Fault line selection method and system for distribution network based on modal decomposition and adaptive noise

Abstract

A fault line selection method and system for a distribution network based on modal decomposition and adaptive noise, comprising: judging whether the power system fails according to the zero-sequence voltage; if a fault occurs, obtaining the transient zero-sequence current signal containing noise on the feeder line; selecting the main frequency modal component from the intrinsic modal components according to the energy, spectrum and time-domain correlation of the intrinsic modal components; calculating the difference between the energy of the main frequency modal component of each feeder line and the energy of the sum of the main frequency modal components of other feeder lines, and the sum of the correlation coefficients between each feeder line and other feeder lines; if there is a feeder line that satisfies both the minimum energy difference and the sum of the correlation coefficients being less than zero, then this feeder line is the faulty feeder line. The present invention improves the accuracy of fault line selection discrimination for a distribution network by screening the faulty feeder line through energy and correlation coefficients.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network fault line selection, and more specifically, relates to a distribution network fault line selection method and system based on complete ensemble empirical mode decomposition and adaptive noise. Background Technique

[0002] With more and more distributed energy sources being connected to the power grid, how to ensure the power supply reliability of the distribution network has become the focus of research by scholars at home and abroad. The probability of single-phase grounding faults occurring in the power system accounts for more than 80% of all fault conditions. When a single-phase fault occurs, the line voltage of the three-phase system still remains balanced, so power supply can be maintained for 1 - 2 hours. If not processed in time, it will lead to insulation breakdown and develop into an interphase fault. Therefore, it is necessary to locate and cut off the faulty section in time. However, due to the compensation effect of the arc suppression coil, the fault current is small, resulting in the inability of existing research methods to accurately identify.

[0003] With the development of deep learning, current fault section location methods can be roughly divided into two types: traditional methods based on fault characteristics and modern methods based on artificial intelligence algorithms. According to the different fault characteristic quantities used, traditional methods can be further divided into those based on steady-state information, matrix methods, and those based on transient characteristic quantities. The method of locating the fault section based on steady-state information is easily affected by topological changes, arc suppression coils, etc., and the matrix method is faced with the influence of data loss and distortion. The method of locating the fault section based on transient characteristic quantities can be further refined into the traveling wave method and the signal decomposition method. The traveling wave method utilizes the reflection and refraction characteristics of traveling wave signals when a fault occurs, and combines the wave velocity and transmission time to measure the distance to the fault. According to the difference in the electrical quantities used, the traveling wave method is divided into the single-terminal traveling wave method and the double-terminal traveling wave method. However, the traveling wave method requires special devices, has a high cost, and there are certain difficulties in wavefront recognition, and it is necessary to further improve the waveform data and improve the waveform fault characteristic matching technology. Some modern signal processing methods are often used to decompose the measured data into different frequency band components.

[0004] CN110632462A provides a small current grounding fault location method and its system, computer equipment, and medium, including the following steps: receiving in real time the zero-mode current recording data of each monitoring node on the line when a fault occurs on the line; wherein, each line is divided into multiple sections, and multiple monitoring nodes are set in each section; extracting the transient component of the zero-mode current recording data, and determining its transient amplitude and transient resonance frequency according to the transient component; generating transient characteristic quantities of each monitoring node according to the transient amplitude and transient resonance frequency; determining the section where the fault point is located according to the transient characteristic quantities of each monitoring node. However, this fault location method does not decompose the zero-mode current recording data obtained by the monitoring node, and cannot remove the influence of noise; and no global transient characteristic quantity index is established, only the amplitude and resonance frequency are considered, and the fact that the transient zero-mode current waveform of the fault line is opposite in polarity to that of the non-fault line is not considered. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the present invention provides a distribution network fault line selection method and system based on complete ensemble empirical mode decomposition and adaptive noise.

[0006] The present invention adopts the following technical solutions.

