Power distribution network fault positioning method and system based on wave head error and weight distribution

Abstract

The invention discloses a power distribution network fault positioning method and system based on wave head errors and weight distribution, and belongs to the technical field of power distribution network fault positioning. The method comprises the steps that a weight matrix of a target line of the power distribution network is acquired, the shortest distance between nodes of the target line is calculated based on the weight matrix, and the arrival time of initial traveling waves after the target line breaks down is recorded based on a traveling wave detection device; constructing a time deviation model according to the arrival time and the transmission characteristics of the traveling waves on the basis of the shortest distance and the wave head error between the nodes; and performing weight distribution on the importance degree of the traveling wave detection device, correcting a time deviation model based on a weight distribution result, and positioning a target line fault based on the corrected time deviation model. According to the method, the fault positioning precision is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and more specifically, to a distribution network fault location method and system based on wave head error and weight distribution. Background Art

[0002] The distribution network is the link between the transmission network and users. It shoulders the important mission of ensuring the healthy development of the national economy and the continuous improvement of residents' living standards. Its construction and development has attracted great attention at the national level. The distribution network is distributed over a wide area, and its line length accounts for more than 90% of the total length of power grid lines at all levels. The distribution lines are exposed to harsh environments such as wind, rain, scorching sun, ice and snow all year round or are buried deep underground. The operating environment is harsh and random faults occur frequently. If they cannot be checked in time, they are easy to cause personal and equipment safety accidents and directly lead to power outages for users. The increase in the proportion of new energy will make the topology and working conditions of the distribution network more complex, and the fault points will be more difficult to detect, affecting the reliability of power supply. Therefore, accurate fault location is of great significance.

[0003] Existing accurate fault location methods can be roughly divided into two categories according to their operating principles:

[0004] Impedance-based method and traveling wave-based method. The impedance-based method is easily affected by factors such as network structure, line parameters, transformer saturation and three-phase load imbalance, and has low positioning accuracy. In addition, inverter-based distributed power sources (DG) usually have fault ride-through capabilities, which has an adverse effect on impedance-based methods. The traveling wave method is less affected by the system operation mode, has a fast operation speed, and only requires a time scale signal of the microsecond level to achieve fault location, which can greatly reduce the impact of DG access.

[0005] The single-ended method of traveling wave fault location requires fewer traveling wave detection devices and does not have multi-terminal time synchronization errors. However, the distribution network has short lines and many branches, and the reflected traveling waves are difficult to identify, so the positioning accuracy is low, and it may even be impossible to locate the fault. The two-terminal method uses the time information of the initial traveling wave of the fault reaching both ends of the line to locate the fault. The positioning accuracy is high, but it is easily affected by the time synchronization of the traveling wave detection device at one end. Summary of the invention

[0006] In view of the above problems, the present invention proposes a distribution network fault location method based on wave head error and weight distribution, comprising:

[0007] Obtaining a weight matrix of a target line of the distribution network, and calculating the shortest distance between nodes of the target line based on the weight matrix, and recording the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device;

[0008] Based on the shortest distance between the nodes and the wave head error, a time deviation model is constructed according to the arrival time and the transmission characteristics of the traveling wave;

[0009] The importance of the traveling wave detection device is weighted, the time deviation model is corrected based on the weighted distribution result, and the target line fault is located based on the corrected time deviation model.

[0010] Optionally, obtain the weight matrix of the target line of the distribution network, including:

[0011] According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E);

[0012] Among them, V is the vertex set of the graph, and E is the edge set of the graph;

[0013] The edges of the constructed graph are given quantitative indices to generate a weight matrix.

[0014] Optionally, calculating the shortest distance between nodes of the target line based on the weight matrix includes:

[0015] After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm;

[0016] The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

[0017] Optionally, the method further includes: detecting the traveling wave head of the target line by a traveling wave detection device to identify whether the target line has a fault, and the fault judgment formula is as follows:

[0018]

[0019] Among them, ΔU P is the change of the traveling wave head, t 1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

[0020] Optional, time deviation model, as follows:

[0021]

[0022] st0≤FTi≤lmax

[0023] 0.9vmax≤v≤vmax

[0024] tmin-lmax / 0.9v max≤t0≤tmin+lmax / 0.9vmax

[0025] Among them, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, and t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0026] Optionally, based on the modified time deviation model, the target line fault is located, including:

[0027] Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

[0028] An optional, modified time-bias model is as follows:

[0029]

[0030] st0≤lFTi≤lmax

[0031] 0.9vmax≤v≤vmax

[0032] tmin-lmax / 0.9vmax≤t0≤tmin+lmax / 0.9vmax

[0033] Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0034] Optionally, the calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows:

[0035]

[0036] Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

[0037] On the other hand, the present invention also proposes a distribution network fault location system based on wave head error and weight distribution, comprising:

[0038] An initial unit, used to obtain a weight matrix of a target line of the distribution network, calculate the shortest distance between nodes of the target line based on the weight matrix, and record the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device;

[0039] A model building unit, configured to build a time deviation model based on the shortest distance between the nodes and the wave head error, according to the arrival time and the transmission characteristics of the traveling wave;

[0040] The solving unit is used to assign weights to the importance of the traveling wave detection device, modify the time deviation model based on the weight assignment result, and locate the target line fault based on the modified time deviation model.

[0041] Optionally, obtain the weight matrix of the target line of the distribution network, including:

[0042] According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E);

[0043] Among them, V is the vertex set of the graph, and E is the edge set of the graph;

[0044] The edges of the constructed graph are given quantitative indices to generate a weight matrix.

