Power distribution network fault identification method and system based on power spectrum density entropy, medium and terminal

Abstract

The invention discloses a power spectrum density entropy-based power distribution network fault identification method and system, a medium and a terminal, and the method comprises the steps: collecting the three-phase current of each branch feeder line of a power distribution network, and extracting the high-frequency component of the three-phase current; calculating a power spectrum density value of the high-frequency component of the three-phase current of each branch feeder line according to the extracted high-frequency component of the three-phase current; calculating a power spectral density entropy value of each branch feeder line based on the power spectral density value, comparing the power spectral density entropy values of different branch lines, and determining a fault branch line; and comparing the current power spectrum density entropy values of each phase of the fault branch line, and judging the fault type. The problem that in a traditional technical scheme, fault type identification is not accurate due to the fact that a power distribution network line is affected by the operation state and noise is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault detection and identification, and in particular to a distribution network fault identification method, system, medium and terminal based on power spectrum density entropy. Background Art

[0002] As a link closely connected with users, the distribution network is the key to ensuring reliable power supply for daily production and life. However, the distribution network has a complex line structure, a harsh operating environment, and a high probability of faults, which seriously affects the power supply reliability of the distribution network. Accurate fault identification is the basis for accelerating fault isolation and power supply restoration. Currently, the commonly used fault identification methods can be divided into steady-state signal methods and transient signal methods. Steady-state signals often use sequence components of voltage and current for identification, and transient signal methods often use high-frequency components for identification, but the above methods are easily affected by operating conditions and noise.

[0003] Therefore, a distribution network fault identification method based on power spectral density entropy is proposed to improve the accuracy of distribution network line fault type identification. Summary of the invention

[0004] The present invention provides a distribution network fault identification method, system, medium and terminal based on power spectrum density entropy, which solves the problem of inaccurate fault type identification caused by the distribution network line being affected by operating status and noise in traditional technical solutions.

[0005] In a first aspect, the present invention provides a distribution network fault identification method based on power spectrum density entropy, comprising:

[0006] S1: Collect the three-phase current at the outlet of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current;

[0007] S2: Calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder according to the extracted high-frequency component of the three-phase current;

[0008] S3: Calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line;

[0009] S4: Compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

[0010] The method only needs to collect the current data at the outlet of each feeder, which reduces the difficulty of obtaining the sampled data; uses the modal analysis method to extract the high-frequency components of the three-phase current data, reduces the interference of noise signals during the sampling process, and improves the reliability of identification; by calculating the power spectrum density entropy value of the high-frequency component, the identification principle of the line fault type is established to achieve accurate identification of the distribution network line fault; it has a certain ability to resist noise interference, the calculation process is simple, and has good engineering practicality. In view of the fact that the steady-state method is affected by the operating state of the system, the method of the present invention calculates the power spectrum density entropy of the fault sampling current, compares the size of the entropy value to identify the fault, and is not affected by changes in the system operating mode.

[0011] Furthermore, in S1, the high-frequency component extraction of the three-phase current is to decompose the three-phase current sampling signals collected at the outlets of each branch feeder of the distribution network using the robust local mean decomposition algorithm, and select the component signals of the preset items after decomposition as the high-frequency components. In the present invention, the PF is selected 1 (n) is the high-frequency component of the three-phase current sampling signal; the decomposed three-phase current signal is expressed as:

[0012]

[0013] Among them, x(n) is the three-phase current sampling signal; PF l (n) is the component signal after decomposition; k is the number of decompositions; u k (n) is the residual signal. The method uses a modal analysis method to extract high-frequency components of three-phase current data and reduce the interference of noise signals during the sampling process.

[0014] Furthermore, the specific process of calculating the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder is as follows:

[0015] S21: performing discrete time Fourier transform on the high frequency components of each sampling current in the three-phase current sampling signal to obtain spectrum data;

[0016] S22: Calculate the power spectrum density of the high-frequency component of the current based on the obtained spectrum data. The calculation formula is:

[0017]

[0018] Among them, S(f k ) is the power spectrum density of the high-frequency component of the current; x(f k ) is the spectrum data of the high-frequency component of the three-phase current; N is the number of samples of the high-frequency component x(n) of the three-phase current.

