Wave head extraction method and system suitable for power distribution network fault traveling wave fault location

Abstract

The invention relates to the technical field of power grid automation, and provides a wave head extraction method and system suitable for power distribution network fault traveling wave distance measurement, and the method comprises the steps: collecting a voltage signal within a fixed time period after a power distribution network fault, and obtaining a fault voltage traveling wave signal according to the voltage signal; performing phase-mode transformation on the obtained fault voltage traveling wave signal to obtain a line-mode component; extracting a high-frequency signal in the line mode component by using wavelet decomposition; performing Hilbert transform on the high-frequency signal to obtain a Hilbert spectrum composed of instantaneous frequencies; transient characteristics of high-frequency abrupt change points in the Hilbert spectrum are enhanced by using a differential symmetric energy operator, and then a first point with the maximum frequency in the high-frequency abrupt change points is calculated by using a modulus maximum and is an initial wave head of a traveling wave reaching a distance measuring device. According to the method, the Hilbert spectrum is demodulated by using the differential symmetric energy operator, the transient characteristics of the abrupt change point are enhanced, and the fault traveling wave head is extracted according to the modulus maximum theory, so that the accuracy of wave head extraction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid automation, and in particular to a wave head extraction method and system suitable for traveling wave ranging of distribution network faults. Background Art

[0002] As the construction of new power systems progresses, large-scale distributed power sources and diverse new loads are actively connected to the distribution network. This is driving the distribution network towards active, proactive, and bidirectional power flow, placing even more stringent demands on power supply reliability. According to statistics from relevant departments, distribution network failures account for over 85% of all power system outages. Short-circuit grounding faults are the primary cause of distribution network failures, with single-phase grounding faults accounting for approximately 80% of all failures. The neutral point of medium and low voltage distribution systems often uses ineffective grounding. When a single-phase short-circuit grounding fault occurs in such systems, the short-circuit current generated is relatively small. Furthermore, distribution networks are typically radial networks with complex fault signals, further complicating fault location. Accurately and rapidly locating faults, enabling timely troubleshooting and improving system power supply reliability is crucial for improving distribution network reliability and a key step in the development of smart distribution networks in the new era. Summary of the Invention

[0003] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a wave head extraction method and system suitable for distribution network fault traveling wave ranging.

[0004] To achieve the above objectives, the present invention provides a wave head extraction method suitable for distribution network fault traveling wave location measurement, comprising:

[0005] Collect the voltage signal within a fixed time after the distribution network fault, subtract the three-phase voltage signal with the same phase before the fault, and obtain the fault voltage traveling wave signal;

[0006] Perform phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component;

[0007] The high frequency signal in the line mode component is extracted by wavelet decomposition;

[0008] Performing Hilbert transform on high frequency signals yields the Hilbert spectrum consisting of instantaneous frequencies;

[0009] The differential symmetric energy operator is used to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then the modulus maximum calculation is used to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

[0010] According to one aspect of the present invention, the fault voltage traveling wave signal is obtained by subtracting the normal three-phase voltage signal of the corresponding time length before the fault from the voltage signal within a preset time length after the fault.

[0011] According to one aspect of the present invention, Karenbauer transformation is performed on the acquired fault voltage traveling wave signal, and the formula is:

[0012]

[0013] Among them, u a 、u b 、u c They are the three-phase fault voltage traveling wave signals, u0 is the corresponding zero-mode component, and u1 and u2 are the corresponding line-mode components.

[0014] According to one aspect of the present invention, the method of extracting the high-frequency signal from the line mode component by using wavelet decomposition includes:

[0015] The line mode components are decomposed into different frequency bands by using the expansion and translation of the wavelet basis function. The wavelet basis function ψ is:

[0016]

[0017] Among them, x is the independent variable of the wavelet basis function; s is the scaling scale; u is the translation scale;

[0018] Since the line mode component is a discrete sampling point, a discrete wavelet transform is performed, and the formula is:

[0019]

[0020] Among them, F(m,n) is the wavelet coefficient of scale n at time m; s(k) is the extracted line mode component, is the scaling factor, where a0=2; t is the discretized time, ψ(*) is the wavelet basis function; nb0 is the translation factor, b0=1;

[0021] Using the Mallat algorithm, the signal is decomposed into approximate components and detail component signals in different frequency bands with the help of high-pass and low-pass filters:

[0022]

[0023] Among them, S j+1，k is the low-frequency signal reconstructed by the Mallat algorithm, W j+1，k It is the high-frequency signal reconstructed by the Mallat algorithm; S j,n and W j,n are the approximate component and detail component at different scales respectively; g n-2k and h n-2k are low-pass and high-pass filters respectively.

