Photovoltaic direct current arc fault detection method, device, equipment, medium and product

Abstract

The invention relates to the technical field of electronics, and discloses a photovoltaic direct-current arc fault detection method, device, equipment, medium and product, the method uses a preset empirical wavelet transform method to process a current signal to obtain multiple groups of modal components, the modal component signals can more accurately reflect the real characteristics of the current signal, and the detection accuracy is improved. And singular value decomposition is carried out on the multiple groups of modal components to obtain noise reduction signals, so that noise signals in the current signals are effectively eliminated, and interference on arc fault detection caused by existence of the noise signals is reduced. Calculating a first target value of the plurality of feature indicators based on the noise reduction signal; and whether the photovoltaic system has an arc fault is judged based on the first target value of each characteristic index and the preset threshold corresponding to each characteristic index, an arc fault detection result of the photovoltaic system is obtained, and the accuracy and effectiveness of arc fault detection of the photovoltaic system are effectively ensured.

Description

Technical Field

[0001] The present invention relates to the field of electronic technologies, and particularly to a photovoltaic DC arc fault detection method, device, equipment, medium and product. Background Art

[0002] During the operation of a series-connected photovoltaic system, DC arc faults may occur due to factors such as line aging and poor contact, which may further cause system failures and even electrical fires. Compared with AC arc faults, DC arc faults do not have a zero-crossing point, and once an arc is generated, it is very difficult to extinguish. At the same time, DC arc faults do not have obvious waveform distortions, which makes it more difficult to detect DC arcs.

[0003] The practical detection research on DC arc faults in photovoltaic systems mainly falls into two categories. One is to detect the physical characteristics when an arc is generated, such as electromagnetic radiation, arc light, and arc sound. Such detection methods have high requirements for sensors, a small detection range, high costs, and are easily interfered, and are generally only used in specific switch cabinets. The other is to detect the current changes when an arc is generated. When an arc is generated, the current will show varying degrees of distortion and an increase in harmonic noise. Such detection methods directly analyze the current signal of the photovoltaic system to determine whether there is an arc fault in the photovoltaic system. This method has low requirements for sensors and strong economy, and is the current mainstream detection method. However, a photovoltaic grid-connected power generation system is easily interfered by the external environment and power electronic devices, resulting in many noise signals in the current signal. The existence of these noise signals will interfere with the detection of arc faults. Moreover, in the initial stage of the generation of a photovoltaic DC arc, the injection of noise harmonics may not be obvious, resulting in extremely strong concealment of the arc fault, making it difficult to detect and easily causing misjudgment of the detection system. Summary of the Invention

[0004] In view of this, the present invention provides a photovoltaic DC arc fault detection method, device, equipment, medium and product to solve the problem in the related art that the noise in the current signal of the photovoltaic system interferes with the detection of arc faults.

[0005] In a first aspect, the present invention provides a photovoltaic DC arc fault detection method, which includes: obtaining a current signal at the end of a photovoltaic string branch in a photovoltaic system; processing the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components; performing singular value decomposition on the multiple groups of modal components to obtain a noise-reduced signal; calculating first target values of multiple characteristic indexes for the noise-reduced signal; and judging whether there is an arc fault in the photovoltaic system based on the first target values of the characteristic indexes and preset thresholds corresponding to the characteristic indexes to obtain an arc fault detection result of the photovoltaic system.

[0006] The photovoltaic DC arc fault detection method provided by the present invention processes the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components. The modal component signals can more accurately reflect the true characteristics of the current signal. The singular value decomposition is performed on the multiple groups of modal components to obtain a denoised signal, effectively realizing the elimination of the noise signal in the current signal and reducing the interference caused by the presence of the noise signal to the arc fault detection. Calculate the first target values of multiple characteristic indexes based on the denoised signal; determine whether there is an arc fault in the photovoltaic system based on the first target values of each characteristic index and the preset thresholds corresponding to each characteristic index, and obtain the arc fault detection result of the photovoltaic system, effectively ensuring the accuracy and effectiveness of the arc fault detection of the photovoltaic system.

[0007] In an alternative embodiment, the step of processing the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components includes: performing Fourier transform on the current signal to obtain the Fourier single-sided spectrum of the current signal; mapping the Fourier single-sided spectrum to a preset range to obtain a target spectrum; dividing the target spectrum by using target boundary values to obtain a first spectrum and a second spectrum; respectively determining a target number of maximum points in the first spectrum and the second spectrum; using the minimum points between two adjacent maximum points in the first spectrum as the segmentation boundary to divide the first spectrum to obtain a target number of first spectrum intervals; using the minimum points between two adjacent maximum points in the second spectrum as the segmentation boundary to divide the second spectrum to obtain a target number of second spectrum intervals; constructing the empirical scaling function and empirical wavelet function of each first spectrum interval and the empirical scaling function and empirical wavelet function of each second spectrum interval; determining the modal component signals corresponding to the corresponding first spectrum intervals based on the empirical scaling function and empirical wavelet function of each first spectrum interval, and determining the modal component signals corresponding to the corresponding second spectrum intervals based on the empirical scaling function and empirical wavelet function of each second spectrum interval; using the modal component signals corresponding to different first spectrum intervals and the modal component signals corresponding to different second spectrum intervals as multiple groups of modal components.

[0008] In an alternative embodiment, the step of performing singular value decomposition on multiple groups of modal components to obtain a denoised signal includes: constructing a Hankel matrix based on the multiple groups of modal components; performing singular value decomposition on the Hankel matrix to obtain a denoised signal.