[0019] Furthermore, the specific process of S3 is as follows:

[0020] S31: Calculate the power spectrum density entropy of the three-phase current based on the power spectrum density of the high-frequency component of the three-phase current. The calculation formula is:

[0021]

[0022] Where E(S) is the power spectrum density entropy of the high-frequency component of the three-phase current; N is the number of samples of the high-frequency component x(n) of the three-phase current; S(q i ) is the probability distribution of three-phase current power spectrum density data; q i is the power spectrum density data of the high-frequency components of the three-phase current;

[0023] S32: According to the three-phase current power spectrum density entropy value of each branch feeder, the characteristic quantity of the power spectrum density entropy value of each branch current is calculated and obtained. The calculation formula is:

[0024]

[0025] Among them, R i is the characteristic value of the current power spectrum density entropy value of the i-th branch feeder; E iA is the current power spectrum density entropy value of phase A of the i-th branch feeder; E iB is the current power spectrum density entropy value of phase B of the i-th branch feeder; E iC is the current power spectrum density entropy value of phase C of the i-th branch feeder; n is the number of branch feeders;

[0026] S33: Determine whether the characteristic quantity of the current power spectrum density entropy value of each branch feeder is greater than or equal to a first preset threshold value: if so, it indicates that the branch feeder is a fault line; if not, it indicates that the branch feeder is normal.

[0027] Furthermore, the specific process of S4 is as follows:

[0028] S41: Calculate the characteristic value of the power spectrum density entropy value of each phase current of the fault line. The calculation formula is:

[0029]

[0030] Among them, R ij is the characteristic quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E ij is the power spectrum density entropy value of each phase current of the i-th branch feeder, j = A, B, C; n is the number of branch feeders;

[0031] S42: Determine whether the characteristic quantity of the power spectrum density entropy value of each phase current of the fault line i is greater than or equal to a second preset threshold: if not, return to S1; if yes, enter S43;

[0032] S43: Determine whether the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is less than 2. If so, determine that the fault type of the fault line i is a single-phase grounding fault; if not, proceed to S44;

[0033] S44: Calculate the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i. If the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is greater than or equal to the third preset threshold, determine that the fault type of the fault line i is a phase-to-phase short circuit fault; if the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 2, determine that the fault type of the fault line i is a two-phase grounding fault; if the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 3, determine that the fault type of the fault line i is a three-phase grounding fault.

[0034] Furthermore, the calculation formula of the auxiliary quantity of the three-phase current power spectrum density entropy value is:

[0035]

[0036] Among them, P ij is the auxiliary quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E iABC is the power spectrum density entropy value of the three-phase composite current of the i-th branch feeder.

[0037] In a second aspect, the present invention provides a distribution network fault identification system based on power spectrum density entropy, comprising:

[0038] Data acquisition module: used to collect the three-phase current of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current;

[0039] Power spectrum density value acquisition module: used to calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder according to the extracted high-frequency component of the three-phase current;

[0040] Fault line determination module: used to calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line;

[0041] Fault type determination module: used to compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

[0042] In a third aspect, the present invention provides an electronic terminal, comprising a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above method.

[0043] In a fourth aspect, the present invention provides a readable storage medium storing a computer program, wherein the computer program is used to execute the steps of the method described above when called by a processor.

[0044] Beneficial Effects

[0045] The present invention proposes a distribution network fault identification method, system, medium and terminal based on power spectrum density entropy, wherein the method does not need to consider phase mode transformation, and in the identification process, only the current data at the outlet of each feeder needs to be collected, and the ratio between the current data of each node and the adjacent nodes does not need to be obtained, so as to realize the identification of the fault type and the line. The method uses the modal analysis method to extract the high-frequency components of the three-phase current data to reduce the interference of the noise signal during the sampling process; by calculating the power spectrum density entropy value of the high-frequency component, the identification principle of the line fault type is established to realize the accurate identification of the distribution network line fault; the method does not need a high sampling rate, has a certain ability to resist noise interference, the calculation process is simple, and has good engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flow chart of a distribution network fault identification method based on power spectrum density entropy provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of a multi-branch power distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0050] Example 1

[0051] like Figure 1 As shown, the present invention provides a distribution network fault identification method based on power spectrum density entropy, comprising:

[0052] S1: Collect the three-phase current at the outlet of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current.

