Distribution line short-circuit fault traveling wave positioning method and distribution line short-circuit fault traveling wave positioning system

Abstract

The invention provides a distribution line short-circuit fault traveling wave positioning method and system, and relates to the technical field of power system fault positioning. The method comprises the following steps: S1, collecting a traveling wave signal of a distribution line; s2, key characteristic parameters are extracted from the traveling wave signals; s3, positioning a fault position based on the key feature parameters; and S4, performing fault alarm based on a positioning result of the fault position. According to the method and the system, short-circuit faults caused by birds and other life bodies can be rapidly and accurately detected and positioned, and the reliability and the economical efficiency of operation of the power distribution network are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system fault location, and in particular to a traveling wave location method and system for short-circuit faults in distribution lines. Background Art

[0002] The distribution network is a key component of the power system, covering a wide range and diverse environments. With the advancement of urbanization and the acceleration of rural power grid transformation, distribution lines have gradually extended to urban-rural junctions and natural ecological areas. However, the special environment of these areas has made the problem of line short-circuit faults increasingly prominent, especially short-circuit faults caused by living organisms such as birds and small animals. Bird-related faults are usually caused by birds contacting or building nests, and manifest as damage to the insulation layer, short circuits between wires, or grounding faults. Such faults not only affect the stable operation of the power grid, but may also cause power outages and equipment damage, and even cause fires in severe cases, bringing huge maintenance costs and social reputation risks to power grid companies.

[0003] Bird-related faults are characterized by strong randomness, high concealment, and strong short-term bursts. Compared with traditional short-circuit faults, bird-related faults are usually accompanied by low-amplitude, short-term traveling wave signals, which makes it difficult for traditional detection methods to accurately capture the faults. In addition, bird-related faults have obvious temporal and spatial distribution characteristics. For example, birds are active during the breeding and migration periods, and early morning and dusk are high-incidence periods. These behavioral patterns directly affect the frequency and distribution of faults. However, traditional power inspections mostly rely on manual methods or general fault monitoring devices, lacking in-depth research and early warning capabilities on the behavioral characteristics of living organisms such as birds, resulting in low efficiency in locating and handling such faults.

[0004] In recent years, the structure of distribution networks has become increasingly complex, with multi-branch and multi-node topological characteristics. The traveling wave signals occurring in the lines often undergo multiple reflections, refractions, and attenuation. Especially in the case of short-circuit faults in living organisms, the uncertainty of the signal propagation path further exacerbates the difficulty of fault location. At the same time, the diversity of the line operating environment (such as the mixed use of overhead lines and cable lines) and external factors (such as equipment electrostatic discharge, lightning strikes, and electromagnetic radiation interference) will also have a significant impact on the signal propagation characteristics. The existing positioning system is mainly designed for simple fault scenarios of a single line or device, and it is difficult to adapt to the needs of bird-related fault detection and positioning in complex power grid environments.

[0005] With the rapid development of artificial intelligence and signal processing technology, fault location methods based on the extraction and analysis of traveling wave signal features have gradually become a research hotspot. These technologies can quickly locate the fault point by capturing and analyzing the voltage or current traveling wave signals generated by line faults. However, current technologies mostly focus on the characteristics of high-intensity, high-current signals, and it is difficult to fully combine the periodicity of bird behavior and effectively process small-amplitude signals such as bird faults. Therefore, it is crucial to quickly and accurately detect and locate short-circuit faults caused by living organisms such as birds to improve the reliability and economy of distribution network operation. Summary of the invention

[0006] The purpose of the embodiments of the present application is to provide a traveling wave positioning method and system for distribution line short-circuit faults, so as to quickly and accurately detect and locate short-circuit faults caused by living things such as birds, and improve the reliability and economy of distribution network operation.

[0007] In a first aspect, the present invention provides a traveling wave location method for a short-circuit fault in a distribution line, the method comprising:

[0008] S1: collects the traveling wave signal of the distribution line;

[0009] S2: Extract key characteristic parameters from traveling wave signals;

[0010] S3: Locate the fault location based on key characteristic parameters;

[0011] S4: Based on the positioning result of the fault position, a fault alarm is issued.

[0012] In an optional embodiment, S1 includes:

[0013] The sensitivity to traveling wave signals is improved through embedded enhancement circuits to ensure that short-term signals caused by bird-borne faults in traveling wave signals are captured.

