A High-Resistance Fault Detection Method and System Based on Sorting Energy and IEMD

Abstract

The present invention discloses a high-resistance fault detection method and system based on sorting energy and IEMD. The method includes: obtaining the zero-sequence current of each feeder, performing a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder; performing improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, respectively calculating the kurtosis values of the plurality of characteristic components, and selecting the characteristic component with the largest kurtosis value as the fault characteristic component; performing normalization processing on the fault characteristic component, and obtaining the squared envelope curve of the normalized fault characteristic component; calculating the sorting energy of the squared envelope curve, and determining whether the sorting energy is greater than a threshold value. It can effectively distinguish high-resistance faults from normal disturbances, and greatly improve the reliability and accuracy of high-resistance fault detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of relay protection for distribution networks in power systems, and particularly relates to a high-impedance fault detection method and system based on sorted energy and IEMD. Background Art

[0002] The distribution network is an important part of the power system, responsible for transmitting electrical energy from the substation to the end users and ensuring the reliable supply of electricity. During this process, the distribution network may face various fault threats, among which high-impedance faults (HIFs) are particularly special. A high-impedance fault is a fault caused by a high-impedance contact between a wire and the ground or an obstacle. Its characteristics are that the fault current is small, it is difficult to be detected by traditional protection devices, and it is easy to be confused with normal disturbances such as capacitor switching and load switching. Although high-impedance faults do not immediately cause large-scale power outages, their concealment and unpredictability may bring a series of safety hazards, such as electrical fires, equipment damage, and personal injuries. Therefore, quickly and accurately detecting high-impedance faults is of great significance for ensuring the safe operation of the power system. Summary of the Invention

[0003] The present invention provides a high-impedance fault detection method and system based on sorted energy and IEMD, which is used to solve the technical problems in the prior art that the fault characteristics are weak during high-impedance faults, it is easy to be confused with normal disturbances, and the traditional protection devices are prone to failure.

[0004] In a first aspect, the present invention provides a high-impedance fault detection method based on sorted energy and IEMD, including:

[0005] Obtain the zero-sequence current of each feeder, and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ;

[0006] Perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, and calculate the kurtosis values of the plurality of characteristic components respectively , and select the characteristic component with the largest kurtosis value as the fault characteristic component ;

[0007] Perform normalization processing on the fault characteristic component , and obtain the square envelope curve of the fault characteristic component after normalization processing ; ;

[0008] Calculate the sorted energy of the square envelope curve , and judge the sorted energy ​Whether it is greater than the threshold ;

[0009] If it is greater than the threshold , it is determined as a high-resistance fault, otherwise it is a normal disturbance.

[0010] In a second aspect, the present invention provides a high-resistance fault detection system based on sorted energy and IEMD, including:

[0011] An acquisition module configured to acquire the zero-sequence current of each feeder and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ;

[0012] A decomposition module configured to perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, and calculate the kurtosis values of the plurality of characteristic components respectively , and select the characteristic component with the largest kurtosis value as the fault characteristic component ;

[0013] A processing module configured to perform normalization processing on the fault characteristic component and obtain the squared envelope curve of the normalized fault characteristic component ; ;

[0014] A judgment module configured to calculate the sorted energy of the squared envelope curve and judge whether the sorted energy is greater than the threshold ; ;

[0015] An output module configured to, if it is greater than the threshold , determine it as a high-resistance fault, otherwise it is a normal disturbance.

[0016] In a third aspect, there is provided an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the high-resistance fault detection method based on sorted energy and IEMD according to any embodiment of the present invention.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor is enabled to execute the steps of the high-resistance fault detection method based on sorted energy and IEMD according to any embodiment of the present invention.