[0024] According to one aspect of the present invention, the Hilbert transform of the high-frequency signal is performed as follows:

[0025]

[0026] Where H[s(t)] represents the Hilbert transform of the line mode high frequency component s(t), and s(τ) represents the value of s(t) at time point τ;

[0027] Construct the analytical signal z(t), which is defined as the complex combination of the original signal s(t) and its Hilbert transform:

[0028]

[0029] Where A(t) is the instantaneous amplitude of the signal, is the instantaneous phase of the signal, and the reciprocal of the instantaneous phase is the instantaneous frequency.

[0030] According to one aspect of the present invention, a differential symmetric energy operator is used to enhance the transient characteristics of high-frequency mutation points in the Hilbert spectrum. The formula is:

[0031]

[0032] Among them, z(n) is the discrete signal after the Hilbert transform of the line mode high frequency signal, n represents the nth discrete signal point, is the signal processed by the differential symmetric energy operator.

[0033] To achieve the above objectives, the present invention further provides a wave head extraction system suitable for distribution network fault traveling wave location measurement, comprising:

[0034] The fault voltage traveling wave signal acquisition module collects the voltage signal within a fixed time after the distribution network fault, subtracts the three-phase voltage signal with the same phase before the fault, and obtains the fault voltage traveling wave signal;

[0035] The line mode component acquisition module performs phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component;

[0036] High-frequency signal extraction module, which uses wavelet decomposition to extract high-frequency signals from line mode components;

[0037] The Hilbert spectrum acquisition module performs Hilbert transform on the high-frequency signal to obtain the Hilbert spectrum composed of instantaneous frequencies;

[0038] The wave head calculation and extraction module uses the differential symmetric energy operator to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then uses the modulus maximum calculation to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

[0039] To achieve the above-mentioned objectives, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the wave head extraction method for traveling wave ranging suitable for distribution network faults as described above is implemented.

[0040] To achieve the above objectives, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the wave head extraction method suitable for distribution network fault traveling wave ranging as described above is implemented.

[0041] According to the above scheme of the present invention, the present invention collects the voltage signal after the distribution network fault, obtains the fault voltage traveling wave signal based on the voltage signal, then performs phase mode transformation on the fault voltage traveling wave signal to obtain the line mode component, and then extracts the high-frequency signal in the line mode component through wavelet decomposition, obtains the Hilbert spectrum by processing the high-frequency signal, obtains the Hilbert spectrum, and processes it based on multiple high-frequency multi-change points in the Hilbert spectrum. Considering that there may be multiple points with similar and difficult-to-distinguish frequencies in the high-frequency mutation point in the Hilbert spectrum, it is impossible to accurately select a suitable mutation point. The present invention uses a differential symmetric energy operator to demodulate the Hilbert spectrum, enhances the transient characteristics of the mutation point, and extracts the fault traveling wave head according to the modulus maximum theory, thereby improving the accuracy of the wave head extraction. Based on this, it is possible to accurately and quickly realize fault ranging, timely troubleshooting, and improve the reliability of system power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The flowchart schematically shows a wave head extraction method suitable for distribution network fault traveling wave location measurement according to one embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only for enabling those skilled in the art to better understand and implement the present invention, rather than implying any limitation on the scope of the present invention.