[0053] The high-frequency component extraction of the three-phase current is to use the robust local mean decomposition algorithm to decompose the three-phase current sampling signals collected at the outlets of each branch feeder of the distribution network, and select the component signals of the preset items after decomposition as the high-frequency components. In the present invention, the PF is selected 1 (n) is the high frequency component of the three-phase current sampling signal. In view of the fact that the steady-state method is affected by the system operation state, the method described in this embodiment calculates the power spectrum density entropy of the fault sampling current, compares the entropy value to identify the fault, and is not affected by changes in the system operation mode.

[0054] The specific decomposition process of the robust local mean decomposition algorithm is: by performing local mean operation and local envelope operation on the original signal of the three-phase current, the local mean function and local envelope function of the signal are determined, and then the high-frequency product function component is separated from the original signal. This process is repeated until the remaining component is a monotonic function or a trend term.

[0055] The decomposed three-phase current signal is expressed as:

[0056]

[0057] Among them, x(n) is the three-phase current sampling signal; PF l (n) is the component signal after decomposition; k is the number of decompositions; u k (n) is the residual signal.

[0058] S2: Calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder based on the extracted high-frequency component of the three-phase current.

[0059] Specifically, S21: Since the current data obtained by sampling is discrete data, the high-frequency components of the three-phase current are extracted as discrete signals, and then the high-frequency components after decomposition of each sampled current are subjected to discrete-time Fourier transform to obtain spectrum data; wherein the discrete-time Fourier transform formula is:

[0060]

[0061] Among them, x(f k ) is the spectrum data of high frequency components; PF 1 (n) is the high-frequency component after decomposition of the three-phase current x(n); f k =k / N, k is the number of Fourier series components; N is the number of samples of the high-frequency component x(n) of the three-phase current;

[0062] S22: Calculate the power spectrum density of the high-frequency components of the three-phase current based on the obtained spectrum data. The calculation formula is:

[0063]

[0064] Among them, S(f k ) is the power spectral density of the high-frequency components of the three-phase current.

[0065] S3: Calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line.

[0066] S31: Calculate the power spectrum density entropy of the three-phase current high-frequency component power spectrum density. The calculation formula is:

[0067]

[0068] Where E(S) is the power spectral density entropy of the high-frequency component of the three-phase current; N is the high-frequency component PF of the current 1 (n) the number of samples; S(q i ) is the probability distribution of current power spectrum density data; q i is the power spectrum density data of high frequency components;

[0069] S32: According to the three-phase current power spectrum density entropy value of each branch feeder, the characteristic quantity of the power spectrum density entropy value of each branch current is calculated and obtained. The calculation formula is:

[0070]

[0071] Among them, R i is the characteristic value of the current power spectrum density entropy value of the i-th branch feeder; E iA is the current power spectrum density entropy value of phase A of the i-th branch feeder; E iB is the current power spectrum density entropy value of phase B of the i-th branch feeder; E iC is the current power spectrum density entropy value of phase C of the i-th branch feeder; n is the number of branch feeders;

[0072] S33: Determine whether the characteristic quantity of the current power spectrum density entropy value of each branch feeder is greater than or equal to a first preset threshold λ: if so, it indicates that the branch feeder is a fault line; if not, it indicates that the branch feeder is normal.

[0073] S4: Compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

[0074] This embodiment uses a modal analysis method to extract high-frequency components of three-phase current data to reduce the interference of noise signals during the sampling process; by calculating the power spectral density entropy value of the high-frequency component, an identification principle for line fault types is established to achieve accurate identification of distribution network line faults; the sampling rate is usually 10kHz, and a higher sampling rate is not required. It has a certain ability to resist noise interference, the calculation process is simple, and it has good engineering practicality.