[0018] The high-resistance fault detection method and system based on sorting energy and IEMD of the present application have the following beneficial effects:

[0019] In terms of fault signal decomposition: First, a soft SSC is proposed to monitor the screening process in EMD, which can improve the fault diagnosis efficiency, reduce the calculation cost without sacrificing the decomposition accuracy, and at the same time can select the optimal number of iterations and suppress the mode mixing problem of the EMD algorithm; Second, the mirror extension algorithm is used in IEMD to effectively suppress the endpoint effect; Finally, the kurtosis index is used to screen the characteristic components after IEMD decomposition, which can effectively select the characteristic components with the richest fault characteristic information, further laying a foundation for high-resistance fault detection;

[0020] In terms of the high-resistance fault detection criterion: The zero-sequence current waveform of the arcing high-resistance fault has the characteristics of zero rest and intermittency, while other load switching and capacitor switching only have the transient attenuation characteristics. Accordingly, the fault characteristic component with the largest kurtosis value obtained by decomposing the fault zero-sequence current using IEMD is normalized, and its squared envelope curve is obtained; Finally, the sorting energy of the squared envelope curve is calculated , and a threshold is set , and by comparing the sorting energy with the threshold , high-resistance fault detection is realized, which can effectively distinguish high-resistance faults from normal disturbances, greatly improving the reliability and accuracy of high-resistance fault detection. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 is a flowchart of a high-resistance fault detection method based on sorting energy and IEMD provided by an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of a 10 kV resonant grounding distribution network simulation model provided by an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of the test result waveform of an arcing high-resistance fault in a specific embodiment provided by an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of the test result waveform of capacitor switching in a specific embodiment provided by an embodiment of the present invention;

[0026] Figure 5 Waveform schematic diagram of the test result of load switching for a specific embodiment provided by an embodiment of the present invention;

[0027] Figure 6 Structural block diagram of a high-resistance fault detection system based on sorted energy and IEMD provided by an embodiment of the present invention;

[0028] Figure 7 Schematic structural diagram of an electronic device provided by an embodiment of the present invention. Specific embodiments

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Please refer to Figure 1 , which shows a flowchart of a high-resistance fault detection method based on sorted energy and IEMD of the present application.

[0031] As Figure 1 shown, the high-resistance fault detection method based on sorted energy and IEMD specifically includes the following steps:

[0032] Step S101: Obtain the zero-sequence current of each feeder, and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder .

[0033] In this step, the expression for calculating the differential zero-sequence current of each feeder is:

[0034] ,

[0035] wherein, is the zero-sequence current, is the zero-sequence current before the fault.

[0036] Step S102: Perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, and calculate the kurtosis values of the plurality of characteristic components respectively, and select the characteristic component with the largest kurtosis value and denote it as the fault characteristic component .

[0037] In this step, Empirical Mode Decomposition (EMD) is developed to decompose a discrete-time signal into multiple single-component signals. Ideally, each Intrinsic Mode Function (IMF) corresponds to a single component. In EMD, the number of sifting iterations is directly determined by the Screening Stop Criteria (SSC). The number of iterations has a significant impact on the mode mixing problem in two cases, namely "under-sifting" and "over-sifting". The case of under-sifting is that multiple single-component signals are decomposed into a single IMF, while the case of over-sifting is that a single-component signal is decomposed into multiple IMFs. Therefore, finding the optimal number of iterations helps to alleviate the mode mixing problem in EMD. If the number of iterations is too small, it may lead to under-sifting, resulting in the failure to extract the fault frequency band. If the number of iterations is too large, two problems will occur: one problem is the over-sifting phenomenon, that is, some irrelevant signals are forcibly decomposed into multiple IMFs, which may lead to misjudgment in fault diagnosis. Another problem is that as the number of iterations increases, the calculation time gradually increases. Vibration signals usually contain a large amount of data. Therefore, it is crucial to improve the efficiency of fault diagnosis and reduce the calculation cost without sacrificing the decomposition accuracy. The proposed soft SSC can monitor the sifting process in EMD. More importantly, it can select the optimal number of iterations. Therefore, the soft SSC can help EMD suppress its mode mixing problem.

[0038] It should be noted that the maximum number of iterations is set to , and let the th signal after the th sifting iteration be the th residual signal subtracted from the previous IMF , the fitness objective function of the th signal is 0, the selection index is 0, and the number of sifting times is 0;

[0039] Start the iteration, let , , ;

[0040] Use the signal after the th sifting iteration to calculate the envelope mean signal after the

[0041] th sifting iteration, and obtain the signal after the Greater than the objective function , then = 1, otherwise , ;

[0042] Judge the number of zeros and the number of extreme points whether the difference is less than a preset quantity threshold;

[0043] If so, judge whether it is greater than , otherwise re-iterate;

[0044] If so, then ;

[0045] If not, judge , otherwise re-iterate;

[0046] If so, then , otherwise re-iterate.

