A traveling wave fault location positioning method, device and apparatus for looped network distribution line

Abstract

The application relates to the technical field of power distribution line fault positioning, in particular to a traveling wave fault distance measurement positioning method, equipment and device of a looped network power distribution line. The method comprises the following steps: collecting and preprocessing the current of the power distribution line; calculating the relative change rate of the current signal and fitting a relative change rate curve; selecting an optimal time scale based on the relative change rate curve, and calibrating the wave head arrival time through a second-order difference operator; dividing a fault area through the wave head arrival time and the power distribution network topological structure; using different methods to determine the preliminary position information of the fault points for non-loop faults and loop faults; correcting the fault position by correcting the wave velocity through the wave head arrival time optimization objective function, so as to complete fault distance measurement positioning. The application improves the accuracy of fault distance measurement.

Description

Technical Field

[0001] This application relates to the field of power distribution line fault location technology, specifically to a method, equipment, and device for measuring and locating traveling wave faults in ring network power distribution lines. Background Technology

[0002] As the requirements for stable operation and reliable power supply of power distribution lines become increasingly stringent, the demands for low fault frequency, accurate fault location, and self-healing capabilities after faults are also gradually increasing. Ring network power distribution lines are characterized by a closed-loop structure with multiple power supply paths, exhibiting bidirectional power flow during closed-loop operation, which fundamentally differs from the fault diagnosis methods used for radial power distribution lines.

[0003] Common fault diagnosis methods used in radial networks, such as impedance methods, matrix algorithms, and traveling wave location methods, rely on the electrical parameter characteristics of unidirectional power flow, such as current amplitude and line impedance, and are not suitable for ring network distribution lines. With the integration of distributed generation, such as inverter-interfaced distributed generators (IIDGs), not only is the direction of the fault current changed, but the fault current is also limited to typically not exceeding twice its rated value and decays rapidly. Traditional fault location methods may not be able to detect weak currents at all. Furthermore, because there are multiple power supply lines in a ring network, the basic parameters such as the type and length of different lines vary, resulting in the traveling wave velocity not being a constant value, but being affected by factors such as line type and frequency.

[0004] Therefore, for ring network distribution lines with distributed power supply access, there is still a need for a method, equipment and device that can perform fault location and accurate positioning, which can effectively identify wavefronts under weak current conditions, be compatible with the influence of non-constant traveling wave velocity on the ranging accuracy, and still give a unique fault location when the fault is located within a closed loop. Summary of the Invention

[0005] To address the technical challenges of detecting weak current fault signals, non-constant traveling wave velocity, and the failure of traditional two-end ranging methods due to multi-path loops in ring network distribution lines with distributed power supply integration, this application provides a traveling wave fault ranging and location method, equipment, and apparatus for ring network distribution lines. The specific technical solution adopted is as follows:

[0006] Firstly, this application proposes a method for locating traveling wave faults in ring network distribution lines, which includes the following steps:

[0007] Monitoring points are deployed at key nodes of the ring network power distribution line. Transient traveling wave current signals are collected synchronously at each monitoring point and preprocessed by bandpass filtering, wavelet denoising and amplitude normalization.

[0008] The relative rate of change of the preprocessed signal at each monitoring point is calculated, and the relative rate of change curve of each monitoring point is obtained by time-series fitting.

[0009] The optimal time scale is adaptively selected for the relative rate of change curve of each monitoring point, and the wavefront arrival time of each monitoring point is calibrated based on the second-order difference operator at this scale.

[0010] Based on the wavefront arrival time of each monitoring point and the distribution network topology, it is determined whether the fault point is located within a closed loop, and the fault area is divided into non-loop faults or loop faults.

[0011] When the fault is determined to be non-loop, the preliminary location information of the fault point is obtained based on the dual-end ranging algorithm and the shortest path search. When the fault is determined to be loop, the topologically symmetrical monitoring points of the loop bidirectional traveling wave convergence are virtually disconnected to form an equivalent radial network. The position equation system is constructed based on the time difference of all effective monitoring points and the least squares solution is performed to obtain the preliminary location information of the fault point.

[0012] Based on the time difference of wavefront arrival times at all effective monitoring points across the network, a wave velocity optimization objective function is constructed. After solving for the corrected wave velocity, the fault location is recalculated, and fault ranging and localization are completed.

[0013] In one embodiment, the method for obtaining the relative rate of change is as follows:

[0014] The difference between the current signal amplitude at the k-th monitoring point at time t and the current signal amplitude one sampling period ago is divided by the maximum current signal amplitude collected at that monitoring point within the set time window to obtain the relative rate of change of the k-th monitoring point at time t.

[0015] In one embodiment, the method for obtaining the optimal time scale is as follows:

[0016] The second-order difference operator is solved for the relative change rate curve of the k-th monitoring point at different time scales r. The optimal time scale is selected as the scale that makes the second-order difference operator reach its maximum value at the time of arrival of the fault wavefront and maintain small fluctuations at the time of arrival of the non-fault wavefront.

[0017] In one embodiment, the upper and lower limits of the time scale are determined as follows:

[0018] The upper limit is the product of the maximum scale factor, the minimum expected wavefront rise time, and the sampling frequency; the lower limit is the product of the minimum scale factor, the maximum expected wavefront rise time, and the sampling frequency, with a minimum value of 3. The maximum scale factor ranges from 0.8 to 1, and the minimum scale factor ranges from 0.5 to 0.8.

[0019] In one embodiment, the method for determining whether the fault point is located within a closed loop is as follows:

[0020] For each loop, the time interval between the arrival times of wavefronts at each monitoring point within the loop is calculated, and the maximum value among all the time intervals is compared with a time threshold. If the maximum value is less than the time threshold, the fault is determined to be located within the loop or on an adjacent line of the loop. If the maximum value is greater than or equal to the time threshold, the fault is determined to be located outside the loop. The time threshold is the ratio of the sum of the lengths of all line segments in the closed loop to twice the standard wave velocity.