[0075] S41: Calculate the characteristic value of the power spectrum density entropy value of each phase current of the fault line. The calculation formula is:

[0076]

[0077] Among them, R ij is the characteristic quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E ij is the power spectrum density entropy value of each phase current of the i-th branch feeder, j = A, B, C; n is the number of branch feeders;

[0078] S42: Determine whether the characteristic quantity of the power spectrum density entropy value of each phase current of the fault line i is greater than or equal to the second preset threshold value, (the setting of the third preset threshold value can be adjusted according to the actual situation and is not limited. In this embodiment, the second preset threshold value is set to λ / 3): if not, return to S1; if yes, enter S43;

[0079] S43: Determine whether the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is less than 2. If so, determine that the fault type of the fault line i is a single-phase grounding fault; if not, proceed to S44;

[0080] S44: Calculate the auxiliary amount of the power spectrum density entropy value of the three-phase current of the fault line i. If the auxiliary amount of the power spectrum density entropy value of the three-phase current of the fault line i is greater than or equal to the third preset threshold (the setting of the third preset threshold can be adjusted according to the actual situation and is not limited. In this embodiment, the third preset threshold is set to 0.1λ), the fault type of the fault line i is determined to be a phase-to-phase short circuit fault; if the auxiliary amount of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 2, then the fault type of the fault line i is determined to be a two-phase grounding fault; if the auxiliary amount of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 3, then the fault type of the fault line i is determined to be a three-phase grounding fault.

[0081] More specifically, the calculation formula of the auxiliary quantity of the three-phase current power spectrum density entropy value is:

[0082]

[0083] Among them, P ij is the auxiliary quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E iABC is the power spectrum density entropy value of the three-phase composite current of the i-th branch feeder.

[0084] Example 2

[0085] This embodiment provides a distribution network fault identification system based on power spectrum density entropy, including:

[0086] Data acquisition module: used to collect the three-phase current of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current;

[0087] Power spectrum density value acquisition module: used to calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder according to the extracted high-frequency component of the three-phase current;

[0088] Fault line determination module: used to calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line;

[0089] Fault type determination module: used to compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

[0090] Example 3

[0091] This embodiment provides an electronic terminal, including a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above method.

[0092] Example 4

[0093] This embodiment provides a readable storage medium storing a computer program, and when the computer program is called by a processor, the computer program executes the steps of the above method.

[0094] In order to explain the technical solution of the present application in more detail, the present application is as follows Figure 2 In the distribution network shown, a phase A grounding fault is simulated at line L5, and the first preset threshold is set to 0.5. When line L5 fails, according to the above method, the power spectrum density entropy value characteristic quantity of line L5 is 0.8053, which is greater than the threshold value 0.5, and the power spectrum density entropy characteristic quantities of the remaining lines are all less than 0.5. Therefore, L5 is determined to be a faulty line.

[0095] The power spectrum density entropy characteristic quantity of each phase current of line L5 is further calculated. According to the above method, the power spectrum density entropy characteristic quantity of phase A current of line L5 is 0.7678, which is greater than the second preset threshold value 0.5 / 3, and the power spectrum density entropy characteristic quantities of the remaining phase currents are all less than the third preset threshold value 0.5 / 3. Therefore, the fault type is determined to be a phase A grounding fault.

[0096] It should be understood that in the embodiments of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the type of device.

[0097] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as a hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the controller. Furthermore, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0098] Based on this understanding, the technical solution of the present invention, in essence or in other words, the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0099] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0100] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A distribution network fault identification method based on power spectrum density entropy, characterized in that: include: S1: Collect the three-phase current at the outlet of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current; S2: Calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder according to the extracted high-frequency component of the three-phase current; S3: Calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line; S4: Compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

2. The method for identifying distribution network faults based on power spectrum density entropy according to claim 1, characterized in that: In S1, the high-frequency component extraction of the three-phase current is to decompose the three-phase current sampling signals collected at the outlets of each branch feeder of the distribution network using the robust local mean decomposition algorithm, and select the component signals of the preset items after decomposition as the high-frequency components; the decomposed three-phase current signal is expressed as: Among them, x(n) is the three-phase current sampling signal; PF l (n) is the component signal after decomposition; k is the number of decompositions; u k (n) is the residual signal.

