A power spectrum density entropy-based power distribution network fault identification method, system, medium and terminal

Abstract

This invention discloses a method, system, medium, and terminal for distribution network fault identification based on power spectral density entropy. The method includes: collecting the three-phase currents of each branch feeder in the distribution network and extracting the high-frequency components of the three-phase currents; calculating the power spectral density value of the high-frequency components of the three-phase currents of each branch feeder based on the extracted high-frequency components; calculating the power spectral density entropy value of each branch feeder based on the power spectral density value, comparing the magnitudes of the power spectral density entropy values ​​of different branch lines, and determining the faulty branch line; comparing the magnitudes of the power spectral density entropy values ​​of each phase current of the faulty branch line to determine the fault type. This solves the problem of inaccurate fault type identification caused by the influence of operating conditions and noise on distribution network lines in traditional technical solutions.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault detection and identification technology, and in particular to a power distribution network fault identification method, system, medium and terminal based on power spectral density entropy. Background Technology

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

[0003] To address this, a power spectral density entropy-based method for distribution network fault identification is proposed to improve the accuracy of fault type identification in distribution network lines. Summary of the Invention

[0004] This invention provides a method, system, medium, and terminal for power distribution network fault identification based on power spectral density entropy. The method solves the problem of inaccurate fault type identification caused by the influence of operating status and noise on distribution network lines in traditional technical solutions.

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

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

[0007] S2: Calculate the power spectral density value of the high-frequency components of the three-phase current in each branch feeder based on the extracted high-frequency components of the three-phase current.

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

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

[0010] The method described herein only requires collecting current data at each feeder outlet, reducing the difficulty of acquiring sampling data. It utilizes modal analysis to extract high-frequency components of the three-phase current data, reducing noise interference during sampling and improving identification reliability. By calculating the power spectral density entropy of the high-frequency components, it establishes a principle for identifying line fault types, achieving accurate identification of distribution network line faults. It possesses a certain degree of noise immunity, a simple calculation process, and good engineering applicability. Addressing the issue that steady-state methods are affected by system operating conditions, the method described in this invention identifies faults by calculating the power spectral density entropy of the fault sampling current and comparing the entropy values, unaffected by changes in system operating conditions.

[0011] Further, in S1, the high-frequency component extraction of the three-phase current is achieved by using a robust local mean decomposition algorithm to decompose the three-phase current sampling signals collected at the feeder outlets of each branch of the distribution network, and selecting the component signals of the preset terms after decomposition as the high-frequency components. In this invention, PF1(n) is selected as the high-frequency component of the three-phase current sampling signal; the decomposed three-phase current signal is expressed as:

[0012]

[0013] Where x(n) is the three-phase current sampling signal; PF l (n) represents the decomposed component signals; k is the number of decompositions; u k (n) represents the margin signal. The method utilizes modal analysis to extract the high-frequency components of the three-phase current data, reducing interference from noise signals during the sampling process.

[0014] Furthermore, the specific process for calculating the power spectral density values ​​of the high-frequency components of the three-phase current in each branch feeder is as follows:

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

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

[0017]

[0018] Wherein, S(f k ) represents the power spectral density of the high-frequency component of the current; x(f) k ) represents the spectral data of the high-frequency components of the three-phase current; N represents the number of samples of the high-frequency components x(n) of the three-phase current.

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

[0020] S31: Calculate the three-phase current power spectral density entropy for the high-frequency components of the three-phase current. The calculation formula is as follows:

[0021]

[0022] Where E(S) is the power spectral 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 ) represents the probability distribution of the three-phase current power spectral density data; q i The power spectral density data are for the high-frequency components of the three-phase current.

[0023] S32: Based on the three-phase current power spectral density entropy value of each branch feeder, calculate the characteristic quantity of the current power spectral density entropy value of each branch. The calculation formula is as follows:

[0024]

[0025] Among them, R i E is a characteristic quantity of the current power spectral density entropy of the i-th branch feeder; iA E represents the entropy value of the current power spectral density of phase A in the i-th branch feeder; iB E represents the entropy value of the current power spectral density of phase B of the i-th branch feeder; iC Let be the current power spectral 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 spectral density entropy value of each branch feeder is greater than or equal to the first preset threshold: if yes, it indicates that the branch feeder is a faulty line; if no, it indicates that the branch feeder is normal.

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

[0028] S41: A characteristic quantity for calculating the entropy of the power spectral density of each phase current in a faulty line. The calculation formula is as follows:

[0029]

[0030] Among them, R ij E represents the characteristic quantity of the entropy value of the power spectral density of each phase current of the i-th branch feeder; ij Let j be the entropy value of the power spectral density 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 spectral density entropy value of each phase current of the faulty line i is greater than or equal to the second preset threshold: if not, return to S1; if yes, proceed to S43.

