A distribution network fault location method and system based on multi-terminal traveling wave ranging

Abstract

The present invention discloses a distribution network fault location method and system based on multi-terminal traveling wave ranging, relating to the technical field of distribution network fault location. The method includes collecting traveling wave signals at N key nodes in the distribution network; N in the N key nodes is a positive integer greater than 2; analyzing and processing the traveling wave signals using a noise suppression algorithm and wavelet transform to obtain effective traveling wave features; extracting and fusing the effective traveling wave features to obtain traveling wave feature signals; calibrating and correcting the traveling wave feature signals using a dynamic topology correction model, and calculating the calibrated and corrected traveling wave feature signals using a multi-terminal traveling wave time difference algorithm to obtain the fault location. The present invention ultimately achieves accurate and rapid positioning of the distribution network fault location through the steps of collecting traveling wave signals, processing signals, extracting and fusing features, and correcting topology, thereby improving the accuracy of fault location.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and in particular to a distribution network fault location method and system based on multi-terminal traveling wave ranging. Background Art

[0002] Distribution network fault location refers to the process of rapidly identifying the type of power system fault and determining its location through technical means. Its core goal is to provide operations and maintenance personnel with accurate fault location information, thereby shortening outage recovery time and improving power supply reliability.

[0003] Currently, traditional methods for locating faults in distribution networks rely primarily on overcurrent protection, impedance methods, or single-ended traveling wave ranging. Overcurrent protection protects equipment by shutting down the circuit when the current exceeds a predetermined value. Impedance methods determine the fault location based on the impedance value measured at the time of the fault. Single-ended traveling wave ranging uses information such as the propagation time of the traveling wave generated by the fault to determine the fault point.

[0004] Currently, single-ended traveling wave ranging is the most widely used method. Due to the numerous branching lines and complex structure in distribution networks, single-ended traveling wave ranging generates reflected waves when traveling waves encounter branching lines or impedance mismatches during propagation. These reflected waves are superimposed on the original traveling waves, complicating the measured traveling wave information and making it difficult to accurately determine the fault location. This reduces positioning accuracy and affects fault location accuracy.

[0005] In the process of implementing the present invention, the inventors discovered that the prior art has at least the following problems:

[0006] When locating a distribution network fault, the existing positioning accuracy is low and the accuracy of fault positioning is low. Summary of the Invention

[0007] The purpose of the present invention is to provide a distribution network fault location method and system based on multi-terminal traveling wave ranging to solve the technical problems existing in the prior art of low positioning accuracy and low fault location accuracy when locating distribution network faults.

[0008] The various technical effects that can be produced by the preferred technical solutions among the various technical solutions provided by the present invention are described in detail below.

[0009] To achieve the above objectives, the present invention provides the following technical solutions:

[0010] The present invention provides a distribution network fault location method based on multi-terminal traveling wave ranging, comprising:

[0011] Collecting traveling wave signals at N key nodes in a distribution network, wherein the traveling wave signals are collected by traveling wave collection devices disposed at the N key nodes, and each of the traveling wave collection devices is time-synchronized using a traveling wave time difference positioning model; wherein N in the N key nodes is a positive integer greater than 2;

[0012] The traveling wave signal is analyzed and processed by adopting a noise suppression algorithm and a wavelet transform to obtain effective traveling wave characteristics;

[0013] Extracting and fusing the effective traveling wave features to obtain traveling wave feature signals;

[0014] The traveling wave characteristic signal is calibrated and corrected using a dynamic topology correction model, and the calibrated and corrected traveling wave characteristic signal is calculated using a multi-terminal traveling wave time difference algorithm to obtain the fault location.

[0015] Optionally, the adopting of a noise suppression algorithm and a wavelet transform to analyze and process the traveling wave signal to obtain effective traveling wave features includes:

[0016] Preprocessing the traveling wave signal to obtain a preprocessed signal;

[0017] Decomposing the preprocessed signal into different intrinsic mode functions using CEEMDAN decomposition;

[0018] The IMF components in the intrinsic mode function are decomposed using wavelet transform to obtain effective traveling wave characteristics.

[0019] Optionally, extracting and fusing the effective traveling wave features to obtain a traveling wave feature signal includes:

[0020] Extracting time domain features, frequency domain features, and time-frequency domain features from the effective traveling wave features to obtain extracted features;

[0021] The extracted features are subjected to principal component analysis and normalization processing to obtain traveling wave characteristic signals.

[0022] Optionally, the method further includes:

[0023] Constructing N of the key nodes into a distributed network topology, wherein the N key nodes include one key node with a master clock and N-1 key nodes with slave clocks;

[0024] The key node with the master clock exchanges messages with N-1 key nodes with the slave clocks;

[0025] According to the message interaction situation, clock synchronization configuration and network delay calculation deviation calculation are performed on the N key nodes to generate a traveling wave time difference positioning model.