[0009] The method provided by this alternative embodiment constructs a Hankel matrix through multiple groups of modal components, performs singular value decomposition processing on the Hankel matrix to obtain a denoised signal, effectively removing the DC component, inverter switching frequency component and its multiple frequency components in the current signal, and maximizing the retention of the arc fault characteristics in the current signal.

[0010] In an alternative embodiment, the steps of constructing a Hankel matrix based on multiple sets of modal components include: determining a one-dimensional discrete digital signal based on multiple sets of modal components; performing segmentation processing on the one-dimensional discrete digital signal to obtain a preset number of segments of signals, where each segment of signal contains a preset number of data points; and constructing a Hankel matrix using the preset number of segments of signals.

[0011] The method provided in this alternative embodiment determines a one-dimensional discrete digital signal based on multiple sets of modal components, performs segmentation processing on the one-dimensional discrete digital signal to obtain a preset number of segments of signals, and constructs a Hankel matrix using the preset number of segments of signals, effectively reducing the complexity of constructing the Hankel matrix, reducing the computational cost, and improving the computational efficiency in arc fault detection.

[0012] In an alternative embodiment, the steps of determining whether there is an arc fault in a photovoltaic system based on the first target values of each characteristic index and the preset threshold corresponding to each characteristic index to obtain the arc fault detection result of the photovoltaic system include: obtaining the first interval and the second interval of each characteristic index; if there is a first target value of a characteristic index greater than the corresponding preset threshold, determining that there is an arc fault in the photovoltaic system; if there is no arc fault in the current signal, determining whether the first target value of each characteristic index is located in the first interval of the corresponding characteristic index to obtain a judgment result; determining a target characteristic index among multiple characteristic indexes based on the judgment result, where the first target value of the target characteristic index is located in the first interval of the corresponding characteristic index; calculating the second target value of the target characteristic index based on the first current component and calculating the third target value of the target characteristic index based on the second current component; calculating a target ratio based on the second target value and the third target value of the target characteristic index; and if the target ratio is located in the second interval of the target characteristic index, determining that there is an arc fault in the photovoltaic system.

[0013] The method provided in this alternative embodiment, to prevent missed or misjudged of atypical arc faults, performs multiple detections on samples with unobvious characteristic indexes, and realizes reliable distinction between atypical arc faults and normal disturbance signals by using the ratio of high and low frequency characteristic indexes.

[0014] In an alternative embodiment, the method further includes: fusing the first target values of multiple characteristic indexes using a preset fusion method to obtain a fusion characteristic value; and performing arc fault judgment on the current signal based on the fusion characteristic value.

[0015] In a second aspect, the present invention provides a photovoltaic DC arc fault detection device, which includes: an acquisition module for acquiring a current signal at the end of a photovoltaic string branch in a photovoltaic system; a processing module for processing the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components; a decomposition module for performing singular value decomposition on the multiple groups of modal components to obtain a denoised signal; a calculation module for calculating a first target value of multiple characteristic indicators for the denoised signal; and a judgment module for judging whether there is an arc fault in the photovoltaic system based on the first target value of each characteristic indicator and a preset threshold corresponding to each characteristic indicator, so as to obtain an arc fault detection result of the photovoltaic system.

[0016] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the photovoltaic DC arc fault detection method according to the first aspect or any corresponding embodiment thereof.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the photovoltaic DC arc fault detection method according to the first aspect or any corresponding embodiment thereof.

[0018] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the photovoltaic DC arc fault detection method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 is a flowchart of a photovoltaic DC arc fault detection method according to an embodiment of the present invention; Figure 2 is a schematic diagram of current waveforms of a typical arc and an atypical arc in an embodiment of the present invention; Figure 3 is a schematic diagram of spectra of a typical arc current and an atypical arc current in an embodiment of the present invention; Figure 4 is a flowchart of another photovoltaic DC arc fault detection method according to an embodiment of the present invention; Figure 5It is a schematic flowchart of yet another photovoltaic DC arc fault detection method according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the current signal noise reduction process in an embodiment of the present invention; Figure 7 It is a noise reduction waveform diagram of the current signal in an embodiment of the present invention; Figure 8a It is a schematic flowchart of arc detection based on an energy index in an embodiment of the present invention; Figure 8b It is a schematic flowchart of arc detection based on a peak-to-peak value index in an embodiment of the present invention; Figure 8c It is a schematic flowchart of arc detection based on a kurtosis factor index in an embodiment of the present invention; Figure 9 It is a structural block diagram of a photovoltaic DC arc fault detection device according to an embodiment of the present invention; Figure 10 It is a schematic diagram of the hardware structure of a computer device in an embodiment of the present invention. Detailed implementation manners

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] In related technologies, a photovoltaic grid-connected power generation system is vulnerable to interference from the external environment and power electronic devices, resulting in many noise signals in the current signal. The existence of these noise signals will cause interference to arc fault detection. Moreover, in the initial stage of the generation of a photovoltaic DC arc, the injection of noise harmonics may not be obvious, making the arc fault highly concealed, difficult to detect, and prone to misjudgment of the detection system.

[0023] In view of this, a photovoltaic DC arc fault detection method provided by an embodiment of the present application can be applied to a server to implement DC arc fault detection of a photovoltaic system. The method provided by the embodiment of the present application processes a current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components. The modal component signals can more accurately reflect the true characteristics of the current signal. Singular value decomposition is performed on the multiple groups of modal components to obtain a noise-reduced signal, effectively realizing the elimination of noise signals in the current signal and reducing the interference caused by the presence of noise signals to arc fault detection. Calculate the first target values of multiple characteristic indicators based on the noise-reduced signal; determine whether there is an arc fault in the photovoltaic system based on the first target values of each characteristic indicator and the preset threshold corresponding to each characteristic indicator, and obtain the arc fault detection result of the photovoltaic system, effectively ensuring the accuracy and effectiveness of arc fault detection in the photovoltaic system.