[0044] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0045] Figure 1 The flowchart of the wave head extraction method for traveling wave location measurement of distribution network faults according to one embodiment of the present invention is schematically shown. Figure 1 As shown, in this embodiment, the wave head extraction method applicable to distribution network fault traveling wave location measurement includes:

[0046] Collect the voltage signal within a fixed time after the distribution network fault, subtract the three-phase voltage signal with the same phase before the fault, and obtain the fault voltage traveling wave signal;

[0047] Perform phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component;

[0048] The high frequency signal in the line mode component is extracted by wavelet decomposition;

[0049] Performing Hilbert transform on the high-frequency signal yields a Hilbert spectrum consisting of instantaneous frequencies, where the high-frequency mutation point corresponds to the specific time when the fault traveling wave reaches the measurement point.

[0050] The differential symmetric energy operator is used to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then the modulus maximum calculation is used to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

[0051] Furthermore, according to one embodiment of the present invention, the fault voltage traveling wave signal is obtained by subtracting the normal three-phase voltage signal for a corresponding duration (e.g., 5ms) before the fault from the voltage signal within a preset duration (e.g., 5ms) after the fault, based on the superposition principle. The 5ms value simply determines the length of the fault signal; the specific duration can be determined based on actual conditions.

[0052] Furthermore, according to an embodiment of the present invention, a Karenbauer transform is performed on the acquired fault voltage traveling wave signal, and the formula is:

[0053]

[0054] Among them, u a 、u b 、u c They are three-phase fault voltage traveling wave signals, u0 is the corresponding zero-mode component, u1 and u2 are the corresponding line-mode components, u1 is also called the α-mode component, and u2 is also called the β-mode component.

[0055] Furthermore, according to an embodiment of the present invention, extracting high-frequency signals from line mode components using wavelet decomposition includes:

[0056] The line mode components are decomposed into different frequency bands by using the expansion and translation of the wavelet basis function. The wavelet basis function ψ is:

[0057]

[0058] Among them, x is the independent variable of the wavelet basis function; s is the scaling scale (non-zero real number); u is the translation scale;

[0059] Since the analyzed signal (line mode component) is a discrete sampling point, a discrete wavelet transform is performed, and the formula is:

[0060]

[0061] Among them, F(m,n) is the wavelet coefficient of scale n at time m; s(k) is the extracted line mode component, is the scaling factor, where a0=2; t is the discretized time, ψ(*) is the wavelet basis function; nb0 is the translation factor, b0=1;

[0062] Using the Mallat algorithm, the signal is decomposed into approximate components and detail component signals in different frequency bands with the help of a series of high-pass and low-pass filters:

[0063]

[0064] Among them, S j+1，k is the low-frequency signal reconstructed by the Mallat algorithm, that is, the low-frequency signal reconstructed by all low-frequency components after low-pass filtering; W j+1，k is the high-frequency signal reconstructed by the Mallat algorithm, that is, the high-frequency signal reconstructed by all high-frequency components after high-pass filtering; S j,n and W j,n are the approximate component and detail component at different scales respectively; g n-2k and h n-2k The approximate component and detail component are multiple low-frequency and high-frequency components decomposed by the Mallat algorithm. After being filtered by low-pass and high-pass filters, these components are reconstructed to form the high-frequency and low-frequency signals in the line mode component.

[0065] In this embodiment, the db4 wavelet is selected as the basis function, the line mode component is decomposed into 5 layers, and the first layer component is reconstructed to obtain the high-frequency component containing the fault information.

[0066] Furthermore, according to one embodiment of the present invention, a Hilbert transform is performed on the high-frequency signal, and the formula is:

[0067]

[0068] Where H[s(t)] represents the Hilbert transform of the line mode high frequency component s(t), and s(τ) represents the value of s(t) at time point τ;

[0069] Construct the analytical signal z(t), which is defined as the complex combination of the original signal s(t) and its Hilbert transform:

[0070]

[0071] Where A(t) is the instantaneous amplitude of the signal, is the instantaneous phase of the signal, and the inverse of the instantaneous phase is the instantaneous frequency, and the corresponding Hilbert spectrum can be obtained.

[0072] Furthermore, according to an embodiment of the present invention, a differential symmetric energy operator is used to enhance the transient characteristics of high-frequency mutation points in the Hilbert spectrum. The formula is:

[0073]

[0074] Among them, z(n) is the discrete signal after the Hilbert transform of the line mode high frequency signal, n represents the nth discrete signal point, is the signal processed by the differential symmetric energy operator.