[0026] Optionally, the key node with the master clock is selected by an optimal master clock algorithm.

[0027] Optionally, the use of a dynamic topology correction model to calibrate and correct the traveling wave characteristic signal, and the use of a multi-terminal traveling wave time difference algorithm to calculate the calibrated and corrected traveling wave characteristic signal to obtain the fault location includes:

[0028] Construct a dynamic topology correction model;

[0029] Using the dynamic topology correction model, calibrate the traveling wave head moment in the traveling wave characteristic signal, and correct the traveling wave velocity in the traveling wave characteristic signal to obtain a calibrated traveling wave head and a corrected traveling wave velocity;

[0030] The calibrated traveling wave head moment and the corrected traveling wave velocity are calculated using a multi-terminal traveling wave time difference algorithm to obtain the fault location.

[0031] Optionally, the constructing of a dynamic topology correction model includes:

[0032] According to a wide-area breadth-first search algorithm, a threshold value of a fault line is set, and a network topology within a valid calculation area is dynamically determined based on the threshold value;

[0033] Combined with the real-time operation status of the power grid, the line impedance parameters and length parameters are updated to form a dynamic weighted connectivity graph;

[0034] A dynamic topology correction model is constructed according to the network topology in the effective computing area and the dynamic weighted connectivity graph.

[0035] Optionally, the multi-terminal traveling wave time difference algorithm is used to calculate the calibrated traveling wave head moment and the corrected traveling wave velocity, and the calculation formula is:

[0036]

[0037] Where L is the distance from the fault point to the nearest critical node, v is the corrected traveling wave velocity, Δ T AB , Δ T AC , Δ T BC It is the time difference between the arrival times of the calibration traveling wave heads between adjacent key nodes.

[0038] On the other hand, the present invention also provides a distribution network fault location system based on multi-terminal traveling wave ranging, which is used to execute the above-mentioned distribution network fault location method based on multi-terminal traveling wave ranging, and the system includes a traveling wave acquisition device and a main control unit;

[0039] The traveling wave acquisition device is arranged at N key nodes in the distribution network, and is used to collect traveling wave signals at the N key nodes;

[0040] The main control unit is used to analyze and process the traveling wave signal to obtain effective traveling wave characteristics, extract and fuse the effective traveling wave characteristics to obtain a traveling wave characteristic signal; and use a dynamic topology correction model to calibrate and correct the traveling wave characteristic signal to calculate the fault location.

[0041] Optionally, the traveling wave acquisition device includes a traveling wave sensor, a high-speed AD sampling unit and a high-precision clock synchronization module.

[0042] Implementing one of the above technical solutions of the present invention has the following advantages or beneficial effects:

[0043] The method described in the present invention can collect traveling wave signals at N key nodes in a distribution network, wherein the traveling wave signals are collected by traveling wave collection devices arranged at the N key nodes, and each traveling wave collection device is time synchronized through a traveling wave time difference positioning model; N in the N key nodes is a positive integer greater than 2; the traveling wave signals are analyzed and processed using a noise suppression algorithm and a wavelet transform to obtain effective traveling wave features; the effective traveling wave features are feature extracted and fused to obtain traveling wave feature signals; and the traveling wave feature signals are calibrated and corrected using a dynamic topology correction model to obtain a fault location.

[0044] The distribution network fault location method based on multi-terminal traveling wave ranging described in the present invention achieves accurate and rapid location of the distribution network fault through the steps of collecting traveling wave signals, processing signals, extracting and fusing features, and correcting topology, which can improve the accuracy of fault location. In addition, the dynamic topology correction model adopted in this embodiment can also adapt to changes in the distribution network structure, further improving the accuracy and practicality of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work. In the drawings:

[0046] Figure 1 This is a flow chart of a distribution network fault location method based on multi-terminal traveling wave ranging according to embodiment 1 of the present invention;

[0047] Figure 2 This is a flowchart of step S20 in the distribution network fault location method based on multi-terminal traveling wave ranging in embodiment 1 of the present invention;

[0048] Figure 3 This is a flowchart of step S30 in the distribution network fault location method based on multi-terminal traveling wave ranging in embodiment 1 of the present invention;

[0049] Figure 4 This is a flowchart of step S40 in the distribution network fault location method based on multi-terminal traveling wave ranging in embodiment 1 of the present invention;

[0050] Figure 5 This is a framework diagram of the installation of three traveling wave acquisition devices at three key nodes of a 10 km distribution network in Example 1 of the present invention;

[0051] Figure 6 It is a schematic diagram of the overall structure of a distribution network fault location system based on multi-terminal traveling wave ranging according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objects, technical solutions and advantages of the present invention clearer, the various exemplary embodiments to be described below will refer to the corresponding drawings, which constitute a part of the exemplary embodiments, in which various exemplary embodiments that may be used to implement the present invention are described. Unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present disclosure. It should be understood that they are only examples of processes, methods and devices that are consistent with some aspects of the present disclosure as detailed in the appended claims, and other embodiments may also be used, or structural and functional modifications may be made to the embodiments listed herein without departing from the scope and essence of the present invention.