[0007] The first aspect of the present invention proposes a distribution network fault line selection method based on mode decomposition and adaptive noise, which is characterized by including the following:

[0008] Monitor the zero-sequence voltage of the power system in real time, and judge whether the power system is faulty according to the zero-sequence voltage; if it is faulty, record and collect the transient zero-sequence current for a set multiple of cycles after the fault, and obtain the transient zero-sequence current signal containing noise on the feeder.

[0009] Decompose the transient zero-sequence current signal containing noise through the mode decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components, and select the main frequency mode component from the intrinsic mode components according to the energy, spectrum and time-domain correlation of the intrinsic mode components.

[0010] Calculate the difference between the energy of the main frequency mode component of each feeder and the energy of the sum of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders; if there is a feeder that satisfies both the minimum energy difference and the sum of the correlation coefficients being less than zero, then this feeder is the faulty feeder, otherwise return to monitor the zero-sequence voltage of the power system, and judge whether the power system is faulty according to the zero-sequence voltage.

[0011] Preferably, the judging whether the power system is faulty according to the zero-sequence voltage is specifically:

[0012] Determine whether the zero-sequence voltage exceeds the set multiple of the bus rated voltage. If it exceeds, the power system has a fault; otherwise, there is no fault. The value range of the set multiple is between 0.05 and 0.15.

[0013] Preferably, the transient zero-sequence current in the post-fault set multiple period is the transient zero-sequence current in 1 / 4 period.

[0014] The method of decomposing the transient zero-sequence current signal containing noise by modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components is as follows:

[0015] Take the transient zero-sequence current signal containing noise as the original signal, and add multiple pairs of white noises that are opposite to each other to the original signal in turn. After each addition, perform modal decomposition on it to obtain its first intrinsic mode component. Take the average value of all the first intrinsic mode components, which is the first intrinsic mode component of the transient zero-sequence current signal containing noise.

[0016] Subtract the first intrinsic mode component of the transient zero-sequence current signal containing noise from the original signal to obtain the residual.

[0017] Add multiple pairs of adaptive noises to the residual. After each addition, perform modal decomposition on it to obtain its second intrinsic mode component. Take the average value of all the second intrinsic mode components, which is the second intrinsic mode component of the transient zero-sequence current signal containing noise.

[0018] Subtract the second intrinsic mode component of the transient zero-sequence current signal containing noise from the residual to obtain the updated residual, and repeat the above process until the updated residual cannot be decomposed by modal decomposition any more.

[0019] Preferably, the addition of multiple pairs of adaptive noises is specifically as follows:

[0020] If the component obtained by this decomposition is the k th intrinsic mode component of the transient zero-sequence current signal containing noise, then perform modal decomposition on the multiple pairs of white noises that are opposite to each other added to the original signal to obtain the k -1th intrinsic mode component of each pair of white noises; multiply the k -1th intrinsic mode component of each pair of white noises by the adaptive amplitude to obtain the corresponding pair of adaptive noises. The adaptive amplitude is the amplitude of the multiple pairs of white noises that are opposite to each other added to the original signal multiplied by the energy of the residual at this time.

[0021] Preferably, according to the energy, spectrum and time-domain correlation of the intrinsic mode components, select the main frequency mode components from the intrinsic mode components, specifically as follows:

[0022] First, calculate the ratio of the energy of each intrinsic mode component to the energy of all intrinsic mode components. If the ratio of an intrinsic mode component exceeds the set energy ratio threshold, select the intrinsic mode component with the maximum energy as the main frequency mode component;

[0023] Otherwise, obtain the spectrum of each intrinsic mode component through Fourier transform, calculate the comprehensive index, and take the intrinsic mode component with the maximum comprehensive index as the main frequency mode component;

[0024] Comprehensive index The calculation formula is:

[0025]

[0026] Among them, 、 、 are the set weights, is the energy of the k th intrinsic mode component, K is the total number of intrinsic mode components, is the maximum value in the energy of the intrinsic mode components, is the standard deviation of the energy of the intrinsic mode components; is the spectral peak frequency of the k th intrinsic mode component; 、 are the frequencies when the left and right sides of the spectrum of the k th intrinsic mode component drop by the set dB respectively; is the k th intrinsic mode component; is the corresponding original signal, n is the sampling point; is the time-domain correlation; represents the inner product operation; .