[0021] In one embodiment, the method for obtaining the preliminary location information of the fault point in a non-loop fault is as follows:

[0022] An adjacency matrix is ​​constructed using the line distance matrix, and the shortest path search algorithm is used to search for the shortest path between the two monitoring points with the earliest arrival time of the traveling wave front. The path distance between the fault point and the two monitoring points along the shortest path is calculated using the double-end ranging formula. The line segment and precise location of the fault point are determined by cumulative search, and the case where the fault point is located on a branch line is handled by branch search.

[0023] In one embodiment, the specific steps for cumulative search and branch search are as follows:

[0024] Cumulative Search: Initialize Cumulative Distance Along the shortest path, the sum is accumulated for each route segment in turn. If 1 ≤ p ≤ q - 1, then the fault point is located on the p-th line segment, and the distances from the two endpoints are respectively... and End the initial search; if Then the updated cumulative distance is further with Compare and perform a search on the (p+1)th route segment until the initial search is completed;

[0025] Branch search: Use the wavefront to reach the third earliest monitoring point o, and obtain the shortest path from monitoring point m to monitoring point o. Calculate along the shortest path The distance m from the fault point to the monitoring point With monitoring point n and monitoring point o as centers, the distances are respectively... , Take a circle with radius , and use the intersection of the two circles and the point on the power distribution line as the initial location of the fault point;

[0026] This represents the length of the p-th route segment. This represents the fault distance corresponding to monitoring point m, and q represents the number of shortest path route segments. , Let represent the distances from the fault point to both ends of the p-th line segment, respectively. This represents the length of the (p+1)th route segment. This represents the fault distance corresponding to monitoring point n.

[0027] In one embodiment, the method for obtaining the corrected wave velocity is as follows:

[0028] Based on the time difference of wavefront arrival times of all effective monitoring points in the network, a wave velocity optimization objective function is constructed with the corrected wave velocity as the independent variable, and the corrected wave velocity is constrained to be within the range of 0.8 to 1.2 times the standard traveling wave velocity. Before solving, it is checked whether the range of time differences exceeds the synchronization resolution threshold. The solution adopts the golden section search method or Newton's iteration method, and the convergence condition is that the wave velocity change rate between two adjacent iterations does not exceed 1%. After the solution is completed, the corrected wave velocity is substituted into the ranging formula to recalculate the fault location.

[0029] Secondly, this application proposes a traveling wave fault location device for ring network power distribution lines. The device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the traveling wave fault location method for ring network power distribution lines described above.

[0030] Thirdly, this application proposes a traveling wave fault location device for ring network distribution lines. The device includes a traveling wave signal acquisition unit, a preprocessing unit, a relative rate of change calculation unit, a wavefront calibration unit, a fault classification unit, a distance measurement execution unit, and a wave velocity optimization unit. The traveling wave signal acquisition unit is communicatively connected to the preprocessing unit, the preprocessing unit is communicatively connected to the relative rate of change calculation unit, the relative rate of change calculation unit is communicatively connected to the wavefront calibration unit, the wavefront calibration unit is communicatively connected to the fault classification unit, the fault classification unit is communicatively connected to the distance measurement execution unit, and the distance measurement execution unit is communicatively connected to the wave velocity optimization unit.

[0031] The beneficial effects of this application are as follows:

[0032] This application adaptively selects the optimal time scale for each monitoring point, enabling the detection of both strong fault current signals and the ambiguous wavefronts of weak current signals. This optimal time scale is then used as a weight in the objective function during subsequent calculations, ensuring the results better match the signal characteristics of monitoring points on the distribution network. Furthermore, this application divides fault areas and selects corresponding fault location methods for different areas, avoiding the use of complex ranging algorithms in non-loop scenarios and preventing location errors caused by using simple radial network ranging directly on fault loops. This application considers the issue of non-constant traveling wave velocity influenced by multiple factors, performing inverse calculations of the wave velocity based on preliminary fault location information to correct the location results based on standard wave velocity, thus improving the accuracy of fault location. The ranging method in this application is tailored to the characteristics of weak current signals, not relying on traditional fault current amplitude threshold detection, but using transient change characteristics for detection, resulting in stronger stability even with distributed power source integration. This application also provides electronic equipment for executing the above method and field devices deployed at key nodes of the distribution network, enabling the method to be implemented at the hardware level and facilitating engineering promotion. Attached Figure Description

[0033] Figure 1 This is a flowchart of a traveling wave fault location method for a ring network power distribution line according to an embodiment of this application;

[0034] Figure 2 This is a schematic diagram of a radial tree structure;

[0035] Figure 3 This is a schematic diagram of a structure containing a closed loop;

[0036] Figure 4 This is a diagram illustrating branch search location.

[0037] Figure 5 This is a structural flowchart of a traveling wave fault location device for a ring network power distribution line, provided in one embodiment of this application.

[0038] Explanation of the attached diagram labels: 11, 13, 21, 23, 25, and 31 represent black circles that are monitoring points; 12, 22, and 24 represent gray circles that are branch nodes; 26 represents a white circle that is a tie switch or a loop-breaking point; and 32 represents a fault point. Detailed Implementation

[0039] The following description, in conjunction with the accompanying drawings, details the specific scheme of the traveling wave fault location method, equipment, and device for ring network distribution lines provided in this application.

[0040] Please see Figure 1The diagram illustrates a flowchart of a traveling wave fault location method for a ring network distribution line according to an embodiment of this application. The method includes the following steps:

[0041] Step S001: Collect the current of the power distribution line and perform preprocessing.