[0014] In an optional embodiment, S1 includes:

[0015] The improved wavelet threshold function adaptive denoising method is used to reduce the influence of noise on the fault location accuracy in traveling wave signals.

[0016] In an optional embodiment, S2 includes:

[0017] The key characteristic parameters are extracted from the traveling wave signal by using the joint analysis technology of wavelet transform and Hilbert-Huang transform.

[0018] In an optional embodiment, S2 includes:

[0019] Wavelet transform is used to decompose the spectral characteristics of the traveling wave signal, and Hilbert-Huang transform is combined to extract the instantaneous frequency and amplitude of the traveling wave signal;

[0020] By analyzing the multi-scale components of the traveling wave signal, the reflection and refraction points in the traveling wave propagation are identified to optimize the positioning accuracy.

[0021] In an optional embodiment, S3 includes:

[0022] Synchronous sampling of multiple measurement points to record the arrival time and signal characteristics of traveling waves;

[0023] By fusing and analyzing the data from each measurement point, the signal propagation path is determined and the location of the fault point is calculated.

[0024] In an optional embodiment, S3 includes:

[0025] The location of the fault point is obtained by comparing and weighting the results of the TDR and FDR methods, and the algorithm parameters are corrected using the life behavior characteristic model to improve the accuracy of the calculation results.

[0026] In an optional implementation, in S4, the alarm information includes the precise location of the fault point, the fault type, key characteristic parameters of the traveling wave signal, and cause analysis.

[0027] In an optional embodiment, the method may further include the steps of data storage and updating.

[0028] In a second aspect, the present invention provides a distribution line short-circuit fault traveling wave positioning system, the system comprising:

[0029] A signal acquisition module, used to collect traveling wave signals of distribution lines;

[0030] A feature extraction module is used to extract key feature parameters from the traveling wave signal;

[0031] A fault location module is used to locate the fault location based on key characteristic parameters;

[0032] The fault alarm module is used to issue a fault alarm based on the positioning result of the fault position.

[0033] The traveling wave positioning method and system for distribution line short-circuit faults provided by this embodiment can effectively solve or improve the problems in the background technology. Specifically, this embodiment proposes a high-sensitivity signal acquisition and enhancement technology for short-circuit faults of living organisms, a multimodal fusion algorithm combining time-frequency domain characteristics, and an intelligent model optimization method based on the behavioral characteristics of living organisms. This solution significantly improves the detection capability of weak signals and the positioning accuracy in complex network environments, supports real-time and accurate fault location, and at the same time achieves an improvement in early warning capabilities through intelligent modeling of bird-related behavior patterns, providing new technical support for the safe and stable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A flow chart of a traveling wave location method for a short-circuit fault in a distribution line provided in an embodiment of the present application;

[0036] Figure 2 A block diagram of the composition of a distribution line short-circuit fault traveling wave location system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0038] Figure 1 The traveling wave positioning method for distribution line short-circuit faults provided in the embodiment of the present application (hereinafter referred to as the method) combines the behavior laws of living organisms and the complex structural characteristics of the distribution network, and optimizes the traveling wave signal processing method and the fault positioning algorithm to achieve accurate detection and rapid positioning of bird-related faults. Specifically, the method includes the following steps:

[0039] S1: Collect the traveling wave signal of the distribution line.

[0040] Specifically, the signal acquisition module is used to collect the traveling wave signals of the distribution line, and multi-point synchronous sampling is performed at both ends of the line and at key nodes. The signal acquisition module is equipped with high-speed sampling equipment to support the collection of high-frequency and weak signals. The signal acquisition module improves the sensitivity of low-amplitude traveling wave signals through embedded enhancement circuits to ensure the capture of short-term signals caused by bird-related faults in traveling wave signals. At the same time, the signal acquisition module has anti-interference capabilities and can effectively filter the impact of environmental noise on the signal.

[0041] Among them, in the collected data signals, due to the interference of the external environment, the traveling wave signals are often mixed with noise. It is necessary to adopt the method of adaptive denoising of improved wavelet threshold function to reduce the influence of noise on the fault location accuracy in the traveling wave signals.

[0042] Among them, the sampling frequency of the signal acquisition module is not less than 1MHz and can reach tens of MHz. It can identify the weak signals caused by bird-related faults, and optimize the signals through anti-noise filters and adaptive enhancement circuits to effectively suppress the interference of environmental noise on the signals and ensure the integrity of signal characteristics.