[0027] Preferably, calculating the difference between the energy of the main frequency mode component of each feeder and the sum of the energy of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders, specifically:

[0028] The difference between the energy of the main frequency mode component of each feeder and the sum of the energy of the main frequency mode components of other feeders , the calculation formula is:

[0029]

[0030] Among them, N is the number of sampling points of the original signal, i , j represents the i ,j The \(i\)-th feeder, and is the value of the \(j\)-th sampling point of the main frequency modal component of the \(i\)-th feeder, i, j where \(i = 1,2,\cdots,n\); \(j = 1,2,\cdots,m\), n and \(n\) is the total number of feeders; J The sum of the correlation coefficients between each feeder and other feeders

[0031] is calculated by the following formula: \(\sum_{i = 1}^{n}\sum_{k = 1,k\neq i}^{n}\rho_{ik}\),

[0032]

[0033] where \(x_{ij}\), is the value of the \(j\)-th sampling point of the original signal of the \(i\)-th feeder, i where \(i = 1,2,\cdots,n\); \(j = 1,2,\cdots,m\), j and n \(\overline{x}_{i}\), is the average value of all sampling points of the original signal of the \(i\)-th feeder, where \(i = 1,2,\cdots,n\). i j

[0034]

[0035] A second aspect of the present invention proposes a distribution network fault line selection system based on modal decomposition and adaptive noise using the method described in the first aspect of the present invention, including: a monitoring module, a main frequency modal component acquisition module, and a distribution network fault line selection module, characterized in that:

[0035] The monitoring module: monitors the zero-sequence voltage of the power system in real time, and determines whether the power system is faulty according to the zero-sequence voltage; if it is faulty, it records and collects the transient zero-sequence current for a set multiple of cycles after the fault, and obtains the transient zero-sequence current signal with noise on the feeder;

[0036] The main frequency modal component acquisition module: decomposes the transient zero-sequence current signal with noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic modal components, and selects the main frequency modal component from the intrinsic modal components according to the energy, spectrum and time-domain correlation of the intrinsic modal components;

[0037] The distribution network fault line selection module: calculates the difference between the energy of the main frequency modal component of each feeder and the sum of the energies of the main frequency modal components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders; if there is a feeder that satisfies both the minimum difference in energy and the sum of the correlation coefficients less than zero, then the feeder is the faulty feeder, otherwise it returns to monitor the zero-sequence voltage of the power system and determines whether the power system is faulty according to the zero-sequence voltage.

[0038] A third aspect of the present invention provides a device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it uses the steps of a method for fault line selection in a distribution network based on modal decomposition and adaptive noise according to the first aspect of the present invention.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it uses the steps of a method for fault line selection in a distribution network based on modal decomposition and adaptive noise according to the first aspect of the present invention.

[0040] The beneficial effects of the present invention are as follows. Compared with the prior art, the method and system for fault line selection in a distribution network based on complete ensemble empirical mode decomposition and adaptive noise of the present invention perform theoretical analysis on the simplified equivalent circuit of single-phase grounding fault transient analysis, detect the zero-sequence current signals of each feeder to obtain the feeder with the transient zero-sequence current signal containing noise, decompose the transient zero-sequence current signal containing noise through the modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components, select the main frequency mode components from the intrinsic mode components according to the energy, spectrum, and time-domain correlation of the intrinsic mode components, and determine the energy and correlation coefficient of the main frequency mode components of each feeder based on the theory of correlation and energy difference, so as to obtain the feeder with the maximum energy and a correlation coefficient less than zero as the fault feeder, improving the accuracy of fault line selection discrimination in the distribution network. Description of the Drawings

[0041] Figure 1 is a flowchart of the present invention;

[0042] Figure 2 is an equivalent circuit diagram of the circuit simplified by the present invention. Detailed Embodiments