[0042] Monitoring equipment is deployed at key nodes in the ring network distribution line, including but not limited to tie switches, branch points, and distributed power supply grid connection points. Traveling wave sensors are used to collect transient traveling wave current signals. The sampling frequency range is set to 1MHz to 10MHz, with 5MHz in this embodiment. The sampling period needs to cover the fault transient process, typically ranging from 2ms to 4ms, with 3ms set in this embodiment. Data is collected synchronously at each monitoring point, and timing alignment is achieved through clock synchronization to meet accuracy requirements, such as GPS timing, BeiDou timing, or GPS / BeiDou dual-mode timing.

[0043] For the acquired transient traveling wave current signal, considering the potential influence of white noise, harmonic pollution, etc., and the fact that the main energy of the traveling wave signal is concentrated between 10kHz and 1MHz, a three-step preprocessing procedure is performed on the signal acquired at each monitoring point: bandpass filtering, wavelet denoising, and amplitude normalization. Considering that the instantaneous current generated during a fault transient is a singularity compared to the current acquired during normal grid operation, whether it is a sudden increase or a jump, the db6 wavelet basis, which has a good singularity detection effect, is selected for the wavelet transform basis function for three-level decomposition, and soft thresholding is used to denoise the detail coefficients. The amplitude normalization range is [-1, 1], and the normalization method adopts Z-Score and other methods. The purpose of normalization is to reduce the magnitude difference in signal amplitude and reduce the influence of the original amplitude magnitude on the low amplitude of the weak current signal.

[0044] At this point, the preprocessed current signals from each monitoring point were obtained.

[0045] Step S002: Calculate the relative rate of change of the current signal and fit the relative rate of change curve.

[0046] In distributed power supply scenarios, the amplitude of fault current is often less than twice the rated value, making single-point detection methods prone to missed detections or significant errors. Therefore, this application amplifies the data characteristics of the current signal. Fault traveling waves propagate from the fault point to both sides, and the arrival time at different monitoring points depends on the distance from the fault point to each monitoring point. For monitoring points at different distances from the fault point, the arrival time of the traveling wave also differs. If the current changes at all monitoring points are accumulated at the same time, the abrupt change characteristics of the monitoring points where the fault traveling wave arrives first will be weakened by the current at monitoring points where the fault traveling wave has not yet arrived. Therefore, this application extracts the fault abrupt change characteristics for each monitoring point based solely on the preprocessed data at that specific monitoring point.

[0047] Taking the k-th monitoring point as an example, the relative rate of change of the current signal at time t is expressed as:

[0048] , This represents the amplitude of the current signal at time t at the k-th monitoring point. For a single sampling period (one-fifth of the frequency). This represents the amplitude of the current signal at the k-th monitoring point at time t-Δt. This indicates the maximum amplitude of the current signal collected at the k-th monitoring point within the set time window, which is not 0; its function here is to normalize the molecular change based on the historical upper limit at each monitoring point. This represents the relative rate of change at time t for the k-th monitoring point. In this embodiment, the time window size is 10 sampling periods.

[0049] When the fault traveling wave has not yet reached the kth monitoring point at time t, the current signal is in a stable state with a change of almost 0 and a relative change rate approaching 0. When the fault traveling wave reaches the kth monitoring point at time t, the amplitude will change significantly compared to the value before one acquisition cycle, and the relative change rate will be significantly greater than 0.

[0050] Furthermore, for each moment in the signal collected at the k-th monitoring point, the relative rate of change is calculated and sorted according to time sequence. Curve fitting is performed with time t as the independent variable and the relative rate of change as the dependent variable to obtain the relative rate of change curve of the k-th monitoring point.

[0051] At this point, the relative rate of change curve for each monitoring point was obtained.

[0052] Step S003: Select the optimal time scale based on the relative rate of change curve, and calibrate the wavefront arrival time using a second-order difference operator.

[0053] Traditional wavefront calibration methods lack sufficient sensitivity for detecting weak fault current signals in ring network distribution lines connected to distributed power sources, and may even result in missed detections. This is because the traveling wavefront slope of the weak current signal generated by the fault is small, resulting in low differentiation from noise, and the fixed-time-scale detection operator does not match the characteristics of the weak current. The relative rate of change curve at each monitoring point reflects the signal change characteristics caused by the fault traveling wave, based on the historical limit at that monitoring point. The second-order difference operator is a commonly used indicator for detecting curve curvature. When the fault traveling wave reaches a certain monitoring point at a certain moment, the relative rate of change curve at that monitoring point will exhibit a steep rising edge at that moment, and the second derivative will reach an extreme value at that point.

[0054] Therefore, in this application, second-order difference operators at different time scales are calculated based on the relative rate of change curve of each monitoring point. The scale parameters are adjusted for fault signals of different intensities at each monitoring point to accurately calibrate the wavefront arrival time of the monitoring point. If the scale is too small, although high-frequency detection is possible, the anti-interference capability is poor; if the scale is too large, it is suitable for low-frequency detection, but the wavefront abrupt change characteristics will be smoothed by an excessively large time interval. Therefore, it is necessary to calculate the optimal size of the monitoring point so that it can detect both strong fault current signals and the ambiguous wavefronts of weak current signals.

[0055] Specifically, the second-order difference operator for the k-th monitoring point at time t and scale r is denoted as:

[0056] , Indicates that the k-th monitoring point is at The relative rate of change at time t. Indicates that the k-th monitoring point is at The relative rate of change at time t. This represents the relative rate of change at time t for the k-th monitoring point. This represents the second-order difference operator for the k-th monitoring point at time t and scale r, where the unit of scale is the number of sampling points.