[0024] According to an embodiment of the present invention, an embodiment of a photovoltaic DC arc fault detection method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0025] In this embodiment, a photovoltaic DC arc fault detection method is provided, which can be used for the above-mentioned server. Figure 1 It is a flowchart of a photovoltaic DC arc fault detection method according to an embodiment of the present invention, as Figure 1 shown, and the process includes the following steps: Step S101, obtain the current signal at the end of the photovoltaic string branch in the photovoltaic system.

[0026] Exemplarily, the photovoltaic system may be a photovoltaic power generation system that requires arc fault detection. In the embodiments of the present application, the photovoltaic system may include, but is not limited to, a string photovoltaic system. The end of the photovoltaic string branch refers to the end away from the inverter or the busbar box in the photovoltaic string circuit. The collected current signal can reflect the operating state of the photovoltaic system. In the method provided by the embodiments of the present application, the sampling frequency of the current signal is 100 kHz, and 1024 current data points are collected as a group of current signals. The collected current signal may be a normal current signal or a fault arc fault current signal. The arc fault current signal includes a typical arc fault current signal and an atypical arc fault current signal. The normal current signal includes a steady-state operating current signal, a photovoltaic system dynamic adjustment current signal, and an inverter start-stop current signal. According to the noise mutation and spectral characteristics of the arc, the arc is divided into a typical arc and an atypical arc. The generation of the arc is roughly divided into three stages - initiation, development, and stabilization. In these three stages, obvious noise and instantaneous mutations will occur in the arc current. However, in the case of a small current and a small distance between the two poles of the arc, these noise mutations will have a more obvious intermittent phenomenon in the initiation and development stages of the arc, mainly manifested as the current showing intermittent smoothing and a reduction in mutations. At this time, the arc is an atypical arc. The current waveforms of the typical arc and the atypical arc are as shown in Figure 2 shown, and the spectra of the typical arc current and the atypical arc current are as shown in Figure 3 shown.

[0027] Step S102: Process the current signal using a preset empirical wavelet transform method to obtain multiple groups of modal components.

[0028] Exemplarily, in the embodiments of the present application, the improved empirical wavelet transform is used to decompose the current signal to achieve precise segmentation of the current noise spectrum. The preset empirical wavelet transform method can also be called the empirical wavelet transform (EWT), which is a new adaptive signal decomposition method. EWT is based on wavelet transform and empirical mode decomposition (EMD) and performs corresponding decomposition according to the information contained in the signal. The adaptive segmentation function of the empirical wavelet transform is used to decompose the current signal to achieve precise segmentation of the DC component, the switching frequency component of the inverter, and its harmonic components in a relatively narrow frequency spectrum range of the current signal.

[0029] Step S103: Perform singular value decomposition on the multiple groups of modal components to obtain a denoised signal.

[0030] Exemplarily, singular value decomposition (SVD) is an important matrix decomposition method. By using singular value decomposition to accurately denoise the current signal, the distinguishability between the normal current signal and the fault current signal can be increased. In the embodiments of the present application, after the current signal is decomposed by empirical wavelet transform, multiple sets of modal components obtained after decomposition are arranged together to form a matrix, and then the singular values representing the DC component, the inverter switching frequency component and its multiple frequency components in this matrix are set to zero by using improved singular value decomposition, so as to achieve the purpose of eliminating the noise affecting the arc fault diagnosis in the current signal.

[0031] Step S104, calculate the first target values of multiple characteristic indexes for the denoised signal.

[0032] Exemplarily, in the embodiments of the present application, the multiple characteristic indexes may include but are not limited to the energy index, the peak-to-peak value index, and the kurtosis factor index. The first target values corresponding to the energy index, the peak-to-peak value index, and the kurtosis factor index are calculated through the denoised signal.

[0033] Step S105, based on the first target values of each characteristic index and the preset threshold corresponding to each characteristic index, determine whether there is an arc fault in the photovoltaic system, and obtain the arc fault detection result of the photovoltaic system.

[0034] Exemplarily, the preset threshold can be determined based on the fluctuation range of the characteristic indexes of the normal current signal. The embodiments of the present application do not limit the specific content of the preset threshold, and those skilled in the art can determine it according to needs. In the embodiments of the present application, if the first target value of any characteristic index is greater than the preset threshold, it can be determined that the current signal is an arc fault current signal and there is an arc fault in the photovoltaic system.

[0035] The photovoltaic DC arc fault detection method provided in this embodiment uses a preset empirical wavelet transform method to process the current signal to obtain multiple sets of modal components. The modal component signals can more accurately reflect the true characteristics of the current signal. The multiple sets of modal components are subjected to singular value decomposition to obtain a denoised signal, effectively realizing the elimination of the noise signal in the current signal and reducing the interference caused by the existence of the noise signal to the arc fault detection. Calculate the first target values of multiple characteristic indexes based on the denoised signal; based on the first target values of each characteristic index and the preset threshold corresponding to each characteristic index, determine whether there is an arc fault in the photovoltaic system, and obtain the arc fault detection result of the photovoltaic system, effectively ensuring the accuracy and effectiveness of the arc fault detection of the photovoltaic system.