[0043] S2: Extract key characteristic parameters from traveling wave signals.

[0044] Specifically, in an ideal transmission line, the mathematical model of the traveling wave signal can be described by the transmission line equation. For a lossless transmission line, the propagation of voltage V and current I satisfies the following partial differential equation:

[0045]

[0046] Among them, L and C are inductance and capacitance per unit length, respectively, x is the distance along the transmission line, and t is the time; by transforming the above equations, the wave equations of voltage and current can be obtained:

[0047]

[0048] These show that the propagation of voltage and current in a transmission line can be viewed as a function of velocity Propagating waves.

[0049] Solving the above wave equation, we can obtain:

[0050]

[0051] in, is the characteristic impedance of the transmission line, and Represent the voltage traveling waves of forward transmission and reverse transmission respectively;

[0052] When a fault occurs, according to the reflection coefficient Refractive index β = 1 + α. Assuming the fault occurs at point x0, the forward propagating wave will be reflected and transmitted:

[0053]

[0054] Where V reflection is the traveling wave reflection velocity, V refraction is the projection speed of the traveling wave. The propagation and reflection behavior of the traveling wave determines that in the transmission line, the information of the fault point can be extracted by measuring the arrival time and characteristics of the traveling wave, thereby realizing the fault location. By installing traveling wave measurement devices at different locations of the distribution network and recording the arrival time and amplitude changes of the traveling wave, the location of the fault point can be calculated using the above-mentioned traveling wave equation and reflection and transmission characteristics.

[0055] Extracting key characteristic parameters from traveling wave signals is achieved through the feature extraction module. The feature extraction module uses time-frequency joint analysis techniques such as wavelet transform and Hilbert-Huang transform (HHT) to extract key characteristic parameters from traveling wave signals, where the key characteristic parameters include amplitude, frequency, phase and propagation time (including arrival time), to achieve feature optimization identification of fault signals.

[0056] Among them, wavelet transform is used to decompose the spectral characteristics of the traveling wave signal, and Hilbert-Huang transform is combined to extract the instantaneous frequency and amplitude of the traveling wave signal. By analyzing the multi-scale components of the traveling wave signal (including spectral characteristics, instantaneous frequency and amplitude), the reflection points and refraction points in the traveling wave propagation can be accurately identified to optimize the positioning accuracy.

[0057] The feature extraction module supports multi-resolution analysis, can capture the detailed changes of traveling wave signals, and identify the typical signal patterns of bird-related faults through adaptive algorithms.

[0058] S3: Locate the fault location based on key characteristic parameters.

[0059] Specifically, the fault location is located through the fault location module. The fault location module integrates multi-mode traveling wave location algorithms, including single-end and dual-end time domain location (TDR), and frequency domain reflection location (FDR). By fusing the key characteristic parameters in the multi-point synchronous sampling data, the fault location module can accurately calculate the propagation path of the traveling wave, and correct the propagation error in the complex environment by combining the key characteristic parameters, and calculate the fault point location by combining the time domain and frequency domain location methods. The time-frequency joint location method here actually mainly utilizes the spectral characteristics, such as analyzing the fault traveling wave characteristics on the time-frequency plane through wavelet transform, short-time Fourier transform and other technologies. This joint location method combines the information of the time domain and frequency domain to provide more comprehensive traveling wave characteristics, which helps to improve the positioning accuracy and has the robustness suitable for complex environments.

[0060] Among them, the main advantage of the TDR method is that the principle is relatively simple, and thus easy to implement. By injecting a pulse signal into the line and measuring the reflected wave generated by the impedance discontinuity point (such as the fault point) encountered when it propagates along the line, the distance to the fault point can be calculated by measuring the time difference of the reflected wave. The simplicity of the measurement principle also makes the measurement waveform of the TDR method intuitive, easy to understand and analyze, and the fault point can be observed directly through the waveform. In addition, due to the direct measurement of the time difference, this method has high real-time performance and is suitable for rapid fault location.