[0057] Furthermore, before the fault wavefront arrives, at time t... The time interval between moments and The current signal during the time interval from time t to time t corresponds to the current state of the power distribution line during normal operation. At this time, the second-order difference operator with time t as the dividing point is close to 0; while when time t is the arrival time of the wavefront, the current signal from time t to time t... The time interval between moments includes the current surge component caused by the fault, and... The difference between current signals is maximized within the time interval from time t to time t, at which point the second-order difference operator is at its maximum. Therefore, selecting the most suitable scale r for the k-th monitoring point is to solve the problem within the local range of the analysis of the relative rate of change of current, so that the second-order difference operator is maximized at scale r and maintains a small fluctuation at the arrival time of the non-fault wavefront.

[0058] It should be noted that, since the relative rate of change curve exhibits a monotonically rising edge at the arrival of the wavefront, its second-order difference displays a bipolar response at that point: the positive maximum corresponds to the starting point of the rising edge, and the subsequent negative extremum corresponds to the overshoot at the end of the rising edge. If the extremum is taken based on the absolute value, the overshoot point may be selected, resulting in a delayed calibration of the wavefront arrival time. Therefore, this scheme consistently uses the positive maximum of the second-order difference operator at the same time as the wavefront calibration in subsequent scale selections to ensure that the calibrated time corresponds to the starting point of the wavefront's rising edge.

[0059] Therefore, based on the above analysis, the optimal time scale is calculated as follows:

[0060] , This represents the function for finding the maximum value. and Let r represent the upper and lower limits of the scale r, respectively. This represents the average value of the second-order difference operator at time t for the k-th monitoring point across all scales, characterizing the global average level of the difference output. The smaller this average value, the better the interference suppression. This represents the second-order difference operator for the k-th monitoring point at time t and scale r. This represents the optimal time scale for the k-th monitoring point. It is a very small positive number, and its function is to prevent the denominator from being 0. In this embodiment, the value is 0.01.

[0061] The upper and lower limits of the scale r depend on the sampling frequency and the rise time of the downlink wavefront in the weak fault scenario, and are determined as follows: ; ; In the formula, , These are the maximum and minimum scaling factors, respectively. The scaling factor represents the amount of rise time used to set the size. The range is 0.5 to 0.8. The range is from 0.8 to 1, and in this embodiment, it is set to 0.7 and 1 respectively; since the number of sampling points for isolated noise is usually 1 or 2, 3 is set as the minimum value of the lower limit of the scale. , , representing the minimum expected wavefront rise time and the maximum expected wavefront rise time, respectively, are set to 0.5 microseconds and 3 microseconds in this embodiment; f is the sampling frequency, set to 5MHz in this embodiment. The calculated upper and lower limits in this embodiment are 15 and 3, respectively.

[0062] Furthermore, after obtaining the optimal time scale at the k-th monitoring point, it is used to calibrate the arrival time of the traveling wave. When the wavefront reaches the k-th monitoring point, the relative rate of change curve exhibits a steep rising edge, and the second derivative of the curve reaches an extreme value at the starting point of the rising edge. The difference curve at the k-th monitoring point reaches its maximum value at the optimal time scale. Therefore, the time corresponding to the maximum value in the difference curve at the k-th monitoring point at the optimal time scale is taken as the wavefront arrival time at the k-th monitoring point.

[0063] At this point, the arrival time of the wavefronts at each monitoring point was obtained.

[0064] Step S004: Delineate fault areas based on wavefront arrival time and distribution network topology.

[0065] In ring network power distribution lines, closed loops exist. When the tie switch within the loop is closed, the ring network operates in a closed-loop mode. In this case, fault traveling waves can reach the same monitoring point from two directions, forming multiple equivalent propagation paths, causing the traditional double-ended ranging method on a single path to fail. However, if the fault is on a radial branch outside the loop, there is no multi-path problem, and a simplified ranging method can be directly used. Therefore, it is essential to first determine whether the faulty line is located within the loop before selecting the appropriate ranging strategy.

[0066] Specifically, a topology diagram of the power distribution lines is constructed based on monitoring points and line information. The real-time opening and closing status of the ring network tie switches is obtained through SCADA (Supervisory Control and Data Acquisition) systems or local DTU (Data Transfer Unit) uploads. A topology diagram of the current operating mode is constructed based on the closing status of the tie switches, where each monitoring point corresponds to a node in the topology diagram. If the tie switch is open, the ring network operates in an open-loop manner, and the topology diagram is a radial tree structure with no loops. Figure 2 As shown, Figure 2 In the diagram, black circles 11 and 13 represent monitoring points, gray circle 12 represents a branch node, and white squares represent distributed power sources. If the tie switch is closed, the ring network operates in a closed loop, and the topology diagram contains at least one closed loop. Fault traveling waves can propagate bidirectionally, either clockwise or counterclockwise, as shown below. Figure 3 As shown, black circles 21, 23, and 25 represent monitoring points; gray circles 22 and 24 represent branch nodes; white circle 26 represents a tie switch or loop-breaking point; 21→22→23→24→25→26→21 forms a closed loop.

[0067] When the ring network is in closed-loop operation, the consistency check results of the wavefront arrival times at each monitoring point on all loops in the line topology diagram are used to determine whether the fault is located in the loop. The above steps extract the wavefront arrival time by setting an optimal time scale for each monitoring point. The wavefront arrival times at each monitoring point will not be exactly the same. The fault is determined based on the time difference between the wavefront arrival times at each monitoring point on each loop.