[0075] Furthermore, according to one embodiment of the present invention, the first point with the largest frequency among the high-frequency mutation points calculated using the modulus maximum is the initial wave head of the traveling wave reaching the ranging device. Subsequently, the distance to the occurrence of the fault can be calculated based on the relevant distance formula and wave speed.

[0076] According to the above scheme of the present invention, the present invention collects the voltage signal after the distribution network fault, obtains the fault voltage traveling wave signal based on the voltage signal, then performs phase mode transformation on the fault voltage traveling wave signal to obtain the line mode component, and then extracts the high-frequency signal in the line mode component through wavelet decomposition, obtains the Hilbert spectrum by processing the high-frequency signal, obtains the Hilbert spectrum, and processes it based on multiple high-frequency multi-change points in the Hilbert spectrum. Considering that there may be multiple points with similar and difficult-to-distinguish frequencies in the high-frequency mutation point in the Hilbert spectrum, it is impossible to accurately select a suitable mutation point. The present invention uses a differential symmetric energy operator to demodulate the Hilbert spectrum, enhances the transient characteristics of the mutation point, and extracts the fault traveling wave head according to the modulus maximum theory, thereby improving the accuracy of the wave head extraction. Based on this, it is possible to accurately and quickly realize fault ranging, timely troubleshooting, and improve the reliability of system power supply.

[0077] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a wave head extraction system suitable for distribution network fault traveling wave ranging, comprising:

[0078] The fault voltage traveling wave signal acquisition module collects the voltage signal within a fixed time after the distribution network fault, subtracts the three-phase voltage signal with the same phase before the fault, and obtains the fault voltage traveling wave signal;

[0079] The line mode component acquisition module performs phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component;

[0080] High-frequency signal extraction module, which uses wavelet decomposition to extract high-frequency signals from line mode components;

[0081] The Hilbert spectrum acquisition module performs Hilbert transform on the high-frequency signal to obtain the Hilbert spectrum composed of instantaneous frequencies;

[0082] The wave head calculation and extraction module uses the differential symmetric energy operator to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then uses the modulus maximum calculation to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

[0083] Furthermore, according to one embodiment of the present invention, the fault voltage traveling wave signal is obtained by subtracting the normal three-phase voltage signal for a corresponding duration (e.g., 5ms) before the fault from the voltage signal within a preset duration (e.g., 5ms) after the fault, based on the superposition principle. The 5ms value simply determines the length of the fault signal; the specific duration can be determined based on actual conditions.

[0084] Furthermore, according to an embodiment of the present invention, a Karenbauer transform is performed on the acquired fault voltage traveling wave signal, and the formula is:

[0085]

[0086] Among them, u a 、u b 、u c They are three-phase fault voltage traveling wave signals, u0 is the corresponding zero-mode component, u1 and u2 are the corresponding line-mode components, u1 is also called the α-mode component, and u2 is also called the β-mode component.

[0087] Furthermore, according to an embodiment of the present invention, extracting high-frequency signals from line mode components using wavelet decomposition includes:

[0088] The line mode components are decomposed into different frequency bands by using the expansion and translation of the wavelet basis function. The wavelet basis function ψ is:

[0089]

[0090] Among them, x is the independent variable of the wavelet basis function; s is the scaling scale (non-zero real number); u is the translation scale;

[0091] Since the analyzed signal (line mode component) is a discrete sampling point, a discrete wavelet transform is performed, and the formula is:

[0092]

[0093] Among them, F(m,n) is the wavelet coefficient of scale n at time m; s(k) is the extracted line mode component, is the scaling factor, where a0=2; t is the discretized time, ψ(*) is the wavelet basis function; nb0 is the translation factor, b0=1;

[0094] Using the Mallat algorithm, the signal is decomposed into approximate components and detail component signals in different frequency bands with the help of a series of high-pass and low-pass filters:

[0095]