[0047] Furthermore, calculate the kurtosis value of several characteristic components The expression is:

[0048] ,

[0049] In the formula, is the total number of sampling signals, is the th mean signal, is the mean of the mean signal, is the sampling point number.

[0050] Step S103, normalize the fault characteristic component and obtain the squared envelope curve of the normalized fault characteristic component .

[0051] In this step, the expression for normalizing the fault characteristic component is:

[0052] ,

[0053] ,

[0054] In the formula, is the value of the th sampling point in the fault characteristic component , , is the sampling point with the largest amplitude in the fault characteristic component ​ is the th normalized fault feature component, is the value of the th sampling point in the fault feature component.

[0055] Obtain the squared envelope curve of the fault feature component after normalization , and its expression is:

[0056] ,

[0057] In the formula, is the th normalized fault feature component, is the value of the th sampling point in the squared envelope curve, is the Hilbert transform of the square of the normalized fault feature component .

[0058] Step S104, calculate the sorted energy of the squared envelope curve , and determine whether the sorted energy is greater than the threshold .

[0059] In this step, the expression for calculating the sorted energy of the squared envelope curve is:

[0060] ,

[0061] In the formula, is the numbered value after arranging the squared envelope curve in descending order of values, is the value after arranging the squared envelope curve in descending order of values, is the number of the last sampling point, is the numbered value of the sampling point.

[0062] Step S105, if it is greater than the threshold , then it is determined as a high-resistance fault, otherwise it is a normal disturbance.

[0063] In summary, for the method of this application, first, the zero-sequence current of each feeder is collected and differential calculation is performed on it; second, IEMD adaptive decomposition is performed on the obtained differential zero-sequence current, and the characteristic component with the largest kurtosis value is selected and denoted as the fault characteristic component; third, normalization processing is performed on the obtained fault characteristic component, and its square envelope curve is obtained; finally, the sorted energy of the square envelope curve is calculated, and a threshold is set, and high-resistance fault detection can be achieved by comparing the sorted energy with the threshold.

[0064] In a specific embodiment, PSCAD is used to build a 10 kV resonant grounding distribution network simulation model as Figure 2 shown. This model has a total of 5 outgoing lines. Among them, is an all-overhead line, is an all-cable line, is an overhead and cable hybrid line. The lengths of each line are clearly marked in the figure. The specific parameters of the lines are shown in Table 1. In addition, to represent the zero-sequence current transformers installed at the heads of each feeder. It is assumed that the system operates with over-compensation, the over-compensation degree is 10%, the inductance of the arc suppression coil is 0.4779 H, the resistance is 4.5040 Ω, and 0.5 MW constant impedance loads are used at the ends of each feeder.

[0065] ,

[0066] At the feeders single-phase high-resistance faults, capacitor switching, and load switching occur respectively. The initial fault phase angle is set to 30°, the system sampling frequency f = 10 kHz, the simulation duration is 0.5 s, and the fault occurrence time is set to 0.2 s.

[0067] Typical load switching, capacitor switching, and arcing high-resistance grounding fault test waveforms are selected to verify the proposed method. Their characteristic components and envelope curves are shown in Figure 3 , Figure 4 and Figure 5 respectively. From Figure 3 , Figure 4 and Figure 5 , it can be seen that the fault characteristic components extracted by IEMD can effectively characterize the current characteristics under different working conditions. After calculation, the values under each working condition are 75.0809, 0.5363, and 0.6471 respectively, which can effectively quantify different working conditions and accurately achieve high-resistance fault detection.

[0068] Please refer to Figure 6 , which shows the structural block diagram of a high-resistance fault detection system based on sorted energy and IEMD of this application.

[0069] As Figure 6As shown in the figure, the high-resistance fault detection system 200 includes an acquisition module 210, a decomposition module 220, a processing module 230, a judgment module 240, and an output module 250.