[0061] Specifically, the measurement principle of the single-ended TDR method is that when a fault occurs, a traveling wave is generated at the fault point, which propagates to both ends. At the measurement end, the location of the fault point can be determined by capturing the initial traveling wave and the traveling wave reflected from the fault point and analyzing the time difference between the two. After the initial traveling wave reaches the fault point, part of the energy will be reflected back, and this reflected wave lags behind the initial traveling wave in time. By measuring the time difference between the two waves reaching the measurement end, the distance to the fault point can be calculated.

[0062] The calculation formula for the distance D from the fault point to the measurement point obtained using single-ended TDR is:

[0063] Δt=t2-t1

[0064] Where v is the transmission speed of the traveling wave in the transmission line, t2 and t1 are the time it takes for the reflected wave to reach the measuring end and the time it takes for the initial traveling wave to reach the measuring end, respectively. The location of the fault point can be determined by using the time difference between the two traveling waves captured by the measuring end.

[0065] The two-terminal TDR method captures the time difference between the traveling wave generated when a fault occurs and the arrival of the two ends by installing measurement equipment at both ends of the transmission line. When a fault occurs in the line, the fault point causes rapid changes in voltage and current, and the generated traveling wave propagates along the line to both ends. After installing high-precision measurement equipment at both ends of the transmission line (A and B ends), the time for the traveling wave to arrive at both ends is captured under the same time reference, and the fault can be located. The calculation formula based on the two-terminal TDR is:

[0066]

[0067] Where l1 and l2 are the distances from the fault point to the two ends, L0 is the C length of the transmission line, and t1 and t2 are the times when the traveling wave reaches the two ends of the line.

[0068] The multi-terminal TDR method installs measurement equipment at multiple locations of the line (including both ends and intermediate nodes) to capture the arrival time and characteristic signals of the traveling wave, and determines the fault location through joint analysis of multi-point data. This method can handle complex line structures and multi-branch and multi-terminal situations, and improve positioning accuracy and reliability. This method does not have an analytical expression, but records the arrival time and characteristic signals of the traveling wave through measurement equipment at multiple locations, performs data fusion and joint analysis on multi-point data, and calculates the location of the fault point.

[0069] The advantage of the FDR method is that it uses rich spectrum information to achieve high measurement accuracy. Specifically, the FDR method determines the fault location by measuring the reflection characteristics of the line at different frequencies. It identifies faults by analyzing the spectrum characteristics of the reflected signal. It has high measurement accuracy and a wide range of applications. It can be used for both overhead lines and underground cables. In addition, frequency domain analysis has a certain robustness to noise and interference, especially in complex electromagnetic environments, and has strong anti-interference capabilities.

[0070] For multi-branch and multi-node line structures, the fault location module supports synchronous sampling of multiple measurement points, records the arrival time and signal characteristics of the traveling waves, determines the signal propagation path, and calculates the location of the fault point by fusing and analyzing the data of each measurement point.

[0071] Among them, by comparing and weighting the results of the TDR and FDR methods, the real-time performance and accuracy are comprehensively improved, and the algorithm parameters are corrected using the behavioral characteristic model of the organism, such as adjusting the fault signal recognition threshold according to the high-incidence period of bird-related activities to improve the accuracy of the calculation results. Specifically, machine learning and deep learning algorithms are introduced to model the behavioral laws of bird-related activities (such as seasonal migration and breeding activities), and a database of biological characteristics is constructed in combination with historical data to achieve trend prediction and dynamic optimization of potential bird-related activities.

[0072] Specifically, in the actual fault analysis of the distribution network, this embodiment performs wavelet transform on the collected traveling wave signal and decomposes the signal into different scales to capture the characteristics of the traveling wave signal at different frequencies and times. The basic formula is:

[0073]

[0074] Among them, W(a,b) is the wavelet coefficient, x(t) is the traveling wave signal, is the wavelet basis function, a is the scale factor, which is used to control the width of the wavelet and scale the signal; b is the translation factor, which is used to control the position of the wavelet and move the signal.

[0075] The multi-resolution analysis mentioned above is an important feature of wavelet transform. The fault traveling waves in the distribution network have components of multiple frequencies. Therefore, wavelet transform can be used to extract wavelet coefficients at various scales. The mutation points of the wavelet coefficients correspond to the mutation points of the traveling wave signal. By analyzing the mutation points, the time when the fault occurred can be determined. Combined with the propagation speed of the traveling wave itself and the time difference when arriving at each point, the location of the fault point can be calculated.