[0096] Among them, S j+1，k is the low-frequency signal reconstructed by the Mallat algorithm, that is, the low-frequency signal reconstructed by all low-frequency components after low-pass filtering; W j+1，k is the high-frequency signal reconstructed by the Mallat algorithm, that is, the high-frequency signal reconstructed by all high-frequency components after high-pass filtering; S j,n and W j,n are the approximate component and detail component at different scales respectively; g n-2k and h n-2k The approximate component and detail component are multiple low-frequency and high-frequency components decomposed by the Mallat algorithm. After being filtered by low-pass and high-pass filters, these components are reconstructed to form the high-frequency and low-frequency signals in the line mode component.

[0097] In this embodiment, the db4 wavelet is selected as the basis function, the line mode component is decomposed into 5 layers, and the first layer component is reconstructed to obtain the high-frequency component containing the fault information.

[0098] Furthermore, according to one embodiment of the present invention, a Hilbert transform is performed on the high-frequency signal, and the formula is:

[0099]

[0100] Where H[s(t)] represents the Hilbert transform of the line mode high frequency component s(t), and s(τ) represents the value of s(t) at time point τ;

[0101] Construct the analytical signal z(t), which is defined as the complex combination of the original signal s(t) and its Hilbert transform:

[0102]

[0103] Where A(t) is the instantaneous amplitude of the signal, is the instantaneous phase of the signal, and the inverse of the instantaneous phase is the instantaneous frequency, and the corresponding Hilbert spectrum can be obtained.

[0104] Furthermore, according to an embodiment of the present invention, a differential symmetric energy operator is used to enhance the transient characteristics of high-frequency mutation points in the Hilbert spectrum. The formula is:

[0105]

[0106] Among them, z(n) is the discrete signal after the Hilbert transform of the line mode high frequency signal, n represents the nth discrete signal point, is the signal processed by the differential symmetric energy operator.

[0107] Furthermore, according to one embodiment of the present invention, the first point with the largest frequency among the high-frequency mutation points calculated using the modulus maximum is the initial wave head of the traveling wave reaching the ranging device. Subsequently, the distance to the occurrence of the fault can be calculated based on the relevant distance formula and wave speed.

[0108] According to the above scheme of the present invention, the present invention collects the voltage signal after the distribution network fault, obtains the fault voltage traveling wave signal based on the voltage signal, then performs phase mode transformation on the fault voltage traveling wave signal to obtain the line mode component, and then extracts the high-frequency signal in the line mode component through wavelet decomposition, obtains the Hilbert spectrum by processing the high-frequency signal, obtains the Hilbert spectrum, and processes it based on multiple high-frequency multi-change points in the Hilbert spectrum. Considering that there may be multiple points with similar and difficult-to-distinguish frequencies in the high-frequency mutation point in the Hilbert spectrum, it is impossible to accurately select a suitable mutation point. The present invention uses a differential symmetric energy operator to demodulate the Hilbert spectrum, enhances the transient characteristics of the mutation point, and extracts the fault traveling wave head according to the modulus maximum theory, thereby improving the accuracy of the wave head extraction. Based on this, it is possible to accurately and quickly realize fault ranging, timely troubleshooting, and improve the reliability of system power supply.

[0109] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the wave head extraction method for traveling wave ranging suitable for distribution network fault is implemented as described above.

[0110] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the wave head extraction method suitable for distribution network fault traveling wave ranging as described above is implemented.

[0111] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0113] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0114] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

[0115] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0116] If the functions are implemented as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion 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 instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal transmission / reception method according to various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0117] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0118] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A wave head extraction method suitable for traveling wave fault location measurement in distribution network, characterized in that: include: Collect the voltage signal within a fixed time after the distribution network fault, subtract the three-phase voltage signal with the same phase before the fault, and obtain the fault voltage traveling wave signal; Perform phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component; The high frequency signal in the line mode component is extracted by wavelet decomposition; Performing Hilbert transform on high frequency signals yields the Hilbert spectrum consisting of instantaneous frequencies; The differential symmetric energy operator is used to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then the modulus maximum calculation is used to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

2. The wave head extraction method for distribution network fault traveling wave ranging according to claim 1 is characterized in that: The fault voltage traveling wave signal is obtained by subtracting the normal three-phase voltage signal of the corresponding time before the fault from the voltage signal within a preset time after the fault.