[0053] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", etc. indicate the orientation or position relationship based on the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the elements referred to must have a specific orientation, be constructed and operate in a specific orientation. The terms "first", "second", etc. are only used for descriptive purposes and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. The term "plurality" means two or more. The terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, an integral connection, a mechanical connection, an electrical connection, a communication connection, a direct connection, an indirect connection through an intermediate medium, and can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0054] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below, in which only the parts related to the embodiment of the present invention are shown.

[0055] Example 1:

[0056] like Figure 1 As shown, the present invention provides a distribution network fault location method based on multi-terminal traveling wave ranging, comprising:

[0057] S10. Collect traveling wave signals at N key nodes in the distribution network, wherein the traveling wave signals are collected by traveling wave collection devices disposed at the N key nodes, and each traveling wave collection device is time-synchronized using a traveling wave time difference positioning model; wherein N in the N key nodes is a positive integer greater than 2;

[0058] S20, analyzing and processing the traveling wave signal using a noise suppression algorithm and wavelet transform to obtain effective traveling wave characteristics;

[0059] S30, extracting and fusing effective traveling wave features to obtain traveling wave feature signals;

[0060] S40. Use the dynamic topology correction model to calibrate and correct the traveling wave characteristic signal, and use the multi-terminal traveling wave time difference algorithm to calculate the calibrated and corrected traveling wave characteristic signal to obtain the fault location.

[0061] In this embodiment, when a power system fault occurs, sudden changes in voltage and current occur, which propagate along the power lines in the form of waves. Therefore, traveling wave acquisition devices are installed at N key nodes in the distribution network to collect traveling wave signals. These traveling wave acquisition devices, located at these N key nodes, are synchronized using a traveling wave time difference positioning model. This allows accurate calculation of the time difference in traveling wave signal propagation, providing a basis for subsequent fault location and avoiding positioning errors caused by inaccurate signal acquisition.

[0062] After collecting the traveling wave signal, a noise suppression algorithm is used to remove noise introduced during propagation. The traveling wave signal is further analyzed using a wavelet transform to obtain valid traveling wave data, effectively improving signal quality and providing a reliable data foundation for subsequent feature extraction and fusion. The valid traveling wave features are extracted and fused to obtain a traveling wave signature signal, reducing the complexity of subsequent fault location. The traveling wave signature signal is calibrated and corrected using a dynamic topology correction model, and the calibrated and corrected traveling wave signature signal is calculated using a multi-terminal traveling wave time difference algorithm to determine the fault location. The dynamic topology correction model automatically adjusts the calibration parameters of the traveling wave signature signal based on actual distribution network topology changes, ensuring accurate fault location. The multi-terminal traveling wave time difference algorithm fully utilizes the traveling wave signals collected by traveling wave acquisition devices located at multiple key nodes. By accurately calculating the time difference between these devices, the fault location can be precisely located.

[0063] In summary, the distribution network fault location method based on multi-terminal traveling wave ranging described in this embodiment accurately and quickly locates the distribution network fault position through the steps of collecting traveling wave signals, processing signals, extracting and fusing features, and correcting topology, which can improve the accuracy of fault location. In addition, this embodiment adopts a dynamic topology correction model, which can adapt to changes in the distribution network structure and further improve the accuracy and practicality of fault location.

[0064] Next, we will combine Figure 1 The distribution network fault location method based on multi-terminal traveling wave ranging described in this embodiment is described in detail.

[0065] First, execute step S10 to collect traveling wave signals at N key nodes in the distribution network, wherein the traveling wave signals are collected by traveling wave collection devices arranged at the N key nodes, and each traveling wave collection device is time synchronized through a traveling wave time difference positioning model; N in the N key nodes is a positive integer greater than 2.

[0066] Specifically, N key nodes are selected in the distribution network. These nodes are significant locations within the network, such as line branch points and critical load access points. Traveling wave acquisition devices are installed at these key nodes to collect traveling wave signals from these key nodes. Traveling wave signals contain rich fault information, and subsequent analysis of these signals enables fault location. Furthermore, collecting traveling wave data at these N key nodes eliminates the reflected wave interference associated with single-ended traveling wave ranging.