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0044] As shown in the Figure 1 accompanying drawings, Embodiment 1 of the present invention discloses a method for fault line selection in a distribution network based on modal decomposition and adaptive noise, comprising the following steps:

[0045] As shown in the Figure 2As shown, the circuit is simplified according to the actual situation, and the theoretical analysis is carried out on the equivalent circuit of the single-phase grounding fault transient analysis after simplification. By analyzing the relationship between the polarity and amplitude of the fault line and the healthy line, it is concluded that the polarities of the fault line and the healthy line are different, and the amplitude of the fault line is significantly greater than that of the healthy line;

[0046] Specifically, at power frequency , it is approximately considered that the inductive reactance of the arc suppression coil is equal to the capacitive reactance of the system to the ground, that is:

[0047]

[0048] In the formula, is 3 times the inductance of the arc suppression coil; is the distributed capacitance of the line to the ground per unit length; is the total length of all outgoing lines in the system; is the sum of the distributed capacitances of all outgoing lines to the ground;

[0049] Since the capacitive reactance of the system to the ground decreases with the increase of frequency, while the inductive reactance of the arc suppression coil increases with the increase of frequency, then at the transient main resonance frequency , the relationship between the capacitive reactance of the system to the ground and the inductive reactance of the arc suppression coil is as follows:

[0050]

[0051] In the formula, is the ratio of the transient main resonance frequency to the power frequency; it can be seen from the above formula that when , it can be approximately considered that the inductive reactance of the arc suppression coil is more than 9 times the capacitive reactance to the ground. Then, in the process of model simplification, the role of the arc suppression coil can be ignored, and the equivalent circuit of the single-phase grounding fault transient analysis after simplification is obtained, as Figure 2 shown. Theoretical analysis is carried out on it, and the following formula is obtained:

[0052]

[0053] In the formula, is the voltage at both ends of the line, R is the equivalent resistance, is the amplitude of the bus voltage, is the time, is the initial phase angle, is the zero-sequence current of the h th healthy line, is the h th healthy line's capacitance to the ground, is the zero-sequence current of the fault line; H is the total number of healthy lines

[0054] Let , substituting into the above equation and solving it, we can obtain:

[0055]

[0056]

[0057]

[0058]

[0059] wherein, and are r two solutions of; is an imaginary number; is a characteristic root;

[0060] Because is greater than 1, it can be concluded that the difference in energy between the main frequency modal component of the transient zero-sequence current of the faulty line and the sum of the energies of the main frequency modal components of other feeders is the smallest, and the waveforms of the transient zero-sequence current of the faulty line and the transient zero-sequence current of other feeders show a state of being flipped up and down.

[0061] Real-time monitor the zero-sequence voltage of the power system, and judge whether the power system is faulty according to the zero-sequence voltage; if it is faulty, record and collect the transient zero-sequence current for a set multiple of cycles after the fault, and obtain the transient zero-sequence current signal containing noise on the feeder;

[0062] Decompose the transient zero-sequence current signal containing noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components, and select the main frequency modal component from the intrinsic mode components according to the energy, spectrum and time-domain correlation of the intrinsic mode components;

[0063] Calculate the difference in energy between the main frequency modal component of each feeder and the sum of the energies of the main frequency modal components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders; if there is a feeder that satisfies both the minimum difference in energy and the sum of the correlation coefficients being less than zero, then this feeder is the faulty feeder, otherwise return to monitor the zero-sequence voltage of the power system and judge whether the power system is faulty according to the zero-sequence voltage.

[0064] The judgment of whether the power system is faulty according to the zero-sequence voltage is specifically:

[0065] Judge whether the zero-sequence voltage exceeds a set multiple of the rated voltage of the bus. If it exceeds, the power system is faulty; otherwise, there is no fault; the value range of the set multiple is between 0.05 - 0.15.

