise reduction device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0171] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 13 CPU0 and CPU1 are shown in the diagram.

[0172] In a specific implementation, as one example, the photovoltaic DC fault arc signal noise reduction device 410 may also include multiple processors, such as... Figure 13 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0173] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0174] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the photovoltaic DC fault arc signal noise reduction device 410. Figure 13 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0175] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0176] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 13 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0177] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected to the interface circuit of the photovoltaic DC fault arc signal noise reduction device 410. Figure 13 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0178] It should be noted that, Figure 13 The structure of the photovoltaic DC fault arc signal noise reduction device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0179] Furthermore, the technical effect of the photovoltaic DC fault arc signal noise reduction device 410 can be referred to the technical effect of the photovoltaic DC fault arc signal noise reduction method described in the above method embodiments, and will not be repeated here.

[0180] It should be understood that the first processor 2001 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0181] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0182] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0183] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0184] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0185] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0187] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0188] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0191] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0192] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for noise reduction of photovoltaic DC fault arc signals, characterized in that, The method includes: S1. Obtain the photovoltaic DC fault arc signal; S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the intrinsic mode components of the photovoltaic DC fault arc. S3. Calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc, and divide the intrinsic mode components of the photovoltaic DC fault arc into high-frequency noise components, low-frequency signal components and noisy signal components according to the multivariate intrinsic mode index. S4. For noisy signal components that are less than the threshold of the multivariate intrinsic mode index, noise reduction is performed by improving the wavelet threshold to obtain the improved wavelet threshold denoised signal. S5. Reconstruct the low-frequency signal components and the improved wavelet threshold denoising signal to obtain a denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening index. The IMGO-ICEEMDAN decomposition method in S2 decomposes the photovoltaic DC fault arc signal to obtain the intrinsic mode components of the photovoltaic DC fault arc, including: S21. Determine the white noise amplitude weight and the number of times noise is added according to the improved moss growth algorithm IMGO. Add white noise to the photovoltaic DC fault arc signal according to the white noise amplitude weight and the number of times noise is added to obtain the fault arc signal after addition. S22. Calculate the first-order residual component r1 based on the added fault arc signal; S23. Calculate the first-order modal component c1 based on the first-order residual component r1; S24. Add white noise to the first-order residual component r1 and take the average value to calculate the second-order modal component c2. S25. Based on the first-order residual component r1, the second-order residual component r2 is calculated; based on the second-order residual component r2, the third-order modal component c3 is calculated; and then the intrinsic modal components of the photovoltaic DC fault arc are obtained. The step S21, which determines the white noise amplitude weight and the number of noise additions based on the improved moss growth algorithm IMGO, includes: Based on the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm, and dual propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions, and determine the optimal white noise amplitude weight and the number of noise additions. The improved wavelet threshold in S4 includes: The db4 wavelet basis function was selected as the discrete wavelet transform wavelet basis function, and the low-frequency signal component was decomposed into three levels of wavelet coefficients W to obtain the high-frequency wavelet coefficients. j,k According to the high-frequency wavelet coefficients W j，k The threshold t is calculated; Based on the high-frequency wavelet coefficients W j，k And the threshold t, processed by an improved wavelet threshold function, yields the low-frequency wavelet coefficients. The improved wavelet threshold function is shown in equation (3) below: In the formula, denoted as low-frequency wavelet coefficients, sign(·) represents the sign function, t represents the threshold, and α, β, and γ represent the adjustment factors of the threshold function.

2. The method for denoising photovoltaic DC fault arc signals according to claim 1, characterized in that, The dynamic weight optimization and particle swarm optimization algorithms are shown in equations (1) and (2) below: v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i ) (1) In the formula, v i Let w represent the velocity of particle i, w represent the inertial weight, c1 represent the personal experience factor, r1 represent the random number, and pbest represent the velocity of particle i. i M represents the historical best position of particle i. i c2 represents the randomly generated position of the particle, c2 represents the global empirical factor, r2 represents the random number, and gbest i w represents the historical best position of all particles. max w represents the maximum value of the inertia weight. min This represents the minimum value of the inertia weight, it represents the current iteration number, and Maxits represents the maximum iteration number.

3. The method for denoising photovoltaic DC fault arc signals according to claim 1, characterized in that, The multivariate intrinsic mode index for calculating the intrinsic mode components of photovoltaic DC fault arc in S3 includes: The correlation coefficient, kurtosis, and variance contribution rate of each component in the intrinsic mode components of photovoltaic DC fault arc are calculated separately. The correlation coefficient, kurtosis, and variance contribution rate are normalized. The normalized data are weighted using the coefficient of variation weighting method. The multivariate intrinsic mode index is obtained based on the weighted data.

4. A photovoltaic DC fault arc signal noise reduction device, wherein the photovoltaic DC fault arc signal noise reduction device is used to implement the photovoltaic DC fault arc signal noise reduction method as described in any one of claims 1-3, characterized in that, The device includes: The acquisition module is used to acquire photovoltaic DC fault arc signals; The decomposition module is used to decompose the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the intrinsic mode components of the photovoltaic DC fault arc. The calculation module is used to calculate the multivariate intrinsic mode index of the intrinsic mode components of the photovoltaic DC fault arc, and to divide the intrinsic mode components of the photovoltaic DC fault arc into high-frequency noise components, low-frequency signal components and noisy signal components according to the multivariate intrinsic mode index. The noise reduction module is used to reduce the noise of noisy signal components that are less than the threshold of the multivariate intrinsic mode index by using an improved wavelet threshold, so as to obtain the improved wavelet threshold denoised signal. The output module is used to reconstruct the low-frequency signal components and the improved wavelet threshold denoising signal to obtain a denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening index.

5. A photovoltaic DC fault arc signal noise reduction device, characterized in that, The photovoltaic DC fault arc signal noise reduction device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 3.

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