[0067] In order to accurately collect traveling wave signals, it is necessary to synchronize the time between the various traveling wave collection devices. In this embodiment, a traveling wave time difference positioning model is used for synchronization.

[0068] Specifically, the method for generating a traveling wave time difference positioning model includes the following steps: constructing N key nodes into a distributed network topology, wherein the N key nodes include a key node with a master clock and N-1 key nodes with slave clocks; performing message interaction between the key node with the master clock and the N-1 key nodes with the slave clocks; and performing clock synchronization configuration and network delay calculation deviation calculation on the N key nodes according to the message interaction situation to generate a traveling wave time difference positioning model.

[0069] In the generated traveling wave time difference positioning model, N key nodes can be connected via industrial Ethernet, optical fiber, or 5G. These N key nodes can work together to form a distributed network topology. In this network topology, each key node has a certain degree of autonomy and can independently handle some tasks while also communicating and collaborating with other key nodes.

[0070] Furthermore, the N key nodes include one key node with a master clock and N-1 key nodes with slave clocks. The key node with the master clock serves as a reference during clock synchronization. Key nodes with slave clocks need to synchronize with the key node with the master clock to ensure time consistency across all key nodes in the network. In this embodiment, the key node with the master clock is elected using the best master clock algorithm and can distribute synchronization configuration information to key nodes with slave clocks.

[0071] More specifically, key nodes with master clocks exchange messages with N-1 key nodes with slave clocks to configure clock synchronization, calculate network delays, and calculate deviations. The following details the message exchange process between key nodes with master clocks and N-1 key nodes with slave clocks. For ease of explanation, key nodes with master clocks are referred to as master clock nodes, and key nodes with slave clocks are referred to as slave clock nodes.

[0072] The message exchange process for clock synchronization configuration involves the following: the master clock node periodically sends Sync messages carrying the timestamp t1, and the slave clock nodes receive these Sync messages and record the reception time t2. During this message exchange process, the slave clock nodes adjust their own clocks based on the time information sent by the master clock node, ensuring that the clocks of all nodes remain as consistent as possible. Specifically, the clock synchronization protocol used in this embodiment can include NTP (Network Time Protocol) and PTP (Precision Time Protocol).

[0073] The message exchange process for network delay and offset calculation involves the following: After completing the clock synchronization configuration message exchange, the slave clock node requests delay measurement via a Delay_resp message and records the sending time t3. The master clock node replies with a Delay_resp message containing the time t4. The slave clock node calculates the network delay Delay = (t4 - t3) - (t2 - t1) and adjusts its local clock to compensate for the master clock offset and the slave clock offset Offset. Specifically, since messages take time to travel across the network, network delays may occur during transmission. Therefore, by analyzing the sending and receiving times of messages between the master and slave clock nodes, the message transmission delay can be calculated. Furthermore, even after clock synchronization, clocks on different nodes may still have some offset. Offset calculation compares the times of the master and slave clock nodes to calculate the time difference between them, which can be used to further adjust the slave clock time.

[0074] By continuously performing the above-mentioned message interactions, the changes in network delay can be dynamically acquired, and a traveling wave time difference positioning model can be generated, providing a basis for subsequent fault location and processing. Specifically, traveling wave time difference positioning is a method for determining the fault point or target location based on the time difference of traveling waves propagating in the transmission medium. In the power system, when a fault occurs, traveling waves are generated. By measuring the time difference of the traveling waves arriving at different monitoring points, the location of the fault point can be calculated. Based on the previously completed clock synchronization configuration, network delay calculation and deviation calculation, combined with the principle of traveling wave propagation, a traveling wave time difference positioning model is established. It is used to accurately calculate the location of the fault point or target based on the time difference of the traveling waves arriving at different key nodes. The traveling wave time difference positioning model can provide a basis for subsequent fault location and processing.

[0075] More specifically, the specific steps for collecting traveling wave signals at N key nodes in the distribution network are as follows: the master clock node sends a synchronization trigger instruction, and each slave clock node starts the data collection process based on the calibrated master clock. When the traveling wave signal reaches each node, the precise collection time is recorded through the hardware timestamp. The collected traveling wave signal will be converged to the monitoring center via Ethernet, and the timestamps of the collection time of the traveling wave signal will be aligned. After alignment, subsequent traveling wave analysis will be carried out.

[0076] In this embodiment, since traveling waves are electrical signals with a propagation speed in conductors close to the speed of light (299,792,458 m / s), the location of the fault point can be calculated by the time difference between the traveling wave signal and multiple key nodes on the line. Since the time accuracy requirement for fault point calculation is very high, a time difference of 1 μs can result in a positioning deviation of up to 200 m. Therefore, a traveling wave time difference positioning model is used to synchronize the traveling wave acquisition devices at N key nodes. This ensures that more effective and accurate traveling wave signals are collected, providing a reliable data foundation for subsequent feature extraction and fusion.