[0032] S43: Determine whether the number of characteristic quantities that are greater than or equal to the second preset threshold current power spectral density entropy value is less than 2. If yes, determine that the fault type of faulty line i is a single-phase ground fault; otherwise, proceed to S44.

[0033] S44: Calculate the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is greater than or equal to the third preset threshold, then the fault type of faulty line i is determined to be a phase-to-phase short-circuit fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of current power spectral density entropy values ​​greater than or equal to the second preset threshold is 2, then the fault type of faulty line i is determined to be a two-phase ground fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of current power spectral density entropy values ​​greater than or equal to the second preset threshold is 3, then the fault type of faulty line i is determined to be a three-phase ground fault.

[0034] Furthermore, the formula for calculating the auxiliary quantity of the three-phase current power spectral density entropy is as follows:

[0035]

[0036] Among them, P ij E is an auxiliary quantity representing the entropy value of the power spectral density of each phase current in the i-th branch feeder; iABC Let be the entropy value of the three-phase combined current power spectral density of the i-th branch feeder.

[0037] Secondly, the present invention provides a power distribution network fault identification system based on power spectral density entropy, comprising:

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

[0039] Power spectral density value acquisition module: used to calculate the power spectral 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.

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

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

[0042] Thirdly, the present invention provides an electronic terminal, including a processor and a memory, wherein the memory stores a computer program, and the processor invokes the computer program to perform the steps of the method described above.

[0043] Fourthly, the present invention provides a readable storage medium storing a computer program, which, when invoked by a processor, performs the steps of the method described above.

[0044] Beneficial effects

[0045] This invention proposes a method, system, medium, and terminal for distribution network fault identification based on power spectral density entropy. The method eliminates the need for phase mode transformation and, during identification, only requires collecting current data at each feeder outlet, without needing to obtain the ratio of current data at each node to that of adjacent nodes, thus enabling fault type and line identification. The method utilizes modal analysis to extract high-frequency components of the three-phase current data, reducing noise interference during sampling. By calculating the power spectral density entropy value of the high-frequency components, a fault type identification principle is established, achieving accurate identification of distribution network line faults. The method does not require a high sampling rate, possesses a certain degree of noise immunity, has a simple calculation process, and demonstrates good engineering applicability. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of a power spectral density entropy-based distribution network fault identification method provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of a multi-control power grid provided in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0050] Example 1

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

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

[0053] The high-frequency component extraction of three-phase current is achieved by using a robust local mean decomposition algorithm to decompose the three-phase current sampling signals collected at the feeder outlets of each branch of the distribution network. The component signals of the decomposed preset terms are selected as the high-frequency components. In this invention, PF1(n) is selected as the high-frequency component of the three-phase current sampling signal. To address the influence of system operating conditions on steady-state methods, the method described in this embodiment identifies faults by calculating the power spectral density entropy of the fault sampling current and comparing the entropy values, thus remaining unaffected by changes in system operating conditions.

[0054] The specific process of the robust local mean decomposition algorithm is as follows: 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. Then, the high-frequency product function component is separated from the original signal. This process is repeated until the remaining components are monotonic functions or trend terms.

[0055] The decomposed three-phase current signals are represented as follows:

[0056]

[0057] Where x(n) is the three-phase current sampling signal; PF l (n) represents the decomposed component signals; k is the number of decompositions; u k (n) represents the margin signal.

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

[0059] Specifically, S21: Since the sampled current data is discrete data, the high-frequency components of the three-phase current are extracted as discrete signals. Then, the high-frequency components of each sampled current are subjected to a discrete-time Fourier transform to obtain the spectrum data. The discrete-time Fourier transform formula is:

[0060]

[0061] Where x(f) k ) represents the spectral data of the high-frequency components; PF1(n) represents the high-frequency components after decomposition of the three-phase current x(n); f k = k / N, where 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 spectral density of the high-frequency components of the three-phase current based on the obtained spectrum data. The calculation formula is as follows:

[0063]

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

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

[0066] S31: Calculate the current power spectral density entropy for the high-frequency components of the three-phase current. The calculation formula is as follows:

[0067]

[0068] Where E(S) is the power spectral density entropy of the high-frequency components of the three-phase current; N is the number of samples of the high-frequency current component PF1(n); S(q i ) represents the probability distribution of the current power spectral density data; q i The power spectral density data are for the high-frequency components;

[0069] S32: Based on the three-phase current power spectral density entropy value of each branch feeder, calculate the characteristic quantity of the current power spectral density entropy value of each branch. The calculation formula is as follows:

[0070]

[0071] Among them, R i E is a characteristic quantity of the current power spectral density entropy of the i-th branch feeder; iA E represents the entropy value of the current power spectral density of phase A in the i-th branch feeder; iB E represents the entropy value of the current power spectral density of phase B of the i-th branch feeder; iC Let be the current power spectral 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 spectral density entropy value of each branch feeder is greater than or equal to the first preset threshold λ: if yes, it indicates that the branch feeder is a faulty line; if no, it indicates that the branch feeder is normal.