[0036] In this embodiment, a photovoltaic DC arc fault detection method is provided, which can be used for the above-mentioned server. Figure 4 It is a flowchart of the photovoltaic DC arc fault detection method according to the embodiments of the present invention, asFigure 4 As shown in the figure, the process includes the following steps: Step S401: Obtain the current signal at the end of the photovoltaic string branch in the photovoltaic system. For details, please refer to Figure 1 Step S101 of the embodiment shown, which will not be elaborated here.

[0037] Step S402: Process the current signal using a preset empirical wavelet transform method to obtain multiple groups of modal components.

[0038] Exemplarily, the adaptive segmentation function of the empirical wavelet transform is used to decompose the current signal to achieve precise segmentation of the DC component, the switching frequency component of the inverter, and its harmonic components in a relatively narrow frequency spectrum range. However, the interference of the arc noise signal in this process will cause the frequency spectrum segmentation interval of the inverter switching frequency component and its harmonic components to be too wide, making it difficult to accurately denoise the inverter switching frequency and its harmonic noise in the subsequent singular value decomposition denoising process. Therefore, it is necessary to improve the empirical wavelet transform. In order to be able to remove the inverter switching frequency component and its harmonic components in the frequency spectrum, it is necessary to improve the method of finding the maximum value of the empirical wavelet transform.

[0039] Specifically, the above step S402 includes: Step S4021: Perform a Fourier transform on the current signal to obtain the unilateral Fourier spectrum of the current signal. Exemplarily, the time-domain current signal is Fourier-transformed to obtain the unilateral Fourier spectrum of the signal.

[0040] Step S4022: Map the unilateral Fourier spectrum to a preset range to obtain the target frequency spectrum.

[0041] Exemplarily, the preset range may include but is not limited to , in the embodiment of the present application, the time-domain current signal is Fourier-transformed to obtain the unilateral Fourier spectrum of the signal, and it is mapped to range.

[0042] Step S4023: Divide the target frequency spectrum using the target boundary value to obtain the first frequency spectrum and the second frequency spectrum.

[0043] Exemplarily, the target boundary value may include but is not limited to , in the embodiment of the present application, the frequency spectrum is divided into two parts with as the boundary and , where the first frequency spectrum may be the frequency spectrum corresponding to the interval , and the second frequency spectrum may be the frequency spectrum corresponding to the interval . Among them, corresponds to in the unilateral Fourier spectrum.

[0044] Step S4024: Determine a target number of maximum points in the first spectrum and the second spectrum respectively.

[0045] Exemplarily, the target number can be any number. The embodiments of the present application do not limit the specific content of the target number, and those skilled in the art can determine it according to requirements. In the embodiments of the present application, the target number may include but is not limited to M / 2. Search for maximum points within the and spectrum ranges respectively. Assume that the preset number of spectrum divisions is M (M is an even number greater than 2), then search for M / 2 maximum points within the spectrum and ranges respectively.

[0046] Step S4025: Use the minimum points between two adjacent maximum points in the first spectrum as the segmentation boundaries to divide the first spectrum, obtaining a target number of first spectrum intervals.

[0047] Exemplarily, in the embodiments of the present application, the method of local maximum and minimum (Locmaxmin) is used, and the minimum value between adjacent maximum values is used as the segmentation boundary. Let be the set of minimum values between the nth maximum value and the (n + 1)th maximum value. Then the boundary division formula of the first spectrum is shown as the following formula:

[0048] where , and the meanings of the remaining variables are not elaborated here.

[0049] Use the minimum points between two adjacent maximum points in the first spectrum as the segmentation boundaries to divide the first spectrum, obtaining M / 2 first spectrum intervals.

[0050] Step S4026: Use the minimum points between two adjacent maximum points in the second spectrum as the segmentation boundaries to divide the second spectrum, obtaining a target number of second spectrum intervals.

[0051] Exemplarily, in the embodiments of the present application, the method of local maximum and minimum (Locmaxmin) is used, and the minimum value between adjacent maximum values is used as the segmentation boundary. Let be the m th maximum value and the m +1 th maximum value between the minimum value set. Then the boundary division formula of the second spectrum is shown as the following formula:

[0052] where , and the meanings of the remaining variables are not elaborated here.

[0053] The minimum point between two adjacent maximum points in the second spectrum is used as the segmentation boundary to divide the second spectrum, obtaining M / 2 first spectrum intervals.

[0054] Step S4027: Construct the empirical scaling function and empirical wavelet function for each first spectrum interval, and the empirical scaling function and empirical wavelet function for each second spectrum interval.

[0055] Exemplarily, in the embodiments of the present application, for M divided intervals, M groups of empirical scaling functions and empirical wavelet functions are respectively constructed.

[0056]

[0057]

[0058] In the formula, is an arbitrary function. In the embodiments of the present application, . is the dilation factor, satisfying . is the half bandwidth of the filtering band.

[0059] Step S4028: Determine the modal component signals corresponding to the first spectrum intervals based on the empirical scaling functions and empirical wavelet functions of the first spectrum intervals, and determine the modal component signals corresponding to the second spectrum intervals based on the empirical scaling functions and empirical wavelet functions of the second spectrum intervals.

[0060] Exemplarily, in the embodiments of the present application, the modal components of each interval can be obtained by using the constructed empirical scaling functions and empirical wavelet functions.

[0061]

[0062]

[0063] In the formula: and are respectively the inverse Fourier transform functions of and , n = 1, 2,..., M; and are respectively the complex conjugates of and ; f (t) is the collected current signal, represents the detail coefficient, which is the inner product of the empirical wavelet function and f (t), represents the approximation coefficient, which is the empirical scaling function Inner product with f (t), denotes Fourier transform, denotes inverse Fourier transform.

[0064] Step S4029: Use the modal component signals corresponding to different first spectral intervals and the modal component signals corresponding to different second spectral intervals as multiple groups of modal components.

