High-resistance fault detection method and system based on sorting energy and IEMD

Abstract

The invention discloses a high-resistance fault detection method and system based on sorting energy and IEMD, and the method comprises the steps: obtaining the zero-sequence current of each feeder line, carrying out the differential operation of the zero-sequence current of each feeder line, and obtaining the differential zero-sequence current of each feeder line; performing improved empirical mode adaptive decomposition on each feeder differential zero-sequence current to obtain a plurality of feature components, calculating kurtosis values of the plurality of feature components respectively, and selecting the feature component with the maximum kurtosis value as a fault feature component; performing normalization processing on the fault feature component, and solving a square envelope curve of the fault feature component after normalization processing; and calculating the sorting energy of the square envelope curve, and judging whether the sorting energy is greater than a threshold value or not. The high-resistance fault and normal disturbance can be effectively distinguished, and the reliability and accuracy of high-resistance fault detection are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of relay protection of power system distribution network, and in particular relates to a high-resistance fault detection method and system based on sequencing 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 user and ensuring the reliable supply of electricity. In this process, the distribution network may face various fault threats, among which high-impedance fault (HIF) is particularly special. A high-impedance fault is a fault caused by high-resistance contact between the conductor and the ground or obstacles. Its characteristics are that the fault current is small, difficult to detect by traditional protection devices, and easily confused with normal disturbances such as capacitor switching and load switching. Although high-impedance faults will 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 injury. Therefore, rapid and accurate detection of high-impedance faults is of great significance to ensure the safe operation of the power system. Summary of the invention

[0003] The present invention provides a high-resistance fault detection method and system based on sorting energy and IEMD, which are used to solve the technical problems in the prior art that the fault characteristics of high-resistance faults are weak, easily confused with normal disturbances, and cause traditional protection devices to fail easily.

[0004] In a first aspect, the present invention provides a high-resistance fault detection method based on sorting energy and IEMD, comprising: 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 ; The fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; Calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; If it is greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0005] In a second aspect, the present invention provides a high-resistance fault detection system based on sorting energy and IEMD, comprising: 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 ; A processing module configured to process the fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; A judgment module configured to calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; Output module, configured to be greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0006] According to a third aspect, an electronic device is provided, comprising: 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 perform the steps of the high-resistance fault detection method based on sorting energy and IEMD of any embodiment of the present invention.

[0007] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the high-resistance fault detection method based on sorting energy and IEMD of any embodiment of the present invention.

[0008] The high-resistance fault detection method and system based on sorting energy and IEMD of the present application have the following beneficial effects: In terms of fault signal decomposition: First, a soft SSC is proposed to monitor the screening process in EMD, which can improve the efficiency of fault diagnosis and reduce the computational cost without sacrificing the decomposition accuracy. At the same time, it can select the optimal number of iterations and suppress the mode mixing problem of the EMD algorithm. Secondly, the mirror extension algorithm is used in IEMD to effectively suppress the endpoint effect. Finally, the characteristic components after IEMD decomposition are screened by the kurtosis index, which can effectively select the characteristic components containing the richest fault characteristic information, further laying the foundation for high-resistance fault detection. In terms of high-resistance fault detection criteria: the zero-sequence current waveform of arc high-resistance fault has zero-break and intermittent characteristics, while other load switching and capacitor switching only have transient attenuation characteristics. Based on this, the fault characteristic component with the largest kurtosis value obtained by IEMD decomposition of the zero-sequence current of the fault is Normalize it and find its square envelope curve; finally, calculate the sorting energy of the square envelope curve , and set the threshold , by comparing the sorting energy With threshold The size of the high-resistance fault can be used to detect high-resistance faults, which can effectively distinguish high-resistance faults from normal disturbances, greatly improving the reliability and accuracy of high-resistance fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a high-resistance fault detection method based on sorting energy and IEMD provided by one embodiment of the present invention; Figure 2 A schematic diagram of a 10kV resonant grounded distribution network simulation model provided by an embodiment of the present invention; Figure 3 A waveform diagram of the test result of an arc high-resistance fault of a specific embodiment is provided for an embodiment of the present invention; Figure 4 A schematic diagram of a waveform of a capacitor switching test result of a specific embodiment provided by an embodiment of the present invention; Figure 5 A schematic diagram of a waveform of a load switching test result of a specific embodiment provided by an embodiment of the present invention; Figure 6 A structural block diagram of a high-resistance fault detection system based on sorting energy and IEMD provided by one embodiment of the present invention; Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] See also Figure 1 , which shows a flow chart of a high-resistance fault detection method based on sorting energy and IEMD of the present application.

[0013] like Figure 1 As shown, the high-resistance fault detection method based on sorting energy and IEMD specifically includes the following steps: Step S101, obtaining the zero-sequence current of each feeder, and performing a differential operation on the zero-sequence current of each feeder to obtain the differential zero-sequence current of each feeder .

[0014] In this step, the differential zero-sequence current of each feeder is calculated The expression is: , In the formula, is the zero sequence current, is the zero-sequence current before the fault.

[0015] Step S102: 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 .