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

[0074] This embodiment utilizes modal analysis to extract high-frequency components of three-phase current data, reducing noise interference during sampling. By calculating the power spectral density entropy of the high-frequency components, a principle for identifying line fault types is established, enabling accurate identification of distribution network line faults. The sampling rate is typically 10kHz, eliminating the need for higher sampling rates. It possesses a certain degree of noise immunity, and the calculation process is simple, demonstrating good engineering applicability.

[0075] S41: A characteristic quantity for calculating the entropy of the power spectral density of each phase current in a faulty line. The calculation formula is as follows:

[0076]

[0077] Among them, R ij E represents the characteristic quantity of the entropy value of the power spectral density of each phase current of the i-th branch feeder; ij Let j be the entropy value of the power spectral density 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 current power spectral density entropy value of each phase of the faulty line i is greater than or equal to the second 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 second preset threshold is set to λ / 3): If not, return to S1; if yes, proceed to S43.

[0079] S43: Determine whether the number of characteristic quantities that are greater than or equal to the second preset threshold current power spectral density entropy value is less than 2. If yes, determine that the fault type of faulty line i is a single-phase ground fault; otherwise, proceed to S44.

[0080] S44: Calculate the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty 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), then the fault type of faulty line i is determined to be a phase-to-phase short-circuit fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectral density entropy value greater than or equal to the second preset threshold is 2, then the fault type of faulty line i is determined to be a two-phase ground fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of the current power spectral density entropy value greater than or equal to the second preset threshold is 3, then the fault type of faulty line i is determined to be a three-phase ground fault.

[0081] More specifically, the formula for calculating the auxiliary quantity of the three-phase current power spectral density entropy is as follows:

[0082]

[0083] Among them, P ij E is an auxiliary quantity representing the entropy value of the power spectral density of each phase current in the i-th branch feeder; iABC Let be the entropy value of the three-phase combined current power spectral density of the i-th branch feeder.

[0084] Example 2

[0085] This embodiment provides a power grid fault identification system based on power spectral density entropy, including:

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

[0087] Power spectral density value acquisition module: used to calculate the power spectral 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.

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

[0089] Fault type determination module: Used to compare the magnitude of the power spectral density entropy values ​​of each phase current of the faulty 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 perform the steps of the method described above.

[0092] Example 4

[0093] This embodiment provides a readable storage medium storing a computer program that, when invoked by a processor, performs the steps of the method described above.

[0094] To illustrate the technical solution of this application in more detail, this application uses the following... Figure 2 In the power distribution network shown, a phase A ground fault is simulated at line L5. The first preset threshold is set to 0.5. When a fault occurs at line L5, the characteristic value of the power spectral density entropy of line L5 is 0.8053 according to the above method, which is greater than the threshold of 0.5. The characteristic values ​​of the power spectral density entropy of the other lines are all less than 0.5. Therefore, L5 is determined to be a faulty line.

[0095] Further calculation of the power spectral density entropy characteristic of each phase current of line L5. According to the above method, the power spectral density entropy characteristic of phase A current of line L5 is 0.7678, which is greater than the second preset threshold of 0.5 / 3, and the power spectral density entropy characteristic of the other phase currents is less than the third preset threshold of 0.5 / 3. Therefore, the fault type is determined to be phase A ground fault.

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

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

[0098] Based on this understanding, the technical solution of the present invention, in essence, or 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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