[0065] Step S403: Perform singular value decomposition on multiple groups of modal components to obtain a noise-reduced signal. For details, please refer to Figure 1 Step S103 of the embodiment shown, which will not be elaborated here.

[0066] Step S404: Calculate the first target values of multiple characteristic indicators for the noise-reduced signal. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0067] Step S405: Based on the first target values of each characteristic indicator and the preset threshold corresponding to each characteristic indicator, determine whether there is an arc fault in the photovoltaic system to obtain the arc fault detection result of the photovoltaic system. For details, please refer to Figure 1 Step S105 of the embodiment shown, which will not be elaborated here.

[0068] In this embodiment, a photovoltaic DC arc fault detection method is provided, which can be used for the above server. Figure 5 It is a flowchart of the photovoltaic DC arc fault detection method according to an embodiment of the present invention. As Figure 5 shown, this process includes the following steps: Step S501: Obtain the current signal at the end of the photovoltaic string branch in the photovoltaic system. For details, please refer to Figure 4 Step S401 of the embodiment shown, which will not be elaborated here.

[0069] Step S502: Process the current signal using a preset empirical wavelet transform method to obtain multiple groups of modal components. For details, please refer to Figure 4 Step S402 of the embodiment shown, which will not be elaborated here.

[0070] Step S503: Perform singular value decomposition on multiple groups of modal components to obtain a noise-reduced signal.

[0071] Specifically, the above step S503 includes: Step S5031: Construct a Hankel matrix based on multiple groups of modal components.

[0072] In some optional implementation manners, the above step S5031 includes: Step a1: Determine a one-dimensional discrete digital signal based on multiple groups of modal components. Exemplarily, in the embodiments of the present application, the purpose of improving the singular value decomposition is to reduce the construction dimension of the Hankel matrix in the singular value decomposition, reduce the amount of calculation, and save time. The singular value decomposition process is essentially a matrix low-rank process. The non-zero singular values with larger numerical values at the front of the sequence represent most of the energy in the signal. The M groups of modal components obtained after the current signal is decomposed by the empirical wavelet are one-dimensional discrete digital signals. For one-dimensional discrete digital signals, it needs to be converted into a suitable matrix before performing the singular value decomposition.

[0073] Step a2: Perform segmentation processing on the one-dimensional discrete digital signal to obtain a preset number of segments of signals, and each segment of signal contains a preset number of data points.

[0074] Exemplarily, in the embodiments of the present application, and usually the one-dimensional discrete digital signal is converted into a Hankel matrix. Let the length of N the one-dimensional discrete digital signal be , , then its Hankel matrix is:

[0075] In the formula, H is the Hankel matrix, , , .

[0076] There are many choices for the values of m and n in the Hankel matrix. However, the arc fault signal incorporates a DC component, an inverter switching frequency component, an arc noise component, a random noise component, etc. To separate the singular values representing different components, it is necessary to increase the number of singular values as much as possible. Therefore, it is best to construct the matrix as a square matrix. Although the Hankel matrix is a commonly used conversion form for one-dimensional discrete digital signals, the redundant usage method of its data will make the dimension of the matrix too large, thereby increasing the computational cost of the singular value decomposition. And arc fault detection has high requirements for the rapidity of data processing. Therefore, to reduce the complexity of matrix construction, the embodiments of the present application introduce a simplified Hankel construction method. The preset number can be expressed as L , assuming that the length of the one-dimensional discrete digital signal can be expressed as, that is, the one-dimensional discrete digital signal can be segmented into L segments, and each segment contains L data points.

[0077] Step a3: Construct a Hankel matrix using the preset number of segments of signals. Exemplarily, in the embodiments of the present application, the Hankel matrix constructed based on L segments of signals can be shown as the following formula:

[0078] Among them, represents a Hankel matrix constructed based on L segment signals.

[0079] In the embodiment of the present application, taking one-dimensional current data points as an example, with a data length of 1024, if the traditional Hankel matrix construction method is adopted, a matrix needs to be constructed. However, if the improved Hankel construction method is adopted, only a matrix needs to be constructed, significantly reducing the computational cost and time cost of singular value decomposition. In the embodiment of the present application, the noise reduction process based on the current signal is as Figure 6 shown. The noise reduction waveform diagram of the current signal is as Figure 7 shown.

[0080] Step S5032: Perform singular value decomposition on the Hankel matrix to obtain a noise reduction signal.

[0081] Exemplarily, by using singular value decomposition to process the Hankel matrix, the DC component, the inverter switching frequency component and its multiple frequency components in the current signal can be eliminated, and the arc characteristics will not be lost.

[0082] Step S504: Calculate the first target values of multiple characteristic indicators of the noise reduction signal. For details, please refer to Figure 4 step S404 of the embodiment shown herein, which will not be elaborated herein.

[0083] Step S505: Based on the first target values of the respective characteristic indicators and the preset thresholds corresponding to the respective characteristic indicators, determine whether there is an arc fault in the photovoltaic system, and obtain the arc fault detection result of the photovoltaic system.