[0068] For each loop, the time interval between the arrival times of the wavefronts at each monitoring point within the loop is calculated. The maximum value of all these time intervals is compared with a time threshold. If the maximum value is less than the time threshold, the fault is considered to be located within the loop or on an adjacent line within the loop. If the maximum value is greater than or equal to the time threshold, the fault is considered to be located outside the loop. The time threshold is the ratio of the sum of the lengths of all line segments in the closed loop to twice the standard wave velocity. The standard wave velocity is obtained through a quick reference table, and the line mode wave velocity is selected according to the line type of the fault path: 2.98 × 10⁸ m / s for overhead lines and 1.7 × 10⁸ - 2.2 × 10⁸ m / s for cable lines.

[0069] The basis for this judgment is that when the fault is located inside the loop, the fault traveling wave propagates in both directions of the loop, and the difference in wavefront arrival time between monitoring points inside the loop is small; when the fault is located outside the loop, the wavefront arrival time of monitoring points inside the loop comes from the same direction (from outside the loop to inside the loop), and the difference in wavefront arrival time is greater depending on the distance of the monitoring points from the loop entrance.

[0070] For networks with multiple loops, each loop is determined one by one using the method described above. If only one loop is determined to be a fault within the loop, that loop is used as the input for subsequent steps. If multiple loops are determined to be faults within the loop, the minimum of the maximum values ​​among the multiple time intervals is taken. If no loop is determined to be a fault within the loop, ranging is performed as if it were a non-loop fault.

[0071] By dividing the fault area, corresponding fault ranging methods are selected for different fault areas, avoiding the use of complex ranging algorithms in non-loop scenarios, and avoiding positioning errors caused by using simple radial network ranging on fault loops.

[0072] This completes the division of the fault area.

[0073] Step S005: For non-loop faults and loop faults, different methods are used to determine the preliminary location information of the fault point.

[0074] The above steps involve two classifications: non-loop faults and loop faults.

[0075] Non-loop faults: Inputs include wavefront arrival time, radial topology map, and line distance matrix (the element in the i-th row and j-th column represents the line length from node i to node j, and the diagonal elements are 0). For non-loop radial distribution lines, the traveling wave propagation path from the fault point to each monitoring point is unique, but the path may pass through multiple intermediate nodes. It is necessary to accurately calculate the shortest path length from the fault point to the two monitoring points, and then use the two-end distance measurement formula based on this.

[0076] Specifically, an adjacency matrix is ​​first constructed using the line distance matrix (when node i and node j are adjacent, the element in the i-th row and j-th column of the matrix is ​​the line length from node i to node j, and the remaining elements are 0). Then, a shortest path search algorithm, specifically the Floyd-Warshall shortest path search algorithm in this embodiment, is used to search for the shortest path between the two nodes with the earliest arrival time of the traveling wave front on the radial topology graph. Where m and n are the earliest and second earliest monitoring points of arrival time of the traveling wave front, respectively; q represents the number of route segments on the shortest path. Let represent the lengths of the path from monitoring point m to the first node on the shortest path (i.e., the length of the first path segment), the lengths of the path from the first node to the second node on the shortest path (i.e., the length of the second path segment), and the lengths of the path from the second-to-last node on the shortest path to monitoring point n (i.e., the length of the q-th path segment). Shortest path search is existing technology and will not be described in detail here.

[0077] Secondly, the two nodes with the earliest arrival time of the traveling wave front are used as the reference endpoints for double-end ranging. This is because the premise of the double-end ranging formula is that the fault traveling wave propagates along the shortest path. The traveling wave generated by the fault propagates to both sides, and the time difference between the two monitoring points is equal to half the time of the traveling wave's round-trip propagation between the fault point and the two ends multiplied by the wave speed.

[0078] Based on the above analysis, calculate the path distance between the fault point and monitoring points m and n along the shortest path: ; ; In the formula, The shortest path distance from monitoring point m to monitoring point n is equal to the sum of the lengths of all road segments on the shortest path. Indicates the standard traveling wave velocity; , These are the arrival times of the traveling wavefronts at monitoring points m and n, respectively. , These represent the fault distances corresponding to monitoring points m and n, respectively.

[0079] Next, considering the total path length constraint: Find the unique solution , Further, perform consistency verification: Alternatively, the calculated fault distance can be compared with the known branch point coordinates on the main line of the distribution network to verify whether they are consistent. If they are consistent, an incremental search is performed; if they are not consistent, the fault point is determined to be located on a certain branch line, and the search is switched to a branch search.

[0080] Cumulative search: finding the shortest path The search is performed segment by segment to determine the segment where the fault point is located and its precise location on that segment. Specifically:

[0081] Initialize accumulated distance Along the shortest path, the sum is accumulated for each route segment in turn. If 1 ≤ p ≤ q - 1, then the fault point is located on the p-th line segment, and the distances from the two endpoints are respectively... and End the initial search; if Then the updated cumulative distance is The sum of the three and further with Compare and perform a search on the (p+1)th route segment until the initial search is complete. Let represent the length of the p-th route segment on the shortest path. This represents the length of the (p+1)th segment on the shortest path.

[0082] Branch search: If the consistency verification is not satisfied, it means that the fault location is not on the shortest path. In this case, use the monitoring point o with the third earliest arrival time of the wavefront to obtain the shortest path from monitoring point m to monitoring point o. Calculate along the shortest path The distance m from the fault point to the monitoring point With monitoring point n and monitoring point o as centers, the distances are respectively... , A circle is drawn with a radius equal to the specified radius. The intersection of the two circles, where the point lies on the distribution network line, is the initial location of the fault. Specifically, if the intersection point is on the distribution network line, it is the fault location; if the point is an intersection but not on the distribution network line, it is not considered the fault location. If the two circles do not intersect or the intersection point is not on any line, it indicates a measurement error or topology error, triggering an alarm. Figure 4 As shown, 11, 13, and 31 represent monitoring points m, n, and o, respectively. Intersection point 32 represents the initially located fault point. Although there is an intersection point below, it is not on the power distribution network line and is not determined to be a fault point.