3. The method for identifying distribution network faults based on power spectrum density entropy according to claim 1, characterized in that: In S2, the specific process of calculating the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder is as follows: S21: performing discrete time Fourier transform on the high frequency components of each sampling current in the three-phase current sampling signal to obtain spectrum data; S22: Calculate the power spectrum density of the high-frequency components of the three-phase current based on the obtained spectrum data. The calculation formula is: Among them, S(f k ) is the power spectrum density of the high-frequency component of the current; x(f k ) is the spectrum data of the high-frequency component of the three-phase current; N is the number of samples of the high-frequency component x(n) of the three-phase current.

4. The method for identifying distribution network faults based on power spectrum density entropy according to claim 1, characterized in that: The specific process of S3 is as follows: S31: Calculate the power spectrum density entropy of the three-phase current based on the power spectrum density of the high-frequency component of the three-phase current. The calculation formula is: Where E(S) is the power spectrum density entropy of the high-frequency component of the three-phase current; N is the number of samples of the high-frequency component x(n) of the three-phase current; S(q i ) is the probability distribution of three-phase current power spectrum density data; q i is the power spectrum density data of the high-frequency components of the three-phase current; S32: According to the three-phase current power spectrum density entropy value of each branch feeder, the characteristic quantity of the power spectrum density entropy value of each branch current is calculated and obtained. The calculation formula is: Among them, R i is the characteristic value of the current power spectrum density entropy value of the i-th branch feeder; E iA is the current power spectrum density entropy value of phase A of the i-th branch feeder; E iB is the current power spectrum density entropy value of phase B of the i-th branch feeder; E iC is the current power spectrum density entropy value of phase C of the i-th branch feeder; n is the number of branch feeders; S33: Determine whether the characteristic quantity of the current power spectrum density entropy value of each branch feeder is greater than or equal to a first preset threshold value: if so, it indicates that the branch feeder is a fault line; if not, it indicates that the branch feeder is normal.

5. The method for identifying distribution network faults based on power spectrum density entropy according to claim 4, characterized in that: The specific process of S4 is as follows: S41: Calculate the characteristic value of the power spectrum density entropy value of each phase current of the fault line. The calculation formula is: Among them, R ij is the characteristic quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E ij is the power spectrum density entropy value of each phase current of the i-th branch feeder, j = A, B, C; n is the number of branch feeders; S42: Determine whether the characteristic quantity of the power spectrum density entropy value of each phase current of the fault line i is greater than or equal to a second preset threshold: if not, return to S1; if yes, enter S43; S43: Determine whether the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is less than 2. If so, determine that the fault type of the fault line i is a single-phase grounding fault; if not, proceed to S44; S44: Calculate the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i. If the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is greater than or equal to the third preset threshold, determine that the fault type of the fault line i is a phase-to-phase short circuit fault; if the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 2, determine that the fault type of the fault line i is a two-phase grounding fault; if the auxiliary quantity of the power spectrum density entropy value of the three-phase current of the fault line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectrum density entropy value greater than or equal to the second preset threshold is 3, determine that the fault type of the fault line i is a three-phase grounding fault.

6. The method for identifying distribution network faults based on power spectrum density entropy according to claim 5, characterized in that: The calculation formula of the auxiliary quantity of the three-phase current power spectrum density entropy value is: Among them, P ij is the auxiliary quantity of the power spectrum density entropy value of each phase current of the i-th branch feeder; E iABC is the power spectrum density entropy value of the three-phase composite current of the i-th branch feeder.

7. A distribution network fault identification system based on power spectrum density entropy, characterized in that: include: Data acquisition module: used to collect the three-phase current of each branch feeder of the distribution network and extract the high-frequency component of the three-phase current; Power spectrum density value acquisition module: used to calculate the power spectrum density value of the high-frequency component of the three-phase current of each branch feeder according to the extracted high-frequency component of the three-phase current; Fault line determination module: used to calculate the power spectrum density entropy value of each branch feeder based on the power spectrum density value, and compare the power spectrum density entropy values ​​of different branch lines to determine the faulty branch line; Fault type determination module: used to compare the power spectrum density entropy values ​​of each phase current of the fault branch line to determine the fault type.

8. An electronic terminal, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that: A computer program is stored, and when the computer program is called by a processor, the computer program is used to execute: the steps of the method according to any one of claims 1 to 6.

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