[0100] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for fault identification in distribution networks based on power spectral density entropy, characterized in that, include: S1: Collect the three-phase current at the feeder outlet of each branch of the distribution network and extract the high-frequency components of the three-phase current; S2: Calculate the power spectral density value of the high-frequency components of the three-phase current in each branch feeder based on the extracted high-frequency components of the three-phase current. S3: Calculate the power spectral density entropy value of each branch feeder based on the power spectral density value, compare the magnitude of the power spectral density entropy values ​​of different branch lines, and determine the faulty branch line. S31: Calculate the three-phase current power spectral density entropy for the high-frequency components of the three-phase current. The calculation formula is as follows: ； in, The power spectral density entropy of the high-frequency components of the three-phase current; High-frequency components of three-phase current The number of samples; The probability distribution of the three-phase current power spectral density data; The power spectral density data are for the high-frequency components of the three-phase current. S32: Based on the three-phase current power spectral density entropy value of each branch feeder, calculate the characteristic quantity of the current power spectral density entropy value of each branch. The calculation formula is as follows: ； in, Let be the characteristic quantity of the current power spectral density entropy value of the i-th branch feeder; Let be the entropy value of the current power spectral density of phase A of the i-th branch feeder; Let be the current power spectral density entropy value of phase B of the i-th branch feeder; Let be the current power spectral 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 spectral density entropy value of each branch feeder is greater than or equal to the first preset threshold: if yes, it indicates that the branch feeder is a faulty line; if no, it indicates that the branch feeder is normal. S4: Compare the magnitudes of the power spectral density entropy values ​​of each phase current in the faulty branch line to determine the fault type; S41: A characteristic quantity for calculating the entropy of the power spectral density of each phase current in a faulty line. The calculation formula is as follows: ； Among them, R ij E represents the characteristic quantity of the entropy value of the power spectral density of each phase current of the i-th branch feeder; ij Let j be the entropy value of the power spectral density 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 spectral density entropy value of each phase current of the faulty line i is greater than or equal to the second preset threshold: if not, return to S1; if yes, proceed to S43. S43: Determine whether the number of characteristic quantities that are greater than or equal to the second preset threshold current power spectral density entropy value is less than 2. If yes, determine that the fault type of faulty line i is a single-phase ground fault; otherwise, proceed to S44. S44: Calculate the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is greater than or equal to the third preset threshold, then the fault type of faulty line i is determined to be a phase-to-phase short-circuit fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of current power spectral density entropy values ​​greater than or equal to the second preset threshold is 2, then the fault type of faulty line i is determined to be a two-phase ground fault. If the auxiliary quantity of the three-phase current power spectral density entropy value of faulty line i is less than the third preset threshold, and the number of characteristic quantities of current power spectral density entropy values ​​greater than or equal to the second preset threshold is 3, then the fault type of faulty line i is determined to be a three-phase ground fault.

2. The distribution network fault identification method based on power spectral density entropy according to claim 1, characterized in that, In step S1, the high-frequency component extraction of the three-phase current is achieved by using a robust local mean decomposition algorithm to decompose the three-phase current sampling signals collected at the feeder outlets of each branch of the distribution network, and selecting the component signals of the preset terms after decomposition as the high-frequency components; the decomposed three-phase current signals are expressed as follows: ； in, This is a three-phase current sampling signal; These are the component signals after decomposition; The number of decompositions; This is the margin signal.

3. The distribution network fault identification method based on power spectral density entropy according to claim 1, characterized in that, In step S2, the specific process for calculating the power spectral density values ​​of the high-frequency components of the three-phase current in each branch feeder is as follows: S21: Perform discrete-time Fourier transform on the high-frequency components of each sampled current in the three-phase current sampling signal to obtain the spectrum data; S22: Calculate the power spectral density of the high-frequency components of the three-phase current based on the obtained spectrum data. The calculation formula is as follows: ； in, The power spectral density of the high-frequency component of the current; The spectral data of the high-frequency components of the three-phase current; High-frequency components of three-phase current The number of samples.

4. The distribution network fault identification method based on power spectral density entropy according to claim 1, characterized in that, The formula for calculating the auxiliary quantity of the three-phase current power spectral density entropy is: ； in, This is an auxiliary quantity representing the entropy value of the power spectral density of each phase current of the i-th branch feeder; Let be the entropy value of the three-phase combined current power spectral density of the i-th branch feeder.

5. A power distribution network fault identification system based on power spectral density entropy, wherein the system executes the method according to any one of claims 1-4, characterized in that, include: Data acquisition module: used to acquire the three-phase current of each branch feeder of the distribution network and extract the high-frequency components of the three-phase current; Power spectral density value acquisition module: used to calculate the power spectral 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. Fault line determination module: used to calculate the power spectral density entropy value of each branch feeder based on the power spectral density value, and compare the power spectral density entropy values ​​of different branch lines to determine the faulty branch line; Fault type determination module: Used to compare the magnitude of the power spectral density entropy values ​​of each phase current of the faulty branch line to determine the fault type.

6. An electronic terminal, characterized in that: It includes a processor and a memory, the memory storing a computer program, the processor calling the computer program to perform the steps of the method according to any one of claims 1-4.

7. A readable storage medium, characterized in that: A computer program is stored, which, when invoked by a processor, performs the steps of the method according to any one of claims 1-4.

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