[0083] Loop Fault Branch: The required data includes wavefront arrival time, topology map containing loops, and faulty loops.

[0084] When a fault is located on a loop, the fault traveling wave will propagate in both clockwise and counterclockwise directions along the loop, forming two equivalent propagation paths. Directly applying the two-end ranging formula will yield two possible fault locations (corresponding to the two paths), making it impossible to uniquely determine the true fault point. Furthermore, the existence of a loop may cause some monitoring points to receive traveling waves from both directions, resulting in a superposition of waves. When the traveling wave propagates along both directions of the loop, the node that receives the wavefront last is the topologically symmetrical midpoint of the bidirectional convergence of the traveling waves. Disconnecting this node directly eliminates the redundant path of reverse routing. The distribution of adjacent lines to the converging receiving point is obtained from the data relationship in the network topology diagram. Based on the principle of edge selection, constraints are set (either selecting the network segment with the smaller node number among the adjacent network parameters, or selecting the network segment with the smaller resistance value of two adjacent equivalent threads). A specific virtual disconnection is implemented for the network segment that meets the conditions, eliminating that segment. This yields a non-loop equivalent radial network for subsequent location.

[0085] Specifically, the monitoring points are first screened, and the valid monitoring points are selected: the first screening condition is that the wavefront must be successfully calibrated, that is, the wavefront arrival time is not 0; the second screening condition is that the wavefront arrival time of any monitoring point cannot be earlier than the longest time required for the traveling wave to cross the entire distribution network line, otherwise it means that the wavefront arrival time of the point may be a misjudgment of the reflected wave as the initial wavefront; the third screening condition is that the monitoring point is not a monitoring point on the line to be eliminated.

[0086] The location process is as follows: For an equivalent radial network, the location equation is constructed using the time difference of all effective monitoring points: the theoretical time difference between the fault point and the z-th effective monitoring point is equal to the actual time difference;

[0087] , This represents the wavefront arrival time of the z-th valid monitoring point. Indicates the time when the fault occurred. This represents the line distance between the fault point and the z-th valid monitoring point.

[0088] Based on the above process, the fault location information to be solved includes the fault location along the line, the line identifier, and the fault arrival time, totaling three parameters. Each valid monitoring point can construct a location equation. At this time, the number of equations is greater than the number of parameters to be solved, which is a typical overdetermined problem. The least squares method is used to solve for the minimum value of the objective function. The constraints in the solution process are that the fault occurrence time must not be later than the earliest arrival time of the monitoring point and the fault location must be located on the line. Numerical methods such as Levenberg-Marquardt (LM) and Gauss-Newton method can be used. These are well-known technologies and will not be described in detail in this application. In this embodiment, the LM algorithm is used.

[0089] Its objective function is: ; In the formula, N is the number of effective monitoring points. The influence weight of the z-th effective monitoring point depends on the optimal time scale of each effective monitoring point. The smaller the optimal time scale, the higher the signal-to-noise ratio and the greater the weight. It represents the time residual of each valid monitoring point. The square is taken so that only the minimum residual is considered in the solution process, and the direction of the residual is not considered.

[0090] The specific expression for influencing the weight is: ; in, The maximum value within the optimal time scale for effective monitoring points. The optimal time scale for the z-th effective monitoring point is... To determine the optimal time scale for the e-th effective monitoring point, a very small positive number (e.g., 0.001) is added to the denominator during calculation to prevent the denominator from being 0.

[0091] Thus, the location information of the fault point and the time of the fault occurrence were obtained.

[0092] Step S006: The fault location is corrected by optimizing the objective function through wavefront arrival time to correct the wave velocity and complete the fault ranging.

[0093] The above steps provide a preliminary fault location. However, the propagation speed of the traveling wave is not constant but is affected by various factors: different line types result in significant differences in wave speed; environmental factors include temperature changes causing variations in conductor resistance and line inductance, which in turn affect the wave speed; and frequency characteristics, the propagation speeds of different frequency components of the traveling wave exhibit slight dispersion (dispersion effect). If a fixed wave speed is used for ranging, it may result in a positioning error of hundreds or even thousands of meters for long lines.

[0094] In this application, time difference measurement information from multiple monitoring points across the network is used to construct a wave velocity optimization objective function, solve for the optimal wave velocity, and use it to correct the fault location. At the same time, a time difference verification mechanism is introduced to prevent abnormal data from causing optimization failure.

[0095] Before performing wave velocity optimization, the time difference of wavefront arrival times at all valid monitoring points is first verified. First, the wavefront arrival times of all valid monitoring points in the network are obtained. Then, the maximum and minimum values ​​are extracted, and their range is calculated. If this range is greater than or equal to the synchronization resolution threshold (consistent with the accuracy of time alignment during data acquisition), the following online iterative wave velocity optimization continues. If the range is less than the threshold, the fault location obtained in the above steps is used to directly calculate the location result. The range reflects the spatial resolution capability of the distribution network structure for fault locations. Only when the range is sufficiently large (exceeding the uncertainty of time synchronization) does the time difference data carry valid spatial location information and can be used for wave velocity inversion.

[0096] Specifically, a wave speed optimization objective function is constructed; the smaller the function value, the better the fit convergence. ; In the formula, Q represents the effective set of monitoring points; Here are the weighting coefficients for the z-th and c-th valid monitoring point pairs. These weighting coefficients depend on the line length between the valid monitoring points. , These are the wavefront arrival times of the z-th and c-th valid monitoring points, respectively. , These are the line lengths from the fault location to the z-th and c-th valid monitoring points, respectively. Indicates the corrected wave velocity; It represents the actual wavefront time difference measured at two monitoring points; This represents the difference in theoretical path distances between the initially located fault point H and the z-th and c-th monitoring points. The larger this distance difference, the longer the corresponding propagation time should be.