[0066] The decomposition of the transient zero-sequence current signal containing noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components is specifically:

[0067] Take the transient zero-sequence current signal containing noise as the original signal, and add multiple pairs of white noises that are opposite to each other to the original signal in sequence; perform modal decomposition on it after each addition to obtain its first intrinsic mode component; average all the first intrinsic mode components, which is the first intrinsic mode component of the transient zero-sequence current signal containing noise;

[0068] Subtract the first intrinsic mode component of the transient zero-sequence current signal containing noise from the original signal to obtain the residual;

[0069] Add multiple pairs of adaptive noises to the residual; perform modal decomposition on it after each addition to obtain its second intrinsic mode component; average all the second intrinsic mode components, which is the second intrinsic mode component of the transient zero-sequence current signal containing noise;

[0070] Subtract the second intrinsic mode component of the transient zero-sequence current signal containing noise from the residual to obtain the updated residual, and repeat the above process until the updated residual cannot be further decomposed by modal decomposition.

[0071] The addition of multiple pairs of adaptive noises is specifically as follows:

[0072] If the one obtained in this decomposition is the k th intrinsic mode component of the transient zero-sequence current signal containing noise, then perform modal decomposition on the multiple pairs of white noises that are opposite to each other added to the original signal to obtain the k -1th intrinsic mode component of each pair of white noises; multiply the k -1th intrinsic mode component of each pair of white noises by the adaptive amplitude to obtain the corresponding pair of adaptive noises; the adaptive amplitude is the amplitude of the multiple pairs of white noises that are opposite to each other added to the original signal multiplied by the energy of the residual at this time.

[0073] Select the main frequency mode component from the intrinsic mode components according to the energy, spectrum and time-domain correlation of the intrinsic mode components, specifically as follows:

[0074] First calculate the proportion of the energy of each intrinsic mode component to the energy of all intrinsic mode components. If there is an intrinsic mode component whose proportion exceeds the set energy proportion threshold, then select the intrinsic mode component with the largest energy among them as the main frequency mode component;

[0075] Otherwise, obtain the spectrum of each intrinsic mode component through Fourier transform, calculate the comprehensive index, and the intrinsic mode component with the largest comprehensive index is used as the main frequency mode component;

[0076] Comprehensive index The calculation formula is:

[0077]

[0078] Among them, 、 、 are set weights, is the energy of the k -th intrinsic mode component, K is the total number of intrinsic mode components, is the maximum value among the energies of the intrinsic mode components, is the standard deviation of the energies of the intrinsic mode components; is the spectral peak frequency of the k -th intrinsic mode component; 、 are respectively the frequencies when the left and right sides of the spectrum of the k -th intrinsic mode component drop by a set dB; is the k -th intrinsic mode component; is the corresponding original signal, n is the sampling point; is the time-domain correlation; represents the inner product operation; .

[0079] The difference between the energy of each feeder's main frequency mode component and the sum of the energies of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders are specifically as follows:

[0080] The difference between the energy of each feeder's main frequency mode component and the sum of the energies of the main frequency mode components of other feeders , and the calculation formula is:

[0081]

[0082] Among them, N is the number of sampling points of the original signal, i , j represents the i , j -th feeder, 、 are the values of the i, j -th sampling point of the main frequency mode component of the n -th feeder, J is the total number of feeders;

[0083] The sum of the correlation coefficients between each feeder and other feeders The calculation formula is:

[0084]

[0085] Among them, , is the value of the i -th j sampling point of the original signal of the n -th feeder, , is the average value of all sampling points of the original signal of the i -th feeder. j

[0086] Embodiment 2 of the present invention proposes a distribution network fault line selection system based on modal decomposition and adaptive noise using the method described in Embodiment 1 of the present invention, including: a monitoring module, a main frequency modal component acquisition module, and a distribution network fault line selection module, and is characterized in that:

[0087] Monitoring module: Monitor the zero-sequence voltage of the power system in real time, and judge whether the power system is faulty according to the zero-sequence voltage; if it is faulty, record and collect the transient zero-sequence current for a set multiple of cycles after the fault, and obtain the transient zero-sequence current signal with noise on the feeder;