[0070] Among them, the acquisition module 210 is configured to acquire the zero-sequence current of each feeder, and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ; the decomposition module 220 is configured to perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, and calculate the kurtosis values of the plurality of characteristic components respectively , and select the characteristic component with the largest kurtosis value and record it as the fault characteristic component ; the processing module 230 is configured to perform normalization processing on the fault characteristic component , and obtain the square envelope curve of the fault characteristic component after normalization processing ; the judgment module 240 is configured to calculate the sorted energy of the square envelope curve , and judge whether the sorted energy is greater than a threshold ; the output module 250 is configured to, if it is greater than the threshold , then determine it as a high-resistance fault, otherwise it is a normal disturbance. ; the output module 250 is configured to, if it is greater than the threshold , then determine it as a high-resistance fault, otherwise it is a normal disturbance.

[0071] It should be understood that Figure 6 the modules described in Figure 1 correspond to the respective steps in the method described in the reference Figure 6 . Therefore, the operations, features, and corresponding technical effects described above for the method also apply to

[0072] In some other embodiments, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the high-resistance fault detection method based on sorted energy and IEMD in any of the above method embodiments;

[0073] As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as:

[0074] Acquire the zero-sequence current of each feeder, and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ;

[0075] Perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder Perform improved empirical mode adaptive decomposition to obtain several feature components, and calculate the kurtosis values of the several feature components respectively , and select the feature component with the largest kurtosis value and denote it as the fault feature component ;

[0076] For the said fault feature component , perform normalization processing, and obtain the squared envelope curve of the fault feature component after normalization processing ; ;

[0077] Calculate the sorted energy of the said squared envelope curve , and judge whether the said sorted energy is greater than the threshold ; ;

[0078] If it is greater than the threshold , then it is determined as a high-resistance fault, otherwise it is a normal disturbance

[0079] A computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of the high-resistance fault detection system based on sorted energy and IEMD, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely provided with respect to the processor, and these remote memories may be connected to the high-resistance fault detection system based on sorted energy and IEMD through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof

[0080] Figure 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 7 shown, the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected through a bus or other means Figure 7Take the bus connection as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, that is, implements the high-resistance fault detection method based on sorted energy and IEMD in the above method embodiments. The input device 330 can receive input digital or character information, and generate key signal inputs related to user settings and function controls of the high-resistance fault detection system based on sorted energy and IEMD. The output device 340 may include display devices such as a display screen.

[0081] The above electronic device can execute the method provided by the embodiments of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiments of the present invention.

[0082] As an implementation manner, the above electronic device is applied to a high-resistance fault detection system based on sorted energy and IEMD, and is used for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0083] Obtain the zero-sequence current of each feeder, and perform a difference operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ;

[0084] Perform improved empirical mode adaptive decomposition on the differential zero-sequence current of each feeder to obtain a plurality of characteristic components, and calculate the kurtosis values of the plurality of characteristic components respectively , and select the characteristic component with the largest kurtosis value as the fault characteristic component ;

[0085] Perform normalization processing on the fault characteristic component , and obtain the square envelope curve of the fault characteristic component after normalization processing ; ;

[0086] Calculate the sorted energy of the square envelope curve , and determine whether the sorted energy is greater than a threshold ; ;

[0087] If it is greater than the threshold , then it is determined as a high-resistance fault, otherwise it is a normal disturbance.