[0076] The Hilbert-Huang transform method consists of two steps: empirical mode decomposition (EMD) and Hilbert spectrum analysis.

[0077] EMD decomposes the initial fault signal to obtain a set of simple components called intrinsic mode functions (IMFs). Each IMF represents a different oscillation mode of the signal, and then analyzes each oscillation mode. The specific steps of EMD are as follows:

[0078] The first step is to find all local extreme points;

[0079] The second step is to use spline interpolation to connect the local maximum and minimum values ​​to obtain the upper and lower envelopes of the signal, and calculate the average values ​​of the upper and lower envelopes;

[0080] The third step is to obtain a preliminary intrinsic mode function. That is, subtract the envelope average value from the original signal to obtain a new signal. However, to determine whether the new signal is an IMF, it is necessary to verify that the number of extreme points and zero crossing points in the entire signal range must be equal or differ by at most one, and the average value of the envelope should be zero on any time scale; if the new signal is not an IMF, repeat the above process until the new signal becomes an IMF;

[0081] The fourth step is to obtain the residual signal and iterate continuously. Subtract the IMF obtained in the third step from the original signal to obtain the residual signal, and repeat the above process for the residual signal until the residual signal is a monotonic function or contains only one extreme value, and the empirical mode decomposition is completed.

[0082] After performing EMD, a series of IMFs are obtained. The subsequent Hilbert spectrum analysis is to perform Hilbert transform on each IMF to obtain its instantaneous frequency and instantaneous amplitude, and generate the Hilbert spectrum of the signal. The formula is as follows:

[0083]

[0084] In the formula, H(x(t)) represents the Hilbert spectrum corresponding to the eigenmode function x(t), where x(t) corresponds to a certain eigenmode function, PV is the Cauchy principal value integral, and A is i (t) represents the amplitude at time t, represents the phase at time t, ω i (t) represents the frequency at time t.

[0085] By calculating the instantaneous amplitude, instantaneous phase, and instantaneous frequency of the signal, the Hilbert spectrum of the original signal can be obtained; the obvious mutation points in the Hilbert spectrum correspond to the time when the fault occurs, and after obtaining them, the fault location can be comprehensively performed.

[0086] S4: Based on the positioning result of the fault position, a fault alarm is issued.

[0087] Specifically, the fault alarm is implemented through the fault alarm module. The fault alarm module is linked with the signal acquisition module to continuously monitor the operating status of the distribution line. When an abnormal traveling wave signal (such as short-term voltage or current fluctuation) is detected and identified as a potential bird-related fault by the feature extraction module, the fault alarm will immediately trigger an alarm.

[0088] Among them, the alarm information includes the precise location of the fault point, the fault type (such as bird-related short circuit), the key characteristic parameters of the traveling wave signal (such as arrival time, amplitude, frequency, etc.) and possible cause analysis, helping maintenance personnel to quickly troubleshoot and handle faults.

[0089] In addition, the method may also include the steps of data storage and updating. Specifically, data storage and updating are implemented through a communication and storage module, which realizes real-time data transmission and historical record storage. The communication and storage module supports cloud synchronization of fault location results, which facilitates multi-site data fusion analysis. At the same time, the stored historical data can be used for dynamic update and model training of the life behavior feature library.

[0090] Among them, a feature library is established through historical data and fault records to achieve long-term monitoring and pattern mining of bird-related behaviors. The algorithm can dynamically adjust the model parameters to adapt to the fault characteristics of living organisms at different times and regions.

[0091] Based on the same invention concept, please refer to Figure 2 In an embodiment of the present application, a distribution line short-circuit fault traveling wave positioning system (hereinafter referred to as: system) is also provided. The system includes a signal acquisition module, a feature extraction module, a fault positioning module and a fault alarm module.

[0092] Specifically, the signal acquisition module, the feature extraction module, the fault location module, and the fault alarm module are connected in sequence.

[0093] The signal acquisition module, feature extraction module, fault location module and fault alarm module are respectively used to execute S1 to S4 in the above method, and the communication and storage module is used to execute the data storage and update steps in the above method.

[0094] In addition, the system may also include a communication and storage module, which is connected to the signal acquisition module, feature extraction module, fault location module, and fault alarm module, and is used to execute the data storage and update steps in the above method.