[0077] It should be noted that the traveling wave time difference positioning model in this embodiment utilizes the Global Positioning System (GPS) and China's independently developed BeiDou satellite navigation system to obtain precise time signals. By receiving standard time information transmitted by satellites, the clocks of connected devices can be synchronized with the standard time, thereby ensuring the accuracy and consistency of device time. Therefore, the time accuracy of the traveling wave time difference positioning model in this embodiment can reach the nanosecond level.

[0078] Then, step S20 is executed to analyze and process the traveling wave signal using a noise suppression algorithm and wavelet transform to obtain effective traveling wave features. After the traveling wave signal is collected, it is necessary to analyze and process the traveling wave signal.

[0079] The specific steps of step S20 are as follows: Figure 2 As shown, it includes: S21, preprocessing the traveling wave signal to obtain a preprocessed signal; S22, using CEEMDAN decomposition to decompose the preprocessed signal into different intrinsic mode functions; S23, using wavelet transform to decompose the IMF component in the intrinsic mode function to obtain an effective traveling wave feature.

[0080] When executing step S20, the traveling wave signal needs to be preprocessed first to obtain a preprocessed signal. Since the traveling wave signal may be affected by noise interference and baseline drift during the acquisition process, the traveling wave signal needs to be preprocessed.

[0081] In this embodiment, the preprocessing process includes cleaning, polynomial fitting detrending, or differential detrending. Cleaning the traveling wave signal can improve signal quality. Detrending by polynomial fitting primarily involves performing a polynomial fitting on the traveling wave signal to obtain a fitted curve, which is then subtracted from the original signal to remove the trend term.

[0082] Polynomial fitting detrending is suitable for cases where the trend term is relatively smooth. Difference detrending removes the trend term by performing operations such as forward and backward differencing or central differencing on the traveling wave signal. Difference detrending is suitable for cases where the trend term changes rapidly or there are sudden changes in the signal.

[0083] In this embodiment, polynomial fitting or differential detrending can be selected based on actual conditions. Both detrending methods can effectively remove noise interference and baseline drift in the traveling wave signal, improve signal quality, and provide a more accurate data basis for subsequent analysis and processing.

[0084] Then, CEEMDAN decomposition is used to decompose the preprocessed signal into different intrinsic mode functions. Specifically, CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) is a signal decomposition method used to decompose the preprocessed signal into different intrinsic mode functions. This method can decompose complex nonlinear and nonstationary signals into several intrinsic mode functions (IMFs), each of which represents a component in a certain frequency band or a specific mode in the signal. CEEMDAN decomposition can decompose the preprocessed traveling wave signal into multiple IMF components with different frequency and amplitude characteristics. Each IMF component is relatively independent and can reflect the characteristics of the original signal in different frequency bands. This allows for independent analysis and processing of each IMF component, leading to more accurate extraction of fault information.

[0085] Finally, the IMF components in the intrinsic mode function are decomposed and processed using wavelet transform to obtain effective traveling wave features. Specifically, the steps of decomposition using wavelet transform include wavelet decomposition, multi-scale decomposition of the IMF components, decomposition of the IMF components into sub-signals of different frequencies, and then threshold denoising of the sub-signals, using soft thresholds to suppress high-frequency noise or hard thresholds to suppress high-frequency noise. Finally, the signal is reconstructed to retain effective key traveling wave features and obtain effective traveling wave features. In this embodiment, wavelet changes are used to perform a fine analysis of the IMF components to extract effective information that can reflect the essential characteristics of the traveling wave. In subsequent steps, the effective traveling wave features can be used to locate the fault location, which can improve the efficiency and accuracy of fault location.

[0086] Then, step S30 is executed to extract and fuse the effective traveling wave features to obtain the traveling wave feature signal. Step S30 includes the following steps: Figure 3 As shown, S31, extract the time domain features, frequency domain features and time-frequency domain features in the effective traveling wave features to obtain extracted features; S32, perform principal component analysis and normalization processing on the extracted features to obtain traveling wave characteristic signals.

[0087] Specifically, in the feature extraction process, time domain features mainly include feature mean, variance and kurtosis.

[0088] The time-domain feature is used to reflect the sudden change in the amplitude of the traveling wave. The frequency-domain feature obtains the frequency components and their distribution by analyzing the spectrum of the effective traveling wave feature. It should be noted that the frequency-domain feature uses FFT energy spectrum analysis to identify the dominant frequency components of the effective traveling wave feature. The time-frequency domain feature combines information from the time and frequency domains, using the wavelet energy spectrum to locate the energy concentration areas in the time-frequency space of the effective traveling wave feature, which can more comprehensively reflect the changing patterns of the effective traveling wave feature.