[0084] In the embodiments of the present application, the eigenvalues of the characteristic indicators corresponding to typical arc faults calculated are significantly distinguishable from the eigenvalues of the characteristic indicators corresponding to normal current signals, which is beneficial for the division of detection thresholds and not prone to misjudgment. The eigenvalues of atypical arc faults are easily confused with the eigenvalues of normal current signals. To improve the accuracy of arc detection, a transition interval can be set to further detect the signals in the transition interval and more accurately distinguish normal signals from atypical fault signals. Through a large number of comparative experiments, the present invention finds that when a typical arc fault occurs, the spectral amplitude of the current significantly increases in the range of 0 - 50 kHz, and the amplitude increase is particularly obvious in the low-frequency band of 0 - 25 kHz; when an atypical arc fault occurs, the amplitude of the current significantly increases in the high-frequency band of 25 kHz - 50 kHz, and the spectral amplitude of the current within 0 - 25 kHz does not increase significantly. The embodiments of the present application utilize this characteristic to propose further judging atypical arc faults by comparing the ratio of high-frequency characteristic indicators to low-frequency characteristic indicators, and constructing a dual-threshold arc fault diagnosis algorithm based on this.

[0085] Specifically, step S505 includes: Step S5051, obtaining the first interval and the second interval of each characteristic indicator.

[0086] Exemplarily, the embodiments of the present application do not limit the specific content of the first interval and the second interval, and those skilled in the art can determine it according to experience.

[0087] Step S5052, if the first target value of one characteristic indicator is greater than the corresponding preset threshold, it is determined that there is an arc fault in the photovoltaic system.

[0088] Step S5053, if there is no arc fault in the current signal, determine whether the first target value of each characteristic indicator is within the first interval of the corresponding characteristic indicator to obtain a judgment result.

[0089] Step S5054, based on the judgment result, determine the target characteristic indicator among multiple characteristic indicators, and the first target value of the target characteristic indicator is within the first interval of the corresponding characteristic indicator.

[0090] Exemplarily, in the embodiments of the present application, the value of the energy indicator can be calculated by the following formula:

[0091] Wherein, E represents the value of the energy indicator, N represents the number of data points in the data sequence, represents the i-th data point in the data sequence.

[0092] The value of the peak-to-peak indicator can be calculated by the following formula:

[0093] Among them, represents the value of the peak-to-peak index, and the meanings of the remaining variables will not be elaborated here.

[0094] The value of the kurtosis factor index can be calculated by the following formula:

[0095] Among them, Kf represents the value of the kurtosis factor index, represents the data mean, represents the standard deviation of the data, represents the effective value of the data.

[0096] In the embodiments of the present application, the preset threshold of the energy index can be 0.5, and the first interval can be E< 0.25. If the first target value of the energy index is less than 0.25, it is determined that the energy index is the target characteristic index. The preset threshold of the peak-to-peak index can be 0.15, and the first interval can be , if the first target value of the peak-to-peak index is less than 0.1, it is determined that the peak-to-peak index is the target characteristic index. The preset threshold of the kurtosis factor index can be 5e6, and the first interval can be 1e7. If the first target value of the kurtosis factor index is less than 1e7, it is determined that the kurtosis factor index is the target characteristic index.

[0097] Step S5055: Calculate the second target value of the target characteristic index based on the first current component, and calculate the third target value of the target characteristic index based on the second current component.

[0098] Exemplarily, calculating the second target value of the target characteristic index based on the first current component and calculating the third target value of the target characteristic index based on the second current component. For the specific calculation process, refer to the description of the relevant content in the above embodiments, which will not be elaborated here. In the embodiments of the present application, the samples of the energy index, that is, the noise-reduced signal, are respectively constructed into a high-frequency current component and a low-frequency current component with the target frequency as the boundary. The high-frequency current component can be the first current component, and the low-frequency current component can be the second current component. The target frequency can include but is not limited to 25 kHz. Calculate respectively and the energy index of and the energy index of .

[0099] Step S5056: Calculate the target ratio based on the second target value and the third target value of the target characteristic index.

[0100] Exemplarily, in the embodiments of the present application, taking the energy index as the target characteristic index as an example, the target ratio is .

[0101] Step S5057, if the target ratio is within the second interval of the target characteristic index, it is determined that there is an arc fault in the photovoltaic system.

[0102] Exemplarily, in the embodiments of the present application, the second interval of the energy index may include or . When the target characteristic index is the energy index, the arc detection process based on the energy index is as Figure 8a shown. If (the value 9 is obtained through experiments), it can be determined that an atypical arc fault has occurred. If (the value 4 is obtained through experiments), it is determined that a typical arc fault has occurred, otherwise it is determined to be normal current. Among them, when the target characteristic index is the peak-to-peak index , the arc detection process based on the peak-to-peak index can be as Figure 8b shown. If the target ratio of the peak-to-peak index F p > 3.5, it can be determined that an atypical arc fault has occurred. When the target ratio of the peak-to-peak index F p <1, it is determined that a typical arc fault has occurred, otherwise it is determined to be normal current. When the target characteristic index is the kurtosis factor index Kf , the arc detection process based on the kurtosis factor can be as Figure 8c shown. If the target ratio of the kurtosis factor index Kf F k > 10, it can be determined that an atypical arc fault has occurred. When the target ratio of the kurtosis factor index Kf F k <0.5, it is determined that a typical arc fault has occurred, otherwise it is determined to be normal current.

[0103] In some alternative embodiments, the method further includes: Step b1, using a preset fusion method to fuse the first target values of multiple characteristic indexes to obtain a fused characteristic value.