[0088] Main frequency modal component acquisition module: Decompose the transient zero-sequence current signal with noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic modal components, and select the main frequency modal component from the intrinsic modal components according to the energy, spectrum and time-domain correlation of the intrinsic modal components;

[0089] Distribution network fault line selection module: Calculate the difference between the energy of the main frequency modal component of each feeder and the sum of the energies of the main frequency modal components of other feeders, and the sum of the correlation coefficients of each feeder and other feeders; if there is a feeder that satisfies both the minimum difference in energy and the sum of the correlation coefficients is less than zero, then this feeder is the faulty feeder, otherwise return to monitor the zero-sequence voltage of the power system, and judge whether the power system is faulty according to the zero-sequence voltage.

[0090] Embodiment 3 of the present invention proposes a device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it uses the steps of a distribution network fault line selection method based on modal decomposition and adaptive noise described in Embodiment 1 of the present invention.

[0091] Embodiment 4 of the present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, it uses the steps of a distribution network fault line selection method based on modal decomposition and adaptive noise described in Embodiment 1 of the present invention.

[0092] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions made without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A fault line selection method for distribution networks based on modal decomposition and adaptive noise, characterized in that It includes the following contents: Real-time monitor the zero-sequence voltage of the power system, and judge whether the power system is faulty according to the zero-sequence voltage; if it is faulty, record and collect the transient zero-sequence current for a set multiple period after the fault, and obtain the transient zero-sequence current signal with noise on the feeder; Decompose the transient zero-sequence current signal with noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components, and select the main frequency mode component from the intrinsic mode components according to the energy, spectrum and time-domain correlation of the intrinsic mode components; Calculate the difference between the energy of the main frequency mode component of each feeder and the sum of the energies of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders; If there is a feeder that satisfies both the minimum energy difference and the sum of the correlation coefficients less than zero, then the feeder is the faulty feeder; otherwise, return to monitor the zero-sequence voltage of the power system and judge whether the power system is faulty according to the zero-sequence voltage.

2. A distribution network fault line selection method based on modal decomposition and adaptive noise according to claim 1, characterized in that: The judgment of whether the power system is faulty according to the zero-sequence voltage is specifically: Judge whether the zero-sequence voltage exceeds a set multiple of the rated voltage of the bus. If it exceeds, the power system is faulty; otherwise, it is not faulty; the value range of the set multiple is between 0.05-0.

15.

3. A distribution network fault line selection method based on modal decomposition and adaptive noise according to claim 1, characterized in that: The transient zero-sequence current for a set multiple period after the fault is the transient zero-sequence current for 1 / 4 period.

4. A distribution network fault line selection method based on modal decomposition and adaptive noise according to claim 1, characterized in that: The decomposition of the transient zero-sequence current signal with noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components is specifically: Take the transient zero-sequence current signal with noise as the original signal, and sequentially add multiple pairs of white noises that are opposite to each other to the original signal; perform modal decomposition on it after each addition to obtain its first intrinsic mode component; take the average value of all the first intrinsic mode components, which is the first intrinsic mode component of the transient zero-sequence current signal with noise; Subtract the first intrinsic mode component of the transient zero-sequence current signal with noise from the original signal to obtain a residual; Add multiple pairs of adaptive noises to the residual; perform modal decomposition on it after each addition to obtain its second intrinsic mode component; take the average value of all the second intrinsic mode components, which is the second intrinsic mode component of the transient zero-sequence current signal with noise; Subtract the second intrinsic mode component of the transient zero-sequence current signal with noise from the residual to obtain an updated residual, and repeat the above process until the updated residual cannot be further decomposed by modal decomposition.