[0097] The expression for the weighting coefficient is: , The maximum line length between effective monitoring points. This represents the length of the path from the z-th valid monitoring point to the c-th valid monitoring point. Shorter paths have higher weights because their time measurement errors are relatively smaller.

[0098] The constraint in the solution process is: corrected wave velocity. Located within 0.8 to 1.2 times the standard traveling wave velocity, the wave velocity is allowed to be adjusted within ±20% of the initial value, covering the range of wave velocity fluctuations caused by environmental changes. Since v appears in the denominator of J(v), it is a single-variable nonlinear function, which can be solved using the golden section search method or Newton's iteration method. The convergence condition is that the rate of change of wave velocity between two adjacent iterations does not exceed 1%. The specific solution is existing technology, and will not be described in detail in this application.

[0099] Finally, the corrected wave velocity is substituted into the ranging formula in step S005 to recalculate the fault location and complete the fault ranging.

[0100] At this point, the fault location has been completed.

[0101] Based on the same inventive concept as the above method, this embodiment of the invention also provides a traveling wave fault location device for ring network power distribution lines.

[0102] Specifically, the processor includes at least one of an embedded microprocessor, a field-programmable gate array (FPGA), or a digital signal processor (DSP); the memory includes at least one of a read-only memory (ROM), a random access memory (RAM), or a non-volatile memory. The device also includes a data acquisition interface, a clock synchronization interface, and a communication interface: the data acquisition interface receives the transient traveling wave current signal output by the traveling wave sensor at the monitoring point; the clock synchronization interface supports GPS timing, BeiDou timing, or GPS / BeiDou dual-mode timing, used for timing alignment of the sampling times of each monitoring point; the communication interface reports the final location information of the fault point and the fault occurrence time to the SCADA system or dispatch master station. The device uses an FPGA or DSP to perform parallel acceleration calculations on the second-order difference operator to meet the millisecond-level wavefront calibration delay requirements.

[0103] The device also includes a power management module, an electromagnetic shielding enclosure, and a lightning surge protection circuit: the power management module supports station DC power supply or online uninterruptible power supply (UPS); the electromagnetic shielding enclosure is used to suppress the influence of electromagnetic interference on the acquisition of transient traveling wave current signals; the lightning surge protection circuit is used to protect the device when it is struck by lightning or subjected to a switching surge.

[0104] Based on the same inventive concept as the method described above, this embodiment of the invention also provides a traveling wave fault location device for ring network power distribution lines. Please refer to... Figure 5 This document illustrates a schematic diagram of a traveling wave fault location device for a ring network power distribution line according to an embodiment of this application. The device is deployed at a key node of the ring network power distribution line and includes a traveling wave signal acquisition unit, a preprocessing unit, a relative rate of change calculation unit, a wavefront calibration unit, a fault classification unit, a ranging execution unit, and a wave velocity optimization unit. The traveling wave signal acquisition unit is communicatively connected to the preprocessing unit, the preprocessing unit is communicatively connected to the relative rate of change calculation unit, the relative rate of change calculation unit is communicatively connected to the wavefront calibration unit, the wavefront calibration unit is communicatively connected to the fault classification unit, the fault classification unit is communicatively connected to the ranging execution unit, and the ranging execution unit is communicatively connected to the wave velocity optimization unit.

[0105] Specifically, the wave velocity optimization unit further includes a time difference verification subunit and a wave velocity inversion subunit: the time difference verification subunit is used to obtain the range of wavefront arrival times of all effective monitoring points in the network and compare it with the synchronization resolution threshold to determine whether to perform wave velocity inversion; the wave velocity inversion subunit is used to construct a wave velocity optimization objective function and solve for the corrected wave velocity when the range exceeds the synchronization resolution threshold. The ranging execution unit further includes a two-end ranging subunit and a multi-end least squares solving subunit: the two-end ranging subunit is used to perform preliminary ranging for non-loop faults; the multi-end least squares solving subunit is used to perform preliminary ranging for loop faults. The traveling wave signal acquisition unit uses a traveling wave current transformer, and the preprocessing unit uses an FPGA chip to implement bandpass filtering and wavelet denoising algorithms; the traveling wave signal acquisition unit communicates with the preprocessing unit, the relative rate of change calculation unit, the wavefront calibration unit, the fault classification unit, the ranging execution unit, and the wave velocity optimization unit via a backplane bus or industrial Ethernet.

[0106] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for locating traveling wave faults in a ring network power distribution line, characterized in that, The method includes the following steps: Monitoring points are deployed at key nodes of the ring network power distribution line. Transient traveling wave current signals are collected synchronously at each monitoring point and preprocessed by bandpass filtering, wavelet denoising and amplitude normalization. The relative rate of change of the preprocessed signal at each monitoring point is calculated, and the relative rate of change curve of each monitoring point is obtained by time-series fitting. The optimal time scale is adaptively selected for the relative rate of change curve of each monitoring point, and the wavefront arrival time of each monitoring point is calibrated based on the second-order difference operator at this scale. Based on the wavefront arrival time of each monitoring point and the distribution network topology, it is determined whether the fault point is located within a closed loop, and the fault area is divided into non-loop faults or loop faults. When the fault is determined to be non-loop, the preliminary location information of the fault point is obtained based on the dual-end ranging algorithm and the shortest path search. When the fault is determined to be loop, the topologically symmetrical monitoring points of the loop bidirectional traveling wave convergence are virtually disconnected to form an equivalent radial network. The position equation system is constructed based on the time difference of all effective monitoring points and the least squares solution is performed to obtain the preliminary location information of the fault point. Based on the time difference of wavefront arrival times at all effective monitoring points across the network, a wave velocity optimization objective function is constructed. After solving for the corrected wave velocity, the fault location is recalculated, and fault ranging and localization are completed.