[0104] Exemplarily, the preset fusion algorithm may include, but is not limited to, the improved entropy weight method. In the embodiments of the present application, in order to reduce the influence of misjudgment of a single characteristic index, it is considered to construct a fusion characteristic index by using three groups of characteristic indexes of energy, peak-to-peak value, and kurtosis factor, and use the improved entropy weight method to assign weights to individual indexes, and fuse the three characteristic indexes for arc diagnosis, which can further improve the accuracy of photovoltaic DC arc fault detection. The specific steps of the improved entropy weight method include: (1) Randomly select 1000 groups of samples, including 500 groups of normal signal samples and 500 groups of fault signal samples, and calculate the corresponding 3 characteristic indexes for each group of samples; (2) Divide the attribute of the characteristic index to obtain two subsets (Normal) and (Arc fault); (3) Calculate the information entropy of each characteristic index belonging to the subset:

[0105] Among them, p k , k = 1, 2. p 1 represents the accuracy rate of the subset diagnosis result, p 2 represents the error rate of the subset diagnosis result, Ent ( D ) represents the information entropy of the subset.

[0106] (4) Calculate the information gain of each characteristic index: ; Among them, G j represents the information gain of each characteristic index, D represents the total number of samples, which is the combination of the two subsets and of, D i , i = 1, 2. are the above two subsets.

[0107] (5) Calculate the weight of each characteristic index: ; Among them, represents the weight of the jth characteristic index.

[0108] Step b2, based on the fusion eigenvalue, perform arc fault judgment on the current signal.

[0109] Exemplarily, in the embodiments of the present application, based on the fusion eigenvalue and a preset fusion threshold, it is determined whether the current signal is an arc fault current signal. The embodiments of the present application do not limit the specific content of the fusion threshold, and those skilled in the art can determine it according to requirements.

[0110] In this embodiment, a photovoltaic DC arc fault detection device is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be elaborated again. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0111] This embodiment provides a photovoltaic DC arc fault detection device, as Figure 9 shown, including: An acquisition module 901, configured to acquire a current signal at the end of a photovoltaic string branch in a photovoltaic system; A processing module 902, configured to process the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components; A decomposition module 903, configured to perform singular value decomposition on the multiple groups of modal components to obtain a denoised signal; A calculation module 904, configured to calculate a first target value of multiple characteristic indexes for the denoised signal; A first judgment module 905, configured to judge whether there is an arc fault in the photovoltaic system based on the first target value of each characteristic index and a preset threshold corresponding to each characteristic index, and obtain an arc fault detection result of the photovoltaic system.

[0112] In some alternative implementation manners, the processing module 902 includes: A transformation sub-module, configured to perform Fourier transform on the current signal to obtain a unilateral Fourier spectrum of the current signal; A mapping sub-module, configured to map the unilateral Fourier spectrum to a preset range to obtain a target spectrum; A first division sub-module, configured to divide the target spectrum by using target boundary values to obtain a first spectrum and a second spectrum; A first determination sub-module, configured to respectively determine a target number of maximum value points in the first spectrum and the second spectrum; A second division sub-module, configured to divide the first spectrum by using a minimum value point between two adjacent maximum value points in the first spectrum as a segmentation boundary to obtain a target number of first spectrum intervals; A third division sub-module, configured to divide the second spectrum by using a minimum value point between two adjacent maximum value points in the second spectrum as a segmentation boundary to obtain a target number of second spectrum intervals; A first construction sub-module, configured to construct an empirical scaling function and an empirical wavelet function for each first spectrum interval, and an empirical scaling function and an empirical wavelet function for each second spectrum interval; The second determination sub-module is configured to determine the modal component signals corresponding to the first spectral intervals based on the empirical scaling functions and empirical wavelet functions of the first spectral intervals, and determine the modal component signals corresponding to the second spectral intervals based on the empirical scaling functions and empirical wavelet functions of the second spectral intervals; The third determination sub-module is configured to use the modal component signals corresponding to different first spectral intervals and the modal component signals corresponding to different second spectral intervals as multiple sets of modal components.

[0113] In some alternative embodiments, the decomposition module 903 includes: The second construction sub-module is configured to construct a Hankel matrix based on multiple sets of modal components; The decomposition sub-module is configured to perform singular value decomposition on the Hankel matrix to obtain a noise-reduced signal.

[0114] In some alternative embodiments, the above-mentioned second construction sub-module includes: The determination unit is configured to determine a one-dimensional discrete digital signal based on multiple sets of modal components; The segmentation processing unit is configured to perform segmentation processing on the one-dimensional discrete digital signal to obtain a preset number of segments of signals, and each segment of signal contains a preset number of data points; The construction unit is configured to construct a Hankel matrix by using the preset number of segments of signals.

[0115] In some alternative embodiments, the first judgment module 905 includes: The acquisition sub-module is configured to acquire the first interval and the second interval of each characteristic index; The fourth determination sub-module is configured to determine that there is an arc fault in the photovoltaic system if the first target value of one characteristic index is greater than the corresponding preset threshold; The judgment sub-module is configured to judge whether the first target value of each characteristic index is within the first interval of the corresponding characteristic index to obtain a judgment result if there is no arc fault in the current signal; The fifth determination sub-module is configured to determine a target characteristic index among multiple characteristic indexes based on the judgment result, and the first target value of the target characteristic index is within the first interval of the corresponding characteristic index; The first calculation sub-module is configured to calculate the second target value of the target characteristic index based on the first current component and calculate the third target value of the target characteristic index based on the second current component; The second calculation sub-module is configured to calculate a target ratio based on the second target value and the third target value of the target characteristic index; The sixth determination sub-module is configured to determine that there is an arc fault in the photovoltaic system if the target ratio is within the second interval of the target characteristic index.

[0116] In some alternative embodiments, the above-mentioned device further includes: A fusion module, configured to fuse first target values of multiple feature indicators by using a preset fusion method to obtain a fusion feature value; A second judgment module, configured to perform arc fault judgment on the current signal based on the fusion feature value.