[0016] In this step, Empirical Mode Decomposition (EMD) is developed to decompose a discrete-time signal into multiple single-component signals, ideally one single component for each intrinsic mode function (IMF). In EMD, the number of screening 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-screening" and "over-screening". In the case of under-screening, multiple single-component signals are decomposed into a single IMF, while in the case of over-screening, 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 insufficient screening, making it impossible to extract the fault frequency band. If the number of iterations is too large, two problems will arise: One problem is the over-screening phenomenon, that is, some irrelevant signals are forcibly decomposed into multiple IMFs, which may lead to misjudgment of fault diagnosis. Another problem is that the calculation time gradually increases with the increase in the number of iterations. Vibration signals usually contain a large amount of data, so it is crucial to improve the efficiency of fault diagnosis and reduce the computational cost without sacrificing the decomposition accuracy. The proposed soft SSC can monitor the screening process in EMD, and more importantly, it can select the optimal number of iterations. Therefore, soft SSC can help EMD suppress its mode mixing problem.

[0017] It should be noted that the maximum number of iterations is set 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 ,if Greater than the objective function ,but =1, otherwise , ; Determine the number of zero points and extreme points Is the difference less than the 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.

[0018] Furthermore, the kurtosis values ​​of several characteristic components are calculated 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.

[0019] Step S103: Perform normalization and obtain the fault characteristic components after normalization The square envelope curve .

[0020] In this step, 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.

[0021] Obtain the normalized fault characteristic components 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 .

[0022] Step S104, calculating the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? .

[0023] In this step, the square envelope curve is calculated 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, The number of the sampling point.

[0024] Step S105: If the , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0025] In summary, the method of the present application first collects the zero-sequence current of each feeder and performs differential calculation on it; secondly, the obtained differential zero-sequence current is adaptively decomposed by IEMD, and the characteristic component with the largest kurtosis value is selected as the fault characteristic component; thirdly, the obtained fault characteristic component is normalized and its square envelope curve is calculated; finally, the sorting energy of the square envelope curve is calculated, and a threshold is set. High-resistance fault detection can be achieved by comparing the sorting energy with the threshold.

[0026] In a specific embodiment, PSCAD is used to build Figure 2 The simulation model of the 10kV resonant grounded distribution network shown in the figure has 5 outgoing lines, among which: It is a fully overhead line. For full cable line, It is a mixed overhead and cable line. The length of each line has been clearly marked in the figure. The specific parameters of the line are shown in Table 1. In addition, to It represents the zero-sequence current transformer installed at the head end of each feeder. Assuming that the system operates with overcompensation, the overcompensation degree is 10%, the arc suppression coil inductance is 0.4779 H, the resistance is 4.5040 Ω, and a 0.5 MW constant impedance load is used at the end of each feeder.

[0027] , Feeder Single-phase high-resistance fault, capacitor switching and load switching occur at the locations respectively. The initial phase angle of the fault is set to 30°, the system sampling frequency is f=10kHz, the simulation time is 0.5s, and the fault occurrence time is set to 0.2s.

[0028] Typical load switching, capacitor switching and arc 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 .Depend on 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. The values ​​are 75.0809, 0.5363, and 0.6471, respectively, which effectively quantify different working conditions and can accurately realize high-resistance fault detection.

[0029] See also Figure 6 , which shows a structural block diagram of a high-resistance fault detection system based on sorting energy and IEMD of the present application.

[0030] like Figure 6 As shown, the high-resistance fault detection system 200 includes an acquisition module 210 , a decomposition module 220 , a processing module 230 , a determination module 240 and an output module 250 .

[0031] The acquisition module 210 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 the differential zero-sequence current of each feeder. ; Decomposition module 220, 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 ; Processing module 230, configured to the fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; Judgment module 240, configured to calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? Output module 250, configured to be greater than a threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0032] It should be understood that Figure 6 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 6 The modules in it will not be described in detail here.

[0033] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the high-resistance fault detection method based on sorting energy and IEMD in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: 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 ; The fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; Calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; If it is greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0034] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the high-resistance fault detection system based on sorting 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 disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the high-resistance fault detection system based on sorting energy and IEMD via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] Figure 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 7 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also 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 via a bus or other means. Figure 7 In the example, the bus connection is used. 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, the high-resistance fault detection method based on sorting energy and IEMD of the above-mentioned method embodiment is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the high-resistance fault detection system based on sorting energy and IEMD. The output device 340 may include display devices such as display screens.

[0036] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0037] As an implementation mode, the electronic device is applied to a high-resistance fault detection system based on sorting energy and IEMD, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; 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: 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 ; The fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; Calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; If it is greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

[0038] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for 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 embodiment.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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 ; The fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; Calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; 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: The differential zero-sequence current of each feeder 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 Is the difference less than the 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.

4. 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.

5. 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.

6. A high-resistance fault detection method based on sorting energy and IEMD according to claim 1, characterized in that: Obtain the normalized fault characteristic components 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 .

7. The high-resistance fault detection method based on sorting energy and IEMD according to claim 1, characterized in that: 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, The number of the sampling point.

8. 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 ; A processing module configured to process the fault characteristic component Perform normalization and obtain the fault characteristic components after normalization The square envelope curve ; A judgment module configured to calculate the square envelope curve The sorting energy , and determine the sorting energy Is it greater than the threshold? ; Output module, configured to be greater than the threshold , it is determined to be a high-resistance fault, otherwise it is a normal disturbance.

9. 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 execute the method described in any one of claims 1 to 7.

10. 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 7 is implemented.

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