[0088] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0089] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-resistance fault detection method based on sorting energy and IEMD, characterized in that: include: Obtain the zero-sequence current of each feeder, and perform differential operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder ; The differential zero-sequence current of each feeder Perform improved empirical mode adaptive decomposition to obtain several characteristic components, and calculate the kurtosis values ​​of several characteristic components respectively , select the characteristic component with the largest kurtosis value and record it as the fault characteristic component , wherein the differential zero-sequence current of each feeder is Improved empirical mode adaptive decomposition is performed to obtain several characteristic components including: Set the maximum number of iterations to , and let the After the sieving iteration Signal The previous IMF minus Residual signal , the fitness objective function of the i-th signal 0, select the indicator is 0, the number of screenings is 0; Start the iteration, let , , ; Using the signal after the k-1th screening iteration Calculate the envelope mean signal after the kth screening iteration , get the signal after the kth screening iteration ; Calculate the objective function after the kth screening iteration ,if Greater than the objective function ,but =1, otherwise , ; Determine the number of zero points and extreme points Whether the difference is less than a preset quantity threshold; If so, then judge Is it greater than , otherwise iterate again; If so, then ; If not, then judge , otherwise iterate again; If so, then , otherwise iterate again; The fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve , where the fault characteristic component after normalization is obtained The square envelope curve The expression is: ， In the formula, For the Normalized fault feature components, is the square envelope curve The value of the sampling point, is the normalized fault characteristic component square The Hilbert transform of Calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? , calculate the square envelope curve The sorting energy The expression is: ， In the formula, The square envelope curve According to the numerical value in descending order, The square envelope curve After sorting the values ​​in descending order, is the number of the last sampling point, is the number value of the sampling point, Indicates that the square envelope curve After arranging the numbers in descending order, we get and , Indicates that the square envelope curve After arranging the numbers in descending order, we get , Indicates that the square envelope curve After sorting in descending order of value, minus The value corresponding to the difference after ; If it is greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

2. A high-resistance fault detection method based on sorting energy and IEMD according to claim 1, characterized in that: Calculate the differential zero-sequence current of each feeder The expression is: ， In the formula, is the zero sequence current, is the zero-sequence current before the fault.

3. The high-resistance fault detection method based on sorting energy and IEMD according to claim 1, characterized in that: Calculate the kurtosis value of several eigencomponents The expression is: ， In the formula, is the total number of sampling signal points, For the The mean signal, is the mean of the mean signal, The sampling point number.

4. The high-resistance fault detection method based on sorting energy and IEMD according to claim 1, characterized in that: The fault characteristic component The normalized expression is: ， ， In the formula, is the fault characteristic component Middle The value of the sampling point, , is the fault characteristic component The sampling point with the largest amplitude, For the Normalized fault feature components, is the fault characteristic component Middle The value of the sampling point.

5. A high-resistance fault detection system based on sorting energy and IEMD, characterized in that: include: An acquisition module is configured to acquire the zero-sequence current of each feeder and perform a differential operation on the zero-sequence current of each feeder to obtain a differential zero-sequence current of each feeder ; A decomposition module configured to decompose the differential zero-sequence current of each feeder Perform improved empirical mode adaptive decomposition to obtain several characteristic components, and calculate the kurtosis values ​​of several characteristic components respectively , select the characteristic component with the largest kurtosis value and record it as the fault characteristic component , wherein the differential zero-sequence current of each feeder is Improved empirical mode adaptive decomposition is performed to obtain several characteristic components including: Set the maximum number of iterations to , and let the After the sieving iteration Signal The previous IMF minus Residual signal , the fitness objective function of the i-th signal 0, select the indicator is 0, the number of screenings is 0; Start the iteration, let , , ; Using the signal after the k-1th screening iteration Calculate the envelope mean signal after the kth screening iteration , get the signal after the kth screening iteration ; Calculate the objective function after the kth screening iteration ,if Greater than the objective function ,but =1, otherwise , ; Determine the number of zero points and extreme points Whether the difference is less than a preset quantity threshold; If so, then judge Is it greater than , otherwise iterate again; If so, then ; If not, then judge , otherwise iterate again; If so, then , otherwise iterate again; A processing module configured to process the fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve , where the fault characteristic component after normalization is obtained The square envelope curve The expression is: ， In the formula, For the Normalized fault feature components, is the square envelope curve The value of the sampling point, is the normalized fault characteristic component square The Hilbert transform of A judgment module configured to calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? , calculate the square envelope curve The sorting energy The expression is: ， In the formula, The square envelope curve According to the numerical value in descending order, The square envelope curve After sorting the values ​​in descending order, is the number of the last sampling point, is the number value of the sampling point, Indicates that the square envelope curve After arranging the numbers in descending order, we get and , Indicates that the square envelope curve After arranging the numbers in descending order, we get , Indicates that the square envelope curve After sorting in descending order of value, minus The value corresponding to the difference after ; Output module, configured to be greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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