[0089] The extracted features are then subjected to principal component analysis and normalization to obtain a traveling wave characteristic signal. Specifically, principal component analysis is an effective dimensionality reduction technique. By performing PCA dimensionality reduction on the extracted features, retaining more than 90% of the variance after dimensionality reduction, and selecting the principal components with the largest variance, this technique removes redundant features and improves signal processing efficiency. Normalization converts the eigenvalues to the same scale, eliminating dimensional differences and facilitating subsequent analysis and processing. After feature extraction and fusion, the resulting traveling wave characteristic signal can more accurately reflect the traveling wave characteristics of the fault, providing strong support for subsequent fault location.

[0090] Finally, step S40 is executed to calibrate and correct the traveling wave characteristic signal using the dynamic topology correction model, and calculate the calibrated and corrected traveling wave characteristic signal using a multi-terminal traveling wave time difference algorithm to obtain the fault location.

[0091] Specifically, execute step S40, such as Figure 4 As shown, the method includes the following steps: S41, constructing a dynamic topology correction model; S42, using the dynamic topology correction model to calibrate the traveling wave head moment in the traveling wave characteristic signal, and correcting the traveling wave velocity in the traveling wave characteristic signal to obtain a calibrated traveling wave head and a corrected traveling wave velocity; S43, using a multi-terminal traveling wave time difference algorithm to calculate the calibrated traveling wave head moment and the corrected traveling wave velocity to obtain the fault location.

[0092] First, execute step S41 to construct a dynamic topology correction model. The specific steps of S41 include: setting a threshold of the fault line according to the wide-area width-first search algorithm, and dynamically determining the network topology within the effective calculation area according to the threshold; updating the line impedance parameters and length parameters in combination with the real-time operation status of the power grid to form a dynamic weighted connectivity graph; and constructing a dynamic topology correction model based on the network topology and the dynamic weighted connectivity graph within the effective calculation area.

[0093] Specifically, starting from the fault point, the search range is gradually expanded to surrounding key nodes according to the principle of breadth-first search. Secondly, during the search process, a threshold (in this embodiment, the threshold can be set to 1.5 times the length of the fault line) is used to determine whether the currently searched node belongs to the valid calculation area. If so, it is included in the calculation range of the dynamic topology correction model; if not, the search is stopped for that node. In this way, the network topology structure within the valid calculation area can be dynamically determined, providing a basis for subsequent calibration and correction of the traveling wave characteristic signal.

[0094] At the same time, the system updates line impedance and length parameters in real time, combining them with the grid's real-time operating status (such as circuit breaker opening and closing signals), forming a dynamic weighted connectivity diagram that accurately reflects the grid's actual conditions. Furthermore, a dynamic topology correction model is constructed based on the network topology and dynamic weighted connectivity diagram within the effective calculation area, providing a more accurate calculation basis for subsequent traveling wave ranging and fault location.

[0095] Then, step S42 is executed to calibrate the traveling wave crest moment in the traveling wave characteristic signal using the dynamic topology correction model, and to correct the traveling wave velocity in the traveling wave characteristic signal to obtain a calibrated traveling wave crest and a corrected traveling wave velocity.

[0096] During this process, the dynamic topology correction model fully considers the real-time operating status and topological changes of the power grid. By precisely calculating the propagation path and time of the traveling wave head in the power grid, it accurately calibrates the time of the traveling wave head. It should be noted that this embodiment uses the Hilbert-Huang Transform (HHT) to calibrate the arrival time of the traveling wave head, and adds Gaussian white noise to suppress modal aliasing and improve the accuracy of high-frequency component detection.

[0097] The model also adjusts the traveling wave velocity based on the actual grid conditions to eliminate the effects of factors such as grid structure and line parameter changes, thereby improving the accuracy of traveling wave ranging and fault location. This embodiment uses a multi-terminal traveling wave ranging formula, combined with iterative corrections (such as Newton interpolation) based on the velocity difference between the two ends of the line, to approximate the true fault distance. This approach ensures that the calibrated traveling wave head moment and corrected traveling wave velocity are more accurate and reliable, providing more precise data support for subsequent fault location.

[0098] Then, the multi-terminal traveling wave time difference algorithm is used to calculate the calibrated traveling wave head moment and the corrected traveling wave velocity to obtain the fault location. Specifically, the calibrated traveling wave head moment and the corrected traveling wave velocity are calculated using the following formula:

[0099]

[0100] Where L is the distance from the fault point to the nearest critical node, v is the corrected traveling wave velocity, Δ T AB , Δ T AC , Δ T BC It is the time difference between the arrival times of the calibration traveling wave heads between adjacent key nodes.