[0117] The further function descriptions of the above-mentioned modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0118] The photovoltaic DC arc fault detection device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0119] An embodiment of the present invention further provides a computer device having the above-mentioned Figure 9 photovoltaic DC arc fault detection device.

[0120] Please refer to Figure 10 , Figure 10 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As shown in Figure 10 , the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other through different buses and can be installed on a common motherboard or installed in other ways according to needs. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 10 In

[0121] Processor 10 may be a central processor, a network processor, or a combination thereof. Among them, processor 10 may further include a hardware chip. The above-mentioned hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device may be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0122] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0123] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] The memory 20 may include a volatile memory, for example, a random access memory; the memory may also include a non-volatile memory, for example, a flash memory, a hard disk, or a solid-state drive; the memory 20 may also include a combination of the above types of memories.

[0125] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0126] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading through a network from an original stored in a remote storage medium or a non-temporary machine-readable storage medium and to be stored in a local storage medium, so that the method described herein can be stored on such a software process on a storage medium using a general computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0127] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways for a computer to execute computer program instructions include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0128] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A photovoltaic DC arc fault detection method, characterized in that, The method includes: Obtaining a current signal at the end of a photovoltaic string branch in a photovoltaic system; Processing the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components; Performing singular value decomposition on the multiple groups of modal components to obtain a noise-reduced signal; Calculating a first target value of multiple characteristic indexes for the noise-reduced signal; Judging whether there is an arc fault in the photovoltaic system based on the first target value of each characteristic index and a preset threshold corresponding to each characteristic index, so as to obtain an arc fault detection result of the photovoltaic system.

2. The method according to claim 1, characterized in that The step of processing the current signal by using a preset empirical wavelet transform method to obtain multiple groups of modal components includes: Performing Fourier transform on the current signal to obtain a unilateral Fourier spectrum of the current signal; Mapping the unilateral Fourier spectrum to a preset range to obtain a target spectrum; Dividing the target spectrum by using target boundary values to obtain a first spectrum and a second spectrum; Determining a target number of maximum points in the first spectrum and the second spectrum respectively; Dividing the first spectrum by using a minimum point between two adjacent maximum points in the first spectrum as a segmentation boundary to obtain a target number of first spectrum intervals; Dividing the second spectrum by using a minimum point between two adjacent maximum points in the second spectrum as a segmentation boundary to obtain a target number of second spectrum intervals; Constructing an empirical scaling function and an empirical wavelet function for each first spectrum interval, and an empirical scaling function and an empirical wavelet function for each second spectrum interval; Determining a modal component signal corresponding to a corresponding first spectrum interval based on the empirical scaling function and the empirical wavelet function of each first spectrum interval, and determining a modal component signal corresponding to a corresponding second spectrum interval based on the empirical scaling function and the empirical wavelet function of each second spectrum interval; Taking the modal component signals corresponding to different first spectrum intervals and the modal component signals corresponding to different second spectrum intervals as the multiple groups of modal components.

3. The method according to claim 1, characterized in that, The step of performing singular value decomposition on the multiple groups of modal components to obtain a noise-reduced signal includes: Constructing a Hankel matrix based on the multiple groups of modal components; Performing singular value decomposition on the Hankel matrix to obtain a noise-reduced signal.

4. The method according to claim 3, wherein The step of constructing a Hankel matrix based on the multiple groups of modal components includes: Determining a one-dimensional discrete digital signal based on multiple groups of modal components; Performing segmentation processing on the one-dimensional discrete digital signal to obtain a preset number of segments of signals, and each segment of signal contains a preset number of data points; Constructing the Hankel matrix by using the preset number of segments of signals.

5. The method according to claim 2, wherein The step of judging whether there is an arc fault in the photovoltaic system based on the first target value of each characteristic index and a preset threshold corresponding to each characteristic index, so as to obtain an arc fault detection result of the photovoltaic system includes: Obtaining a first interval and a second interval of each characteristic index; If the first target value of one characteristic index is greater than the corresponding preset threshold, determining that there is an arc fault in the photovoltaic system; If there is no arc fault in the current signal, judging whether the first target value of each characteristic index is located in the first interval of the corresponding characteristic index to obtain a judgment result; Determining a target characteristic indicator from a plurality of characteristic indicators based on the judgment result, wherein a first target value of the target characteristic indicator is located in the first interval of the corresponding characteristic indicator; calculating a second target value of the target characteristic index based on the first current component, and calculating a third target value of the target characteristic index based on the second current component; Calculating a target ratio based on a second target value and a third target value of the target characteristic indicator; If the target ratio is within the second interval of the target characteristic index, it is determined that an arc fault exists in the photovoltaic system.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Using a preset fusion method to fuse the first target values of multiple feature indicators to obtain a fused feature value; An arc fault is determined on the current signal based on the fused characteristic value.

7. A photovoltaic DC arc fault detection device, characterized in that: The device comprises: An acquisition module is used to acquire the current signal at the end of the photovoltaic string branch in the photovoltaic system; a processing module, configured to process the current signal using a preset empirical wavelet transform method to obtain multiple groups of modal components; A decomposition module, configured to perform singular value decomposition on the multiple groups of modal components to obtain a noise reduction signal; A calculation module, configured to calculate a first target value of a plurality of characteristic indicators of the noise reduction signal; The judgment module is used to judge whether there is an arc fault in the photovoltaic system based on the first target value of each characteristic indicator and the preset threshold value corresponding to each characteristic indicator, and obtain the arc fault detection result of the photovoltaic system.

8. A computer device, characterized in that, include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the photovoltaic DC arc fault detection method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the photovoltaic DC arc fault detection method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the photovoltaic DC arc fault detection method according to any one of claims 1 to 6.

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