[0095] The beneficial effects of the traveling wave location method and system for distribution line short-circuit fault provided in this embodiment include:

[0096] (1) High-precision fault location: The multimodal traveling wave signal processing technology proposed in this embodiment combines time domain analysis, frequency domain analysis and time-frequency domain joint analysis to effectively improve the accuracy of fault location. By extracting the key characteristic parameters of weak traveling wave signals through wavelet transform and Hilbert-Huang transform, low-amplitude and short-time signals can be accurately captured in complex environments. The system positioning error is significantly reduced, which can meet the high-precision positioning requirements of complex distribution networks (such as multi-branch and multi-node topology structures).

[0097] (2) Improved real-time performance and response efficiency: Through the high-sensitivity signal acquisition module and optimized time domain analysis algorithm, a rapid response of nanoseconds is ensured after a fault occurs. The multi-level alarm mechanism of the fault alarm module supports local and remote real-time alarms, significantly shortening the time from fault detection to processing. Compared with traditional methods, the positioning and alarm response speed is increased by more than 50%, effectively reducing the power outage time of the power grid.

[0098] (3) Wide adaptability: The system is not only suitable for bird-related faults, but can also be extended to other types of short-circuit faults in living organisms (such as short circuits caused by squirrels and snakes), as well as other fault scenarios with weak signals. The high adaptability of multimodal traveling wave processing technology and intelligent algorithms gives the system a wide range of application scenarios. At the same time, multi-point synchronous sampling and signal fusion algorithms enhance the robustness of the system in noisy environments and complex network conditions, and can reliably cope with challenges such as signal reflection, attenuation and interference.

[0099] (4) Intelligent early warning and behavior analysis: The system's built-in life behavior feature library and trend analysis function can provide early warning of potential bird-related fault risks, combine historical data for trend modeling, and identify high-risk behaviors or time periods that may cause short circuits. Through early warning, the incidence of life short-circuit faults is reduced, providing power companies with the possibility of proactive maintenance.

[0100] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units 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 communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0101] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0103] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0104] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0105] The above are only embodiments of the present application and are not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A traveling wave location method for short-circuit faults in distribution lines, characterized in that: The method comprises: S1: collects the traveling wave signal of the distribution line; S2: extracting key characteristic parameters from the traveling wave signal; S3: locating the fault location based on the key characteristic parameters; S4: Based on the positioning result of the fault position, a fault alarm is issued.

2. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: S1 includes: The sensitivity to traveling wave signals is improved through embedded enhancement circuits to ensure that short-term signals caused by bird-borne faults in traveling wave signals are captured.

3. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: S1 includes: The improved wavelet threshold function adaptive denoising method is used to reduce the influence of noise on the fault location accuracy in traveling wave signals.

4. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: S2 includes: The key characteristic parameters are extracted from traveling wave signals by using the joint analysis technique of wavelet transform and Hilbert-Huang transform.

5. The traveling wave location method for short-circuit fault in distribution line according to claim 4, characterized in that S2 include: Wavelet transform is used to decompose the spectral characteristics of the traveling wave signal, and Hilbert-Huang transform is combined to extract the instantaneous frequency and amplitude of the traveling wave signal; By analyzing the multi-scale components of the traveling wave signal, the reflection and refraction points in the traveling wave propagation are identified to optimize the positioning accuracy.

6. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that S3 include: Synchronous sampling of multiple measurement points to record the arrival time and signal characteristics of traveling waves; By fusing and analyzing the data from each measurement point, the signal propagation path is determined and the location of the fault point is calculated.

7. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: S3 includes: The location of the fault point is obtained by comparing and weighting the results of the TDR and FDR methods, and the algorithm parameters are corrected using the life behavior characteristic model to improve the accuracy of the calculation results.

8. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: In S4, the alarm information includes the precise location of the fault point, the fault type, the key characteristic parameters of the traveling wave signal, and the cause analysis.

9. The traveling wave location method for short-circuit fault in distribution line according to claim 1, characterized in that: The method also includes the steps of data storage and updating.

10. A distribution line short-circuit fault traveling wave positioning system, characterized in that: The system comprises: A signal acquisition module, used to collect traveling wave signals of distribution lines; A feature extraction module, used to extract key feature parameters from the traveling wave signal; A fault location module, used to locate the fault location based on the key characteristic parameters; The fault alarm module is used to issue a fault alarm based on the positioning result of the fault position.

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