[0101] Using the above formula, the calibration of the traveling wave head moment and the correction of the traveling wave velocity are calculated to confirm the fault location. The following is an example:

[0102] like Figure 5 As shown in the figure, three traveling wave acquisition devices (A, B, C) are installed at three key nodes of a 10kV distribution network. When a fault occurs: the traveling wave acquisition device at the key node A detects the traveling wave head moment =1000.000ms, the traveling wave acquisition device at the key node B detects =1000.053ms, the traveling wave acquisition device at the key node C detects =1000.078ms, the main control unit calculates the time difference between the calibrated traveling wave head moment of the traveling wave acquisition device at the key node A and the calibrated traveling wave head moment of the traveling wave acquisition device at the key node B =0.053ms; the time difference between the calibrated traveling wave head moment of the traveling wave acquisition device at the key node A and the calibrated traveling wave head moment of the traveling wave acquisition device at the key node C = 0.078ms, the time difference between the calibrated traveling wave head moment of the traveling wave acquisition device at the key node B and the calibrated traveling wave head moment of the traveling wave acquisition device at the key node C =0.025ms, the corrected traveling wave velocity is v=298m / μs, and substituting it into the above positioning formula, the distance between the fault point and the traveling wave acquisition device at the key node A is L=2.35km, with an error of ≤50 meters.

[0103] The results calculated in the above examples demonstrate the effectiveness and accuracy of the method described in this embodiment. The method described in this embodiment enables rapid and accurate fault location determination. This not only improves fault location efficiency but also significantly reduces location errors, providing a strong guarantee for the safe and stable operation of the distribution network. Furthermore, this method offers high flexibility and adaptability, being applicable to distribution network lines of varying voltage levels and structures, and possessing significant practical value.

[0104] The embodiment is only a special example and does not represent only one way of implementing the present invention.

[0105] Example 2:

[0106] like Figure 6 As shown, the present invention also provides a distribution network fault location system based on multi-terminal traveling wave ranging, which is used to execute the distribution network fault location method based on multi-terminal traveling wave ranging as described in Example 1. The system includes a traveling wave acquisition device and a main control unit; the traveling wave acquisition device is arranged at N key nodes in the distribution network and is used to collect traveling wave signals at the N key nodes; the main control unit is used to analyze and process the traveling wave signals to obtain effective traveling wave characteristics, extract and fuse the effective traveling wave characteristics to obtain traveling wave characteristic signals; and use a dynamic topology correction model to calibrate and correct the traveling wave characteristic signals to calculate the fault location. The traveling wave acquisition device includes a traveling wave sensor, a high-speed AD sampling unit, and a high-precision clock synchronization module.

[0107] Specifically, traveling wave sensors are used to detect sudden current changes in the distribution network, thereby capturing traveling wave signals. High-speed analog-to-digital sampling units perform high-precision sampling of these signals, ensuring signal integrity and accuracy. High-precision clock synchronization modules ensure that traveling wave acquisition devices at key nodes synchronously collect traveling wave signals, avoiding positioning errors caused by time asynchrony.

[0108] In addition, the system also includes a communication module, which is used to send the calculated fault location to the distribution master station, so that the operation and maintenance personnel at the distribution master station can quickly respond to and handle the fault.

[0109] After receiving the traveling wave signal, the main control unit first performs filtering and denoising to extract valid traveling wave features. These valid traveling wave features are then extracted and fused to produce a traveling wave signature signal containing fault information. The main control unit then uses a pre-established dynamic topology correction model to calibrate and correct the traveling wave signature signal to eliminate the impact of changes in the distribution network structure on the positioning results. Finally, the main control unit uses the dynamic topology correction model to calibrate and correct the traveling wave signature signal and uses a multi-terminal traveling wave time difference algorithm to calculate the data in the traveling wave signature signal to determine the fault location.

[0110] The entire system, through its highly integrated and intelligent design, enables rapid and accurate location of distribution network faults. Its flexibility and adaptability enable it to be applied to distribution networks of varying voltage levels and structures, providing strong support for the safe and stable operation of the distribution network.

[0111] The foregoing is merely a preferred embodiment of the present invention. Those skilled in the art will appreciate that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. Furthermore, under the guidance of the present invention, these features and embodiments may be modified to suit specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be within the scope of the present invention.