2. The method for locating traveling wave faults in a ring network distribution line as described in claim 1, characterized in that, The method for obtaining the relative rate of change is as follows: The difference between the current signal amplitude at the k-th monitoring point at time t and the current signal amplitude one sampling period ago is divided by the maximum current signal amplitude collected at that monitoring point within the set time window to obtain the relative rate of change of the k-th monitoring point at time t.

3. The method for locating traveling wave faults in a ring network distribution line as described in claim 1, characterized in that, The method for obtaining the optimal time scale is as follows: The second-order difference operator is solved for the relative change rate curve of the k-th monitoring point at different time scales r. The optimal time scale is selected as the scale that makes the second-order difference operator reach its maximum value at the time of arrival of the fault wavefront and maintain small fluctuations at the time of arrival of the non-fault wavefront.

4. The method for determining and locating traveling wave faults in a ring network distribution line as described in claim 3, characterized in that, The method for determining the upper and lower limits of the time scale is as follows: The upper limit is the product of the maximum scale factor, the minimum expected wavefront rise time, and the sampling frequency; the lower limit is the product of the minimum scale factor, the maximum expected wavefront rise time, and the sampling frequency, with a minimum value of 3. The maximum scale factor ranges from 0.8 to 1, and the minimum scale factor ranges from 0.5 to 0.

8.

5. The method for locating traveling wave faults in a ring network distribution line as described in claim 4, characterized in that, The method for determining whether the fault point is located within a closed loop is as follows: For each loop, the time interval between the arrival times of wavefronts at each monitoring point within the loop is calculated, and the maximum value among all the time intervals is compared with a time threshold. If the maximum value is less than the time threshold, the fault is determined to be located within the loop or on an adjacent line of the loop. If the maximum value is greater than or equal to the time threshold, the fault is determined to be located outside the loop. The time threshold is the ratio of the sum of the lengths of all line segments in the closed loop to twice the standard wave velocity.

6. The method for locating traveling wave faults in a ring network distribution line as described in claim 1, characterized in that, The method for obtaining the preliminary location information of the fault point in a non-loop fault is as follows: An adjacency matrix is ​​constructed using the line distance matrix, and the shortest path search algorithm is used to search for the shortest path between the two monitoring points with the earliest arrival time of the traveling wave front. The path distance between the fault point and the two monitoring points along the shortest path is calculated using the double-end ranging formula. The line segment and precise location of the fault point are determined by cumulative search, and the case where the fault point is located on a branch line is handled by branch search.

7. The method for locating traveling wave faults in a ring network distribution line as described in claim 6, characterized in that, The specific steps for cumulative search and branch search are as follows: Cumulative Search: Initialize Cumulative Distance Along the shortest path, the sum is accumulated for each route segment in turn. If 1 ≤ p ≤ q - 1, then the fault point is located on the p-th line segment, and the distances from the monitoring points at both ends are respectively... and End the initial search; if Then the updated cumulative distance is further with Compare and perform a search on the (p+1)th route segment until the initial search is completed; Branch search: Use the wavefront to reach the third earliest monitoring point o, and obtain the shortest path from monitoring point m to monitoring point o. Calculate along the shortest path The distance m from the fault point to the monitoring point With monitoring point n and monitoring point o as centers, the distances are respectively... , Take a circle with radius , and use the intersection of the two circles and the point on the power distribution line as the initial location of the fault point; This represents the length of the p-th route segment. This represents the fault distance corresponding to monitoring point m, and q represents the number of shortest path route segments. , Let represent the distances from the fault point to both ends of the p-th line segment, respectively. This represents the length of the (p+1)th route segment. This represents the fault distance corresponding to monitoring point n.

8. The method for determining and locating traveling wave faults in a ring network distribution line as described in claim 1, characterized in that, The method for obtaining the corrected wave velocity is as follows: Based on the time difference of wavefront arrival times of all effective monitoring points in the network, a wave velocity optimization objective function is constructed with the corrected wave velocity as the independent variable, and the corrected wave velocity is constrained to be within the range of 0.8 to 1.2 times the standard traveling wave velocity. Before solving, it is checked whether the range of time differences exceeds the synchronization resolution threshold. The solution adopts the golden section search method or Newton's iteration method, and the convergence condition is that the wave velocity change rate between two adjacent iterations does not exceed 1%. After the solution is completed, the corrected wave velocity is substituted into the ranging formula to recalculate the fault location.

9. A traveling wave fault location device for a ring network power distribution line, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the traveling wave fault location method for ring network distribution lines as described in any one of claims 1 to 8.

10. A traveling wave fault location device for a ring network distribution line, deployed at key nodes of a ring network distribution network, characterized in that, To implement the traveling wave fault location method for a ring network distribution line as described in claim 1, the device includes a traveling wave signal acquisition unit, a preprocessing unit, a relative rate of change calculation unit, a wavefront calibration unit, a fault classification unit, a distance measurement execution unit, and a wave velocity optimization unit; wherein, the traveling wave signal acquisition unit is communicatively connected to the preprocessing unit, the preprocessing unit is communicatively connected to the relative rate of change calculation unit, the relative rate of change calculation unit is communicatively connected to the wavefront calibration unit, the wavefront calibration unit is communicatively connected to the fault classification unit, the fault classification unit is communicatively connected to the distance measurement execution unit, and the distance measurement execution unit is communicatively connected to the wave velocity optimization unit.

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