3. The wave head extraction method for distribution network fault traveling wave ranging according to claim 1 is characterized in that: Perform Karenbauer transformation on the acquired fault voltage traveling wave signal, and the formula is: Among them, u a 、u b 、u c They are the three-phase fault voltage traveling wave signals, u0 is the corresponding zero-mode component, and u1 and u2 are the corresponding line-mode components.

4. The wave head extraction method for distribution network fault traveling wave ranging according to claim 1 is characterized in that: The method of extracting the high-frequency signal from the line mode component by using wavelet decomposition includes: The line mode components are decomposed into different frequency bands by using the expansion and translation of the wavelet basis function. The wavelet basis function ψ is: Among them, x is the independent variable of the wavelet basis function; s is the scaling scale; u is the translation scale; Since the line mode component is a discrete sampling point, a discrete wavelet transform is performed, and the formula is: Among them, F(m,n) is the wavelet coefficient of scale n at time m; s(k) is the extracted line mode component, is the scaling factor, where a0=2; t is the discretized time, ψ(*) is the wavelet basis function; nb0 is the translation factor, b0=1; Using the Mallat algorithm, the signal after discrete wavelet transform is decomposed into approximate components and detail component signals in different frequency bands with the help of high-pass and low-pass filters: Among them, S j+1,k is the low-frequency signal reconstructed by the Mallat algorithm, W j+1,k It is the high-frequency signal reconstructed by the Mallat algorithm; S j,n and W j,n are the approximate component and detail component at different scales respectively; g n-2k and h n-2k are low-pass and high-pass filters respectively.

5. The wave head extraction method for distribution network fault traveling wave ranging according to claim 1 is characterized in that: The Hilbert transform of the high-frequency signal is performed, that is, the convolution of the high-frequency signal and the impulse response h(t)=1 / πt, and the formula is: Where H[s(t)] represents the Hilbert transform of the line mode high frequency component s(t), and s(τ) represents the value of s(t) at time point τ; Construct the analytical signal z(t), which is defined as the complex combination of the original signal s(t) and its Hilbert transform: Where A(t) is the instantaneous amplitude of the signal, is the instantaneous phase of the signal, and the reciprocal of the instantaneous phase is the instantaneous frequency.

6. The wave head extraction method for traveling wave fault location measurement in distribution network according to any one of claims 1 to 5, characterized in that: The transient characteristics of high-frequency mutation points in the Hilbert spectrum are enhanced using the differential symmetric energy operator. The formula is: Among them, z(n) is the discrete signal after the Hilbert transform of the line mode high frequency signal, n represents the nth discrete signal point, is the signal processed by the differential symmetric energy operator.

7. A wave head extraction system suitable for fault traveling wave ranging in distribution network, characterized by: include: The fault voltage traveling wave signal acquisition module collects the voltage signal within a fixed time after the distribution network fault, subtracts the three-phase voltage signal with the same phase before the fault, and obtains the fault voltage traveling wave signal; The line mode component acquisition module performs phase mode transformation on the acquired fault voltage traveling wave signal to obtain the line mode component; High-frequency signal extraction module, which uses wavelet decomposition to extract high-frequency signals from line mode components; The Hilbert spectrum acquisition module performs Hilbert transform on the high-frequency signal to obtain the Hilbert spectrum composed of instantaneous frequencies; The wave head calculation and extraction module uses the differential symmetric energy operator to enhance the transient characteristics of the high-frequency mutation points in the Hilbert spectrum, and then uses the modulus maximum calculation to obtain the first point with the largest frequency among the high-frequency mutation points as the initial wave head of the traveling wave reaching the ranging device.

8. An electronic device, characterized in that The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for extracting a wave head suitable for traveling wave ranging of a distribution network fault as claimed in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the wave head extraction method applicable to distribution network fault traveling wave location measurement is implemented as described in any one of claims 1 to 7.

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