Claims

1. A distribution network fault location method based on multi-terminal traveling wave ranging, characterized in that: include: Collecting traveling wave signals at N key nodes in a distribution network, wherein the traveling wave signals are collected by traveling wave collection devices disposed at the N key nodes, and each of the traveling wave collection devices is time-synchronized using a traveling wave time difference positioning model; wherein N in the N key nodes is a positive integer greater than 2; The traveling wave signal is analyzed and processed by adopting a noise suppression algorithm and a wavelet transform to obtain effective traveling wave characteristics; Extracting and fusing the effective traveling wave features to obtain traveling wave feature signals; The traveling wave characteristic signal is calibrated and corrected using a dynamic topology correction model, and the calibrated and corrected traveling wave characteristic signal is calculated using a multi-terminal traveling wave time difference algorithm to obtain the fault location, including: Constructing a dynamic topology correction model; wherein constructing the dynamic topology correction model includes: setting a threshold value of the fault line according to a wide-area width-first search algorithm, and dynamically determining the network topology within the effective calculation area according to the threshold value; updating the line impedance parameters and length parameters in combination with the real-time operation status of the power grid to form a dynamic weighted connectivity graph; constructing the dynamic topology correction model according to the network topology within the effective calculation area and the dynamic weighted connectivity graph; Using the dynamic topology correction model, calibrate the traveling wave head moment in the traveling wave characteristic signal, and correct the traveling wave velocity in the traveling wave characteristic signal to obtain a calibrated traveling wave head and a corrected traveling wave velocity; The multi-terminal traveling wave time difference algorithm is used to calculate the calibrated traveling wave head moment and the corrected traveling wave velocity to obtain the fault location; the multi-terminal traveling wave time difference algorithm is used to calculate the calibrated traveling wave head moment and the corrected traveling wave velocity, and the calculation formula is: Where L is the distance from the fault point to the nearest critical node, v is the corrected traveling wave velocity, Δ T AB is the time difference between the calibrated traveling wave head moment of the traveling wave acquisition device at key node A and the calibrated traveling wave head moment of the traveling wave acquisition device at key node B, Δ T AC is the time difference between the calibrated traveling wave head moment of the traveling wave acquisition device at the key node A and the calibrated traveling wave head moment of the traveling wave acquisition device at the key node C, Δ T BC It is the time difference between the calibrated traveling wave front moment of the traveling wave acquisition device at the key node B and the calibrated traveling wave front moment of the traveling wave acquisition device at the key node C.

2. The distribution network fault location method based on multi-terminal traveling wave ranging according to claim 1 is characterized in that: The noise suppression algorithm and wavelet transform are used to analyze and process the traveling wave signal to obtain effective traveling wave characteristics, including: Preprocessing the traveling wave signal to obtain a preprocessed signal; Decomposing the preprocessed signal into different intrinsic mode functions using CEEMDAN decomposition; The IMF components in the intrinsic mode function are decomposed using wavelet transform to obtain effective traveling wave characteristics.

3. The distribution network fault location method based on multi-terminal traveling wave ranging according to claim 1 is characterized in that: The extracting and fusing the effective traveling wave features to obtain a traveling wave feature signal includes: Extracting time domain features, frequency domain features, and time-frequency domain features from the effective traveling wave features to obtain extracted features; The extracted features are subjected to principal component analysis and normalization processing to obtain traveling wave characteristic signals.

4. The method for locating distribution network faults based on multi-terminal traveling wave ranging according to claim 1, characterized in that: The method further comprises: Constructing N of the key nodes into a distributed network topology, wherein the N key nodes include one key node with a master clock and N-1 key nodes with slave clocks; The key node with the master clock exchanges messages with N-1 key nodes with the slave clocks; According to the message interaction situation, clock synchronization configuration and network delay calculation deviation calculation are performed on the N key nodes to generate a traveling wave time difference positioning model.

5. The method for locating distribution network faults based on multi-terminal traveling wave ranging according to claim 4, characterized in that: The key nodes with master clocks are selected by an optimal master clock algorithm.

6. A distribution network fault location system based on multi-terminal traveling wave ranging, characterized in that: Used to execute the distribution network fault location method based on multi-terminal traveling wave ranging according to any one of claims 1 to 5, the system includes a traveling wave acquisition device and a main control unit; The traveling wave acquisition device is arranged at N key nodes in the distribution network, and is used to collect traveling wave signals at the N key nodes; The main control unit is used to analyze and process the traveling wave signal using the noise suppression algorithm and wavelet transform to obtain effective traveling wave characteristics; extract and fuse the effective traveling wave characteristics to obtain a traveling wave characteristic signal; use the dynamic topology correction model to calibrate and correct the traveling wave characteristic signal, and use the multi-terminal traveling wave time difference algorithm to calculate the calibrated and corrected traveling wave characteristic signal to obtain the fault location.

7. The distribution network fault location system based on multi-terminal traveling wave ranging according to claim 6, characterized in that: The traveling wave acquisition device includes a traveling wave sensor, a high-speed AD sampling unit and a high-precision clock synchronization module.

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