5. A distribution network fault line selection method based on modal decomposition and adaptive noise according to claim 4, characterized in that: The addition of multiple pairs of adaptive noises is specifically: If the one obtained from this decomposition is the k th intrinsic mode component of the transient zero-sequence current signal containing noise, then perform modal decomposition on each pair of mutually opposite white noises added to the original signal respectively, and obtain the k -1th intrinsic mode component of each pair of white noises; For the -1st intrinsic mode component of each pair of white noise, multiply it by an adaptive amplitude to obtain the corresponding pair of adaptive noises; the adaptive amplitude is the amplitude of a pair of white noises that are opposite to each other added to the original signal multiplied by the energy of the residual at this time. k ​ 6. A distribution network fault line selection method based on modal decomposition and adaptive noise according to claim 5, characterized in that: According to the energy, spectrum and time-domain correlation of the intrinsic mode components, the main frequency mode components are selected from the intrinsic mode components, specifically as follows: First, calculate the ratio of the energy of each intrinsic mode component to the energy of all intrinsic mode components. If the ratio of a certain intrinsic mode component exceeds the set energy ratio threshold, then select the intrinsic mode component with the largest energy as the main frequency mode component; Otherwise, obtain the spectrum of each intrinsic mode component through Fourier transform, calculate the comprehensive index, and select the intrinsic mode component with the largest comprehensive index as the main frequency mode component; Comprehensive index The calculation formula is as follows: Among them, , , are the set weights, is the energy of the k th intrinsic mode component, K is the total number of intrinsic mode components, is the maximum value among the energies of the intrinsic mode components, is the standard deviation of the energies of the intrinsic mode components; is the spectral peak frequency of the k th intrinsic mode component; , are respectively the frequencies when the left and right sides of the spectrum of the k th intrinsic mode component drop by the set dB; is the k th intrinsic mode component; is the corresponding original signal, n is the sampling point; is the time-domain correlation; represents the inner product operation; .

7. A method for fault line selection in a distribution network based on modal decomposition and adaptive noise according to claim 6, characterized in that: The calculation of the difference between the energy of the main frequency mode component of each feeder and the sum of the energies of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders is specifically as follows: The difference between the energy of each main frequency modal component of a feeder and the energy of the sum of the main frequency modal components of other feeders , and the calculation formula is as follows: Among them, N is the number of sampling points of the original signal, i , j represents the i th j feeder line, , and i, j is the value of the n th J sampling point of the main frequency modal component of the i, j th j feeder line; J is the total number of feeder lines; The sum of the correlation coefficients of each feeder with other feeders The calculation formula is as follows: Among them, and are the values of the i th j sampling points of the original signal of the n th feeder, and and are the average values of all sampling points of the original signal of the i th feeder. j ​ 8. A distribution network fault line selection system based on modal decomposition and adaptive noise using the method according to any one of claims 1-7, comprising: A monitoring module, a main frequency mode component acquisition module, and a distribution network fault line selection module, characterized in that: Monitoring module: Monitor the zero-sequence voltage of the power system in real time, and judge whether the power system is faulty according to the zero-sequence voltage; if it is faulty, record and collect the transient zero-sequence current for a set multiple of cycles after the fault, and obtain the transient zero-sequence current signal with noise on the feeder; Main frequency mode component acquisition module: Decompose the transient zero-sequence current signal with noise through modal decomposition and adaptive noise algorithm to obtain multiple intrinsic mode components, and select the main frequency mode components from the intrinsic mode components according to the energy, spectrum and time-domain correlation of the intrinsic mode components; Distribution network fault line selection module: Calculate the difference between the energy of the main frequency mode component of each feeder and the sum of the energies of the main frequency mode components of other feeders, and the sum of the correlation coefficients between each feeder and other feeders; if there is a feeder that satisfies both the minimum energy difference and the sum of the correlation coefficients less than zero, then this feeder is the faulty feeder, otherwise return to monitor the zero-sequence voltage of the power system, and judge whether the power system is faulty according to the zero-sequence voltage.

9. A device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it uses the steps of the method for fault line selection in a distribution network based on modal decomposition and adaptive noise according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, it uses the steps of the method for fault line selection in a distribution network based on modal decomposition and adaptive noise according to any one of claims 1 to 7.

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