[0045] Optionally, calculating the shortest distance between nodes of the target line based on the weight matrix includes:

[0046] After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm;

[0047] The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

[0048] Optionally, the model building unit is further used to: detect the traveling wave head of the target line through a traveling wave detection device to identify whether the target line has a fault, and the fault judgment formula is as follows:

[0049]

[0050] Among them, ΔU P is the change of the traveling wave head, t1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

[0051] Optional, time deviation model, as follows:

[0052]

[0053] st0≤lFTi≤lmax

[0054] 0.9vmax≤v≤vmax

[0055] tmin-lmax / 0.9vmax≤t0≤tmim+lmax / 0.9vmax

[0056] Among them, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, and t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0057] Optionally, based on the modified time deviation model, the target line fault is located, including:

[0058] Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

[0059] An optional, modified time-bias model is as follows:

[0060]

[0061] st0≤lFTi≤lmax

[0062] 0.9vmax≤v≤vmax

[0063] t min-lmax / 0.9vmax≤t0≤t min+lmax / 0.9vmax

[0064] Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, t′0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0065] Optionally, the calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows:

[0066]

[0067] Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

[0068] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0069] a processor for executing one or more programs;

[0070] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0071] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention provides a distribution network fault location method based on wave head error and weight allocation, including: obtaining the weight matrix of the target line of the distribution network, and calculating the shortest distance between the nodes of the target line based on the weight matrix, recording the arrival time of the initial traveling wave after the target line fails based on the traveling wave detection device; based on the shortest distance between the nodes and the wave head error, constructing a time deviation model according to the arrival time and the transmission characteristics of the traveling wave; weighting the importance of the traveling wave detection device, and based on the weight allocation result, correcting the time deviation model, and locating the target line fault based on the corrected time deviation model. The present invention significantly improves the accuracy of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a flow chart of the method of the present invention;

[0075] Figure 2 A schematic diagram of a simple T-type distribution network fault traveling wave transmission path of the method of the present invention;

[0076] Figure 3 It is a schematic diagram of a 10kv complex distribution network of the method of the present invention;

[0077] Figure 4 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0078] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0079] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0080] Embodiment 1:

[0081] The present invention proposes a distribution network fault location method based on wave head error and weight allocation, such as Figure 1 As shown, including:

[0082] Step 1: Obtain a weight matrix of a target line of a distribution network, and calculate the shortest distance between nodes of the target line based on the weight matrix, and record the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device;

[0083] Step 2: Based on the shortest distance between the nodes and the wave head error, a time deviation model is constructed according to the arrival time and the transmission characteristics of the traveling wave;

[0084] Step 3: weight the importance of the traveling wave detection device, modify the time deviation model based on the weight distribution result, and locate the target line fault based on the modified time deviation model.

[0085] Among them, obtaining the weight matrix of the target line of the distribution network includes:

[0086] According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E);

[0087] Among them, V is the vertex set of the graph, and E is the edge set of the graph;

[0088] The edges of the constructed graph are given quantitative indices to generate a weight matrix.

[0089] The shortest distance between nodes of the target line is calculated based on the weight matrix, including:

[0090] After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm;

[0091] The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

[0092] The method further includes: detecting the traveling wave head of the target line by a traveling wave detection device to identify whether the target line has a fault, and the fault judgment formula is as follows:

[0093]

[0094] Among them, ΔU P is the change of the traveling wave head, t 1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

[0095] Among them, the time deviation model is as follows:

[0096]

[0097] st0≤lFTi≤lmax

[0098] 0.9vmax≤v≤vmax

[0099] tmin-lmax / 0.9vmax≤t0≤tmin+lmax / 0.9vmax

[0100] Among them, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, and t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0101] The target line fault is located based on the modified time deviation model, including:

[0102] Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

[0103] Among them, the modified time deviation model is as follows:

[0104]

[0105] st0≤lFTi≤lmax

[0106] 0.9vma.x≤v≤vmax

[0107] tmin-lmax / 0.9vmax≤t0≤tmin+lmax / 0.9vmax

[0108] Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0109] The calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows:

[0110]

[0111] Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

[0112] The present invention is further described below with reference to specific cases:

[0113] The present invention analyzes the error source of the initial traveling wave head calibration, establishes the intrinsic connection between the head calibration time and the fault point, defines the time deviation, and realizes fault location by comparing the deviation size. Subsequently, the importance of each end device is weighted according to the deviation size of each end device, which can reduce the influence of the positioning result on the head error.

[0114] Part 1: S1. Obtain the weight matrix L of the corresponding line = (l ij ) n×n, where i and j are different nodes, n is the total number of nodes, and the shortest distance between each node is calculated.

[0115] S2. After a distribution line fault occurs, each traveling wave detection device will record the arrival time of the initial traveling wave.

[0116] S3. Based on the traveling wave transmission characteristics, a time deviation model is constructed.

[0117] S4. Calculate the minimum time deviation in each branch in turn, and compare to obtain the minimum time deviation of the entire network. At this time, the corresponding fault position is the actual fault position.

[0118] S5. The importance of each terminal device is weighted according to the deviation size of each terminal device, which can reduce the influence of the positioning result on the wave head error and correct the positioning result.

[0119] S1 The specific steps are as follows: Based on the topological structure of the distribution system, with busbars or feeders as nodes, overhead lines or cables between busbars and feeders as edges, and line length as edge features, a graph G = (V, E) is constructed, where V represents the vertex set of the graph and E represents the edge set of the graph. The quantitative index assigned to the edges of the network graph is called "weight". For an n-node distribution system, the weight matrix L is an n×n matrix. When node i is directly connected to node j, l ij is the weight, otherwise it is positive infinity.

[0120] After obtaining the weight matrix, the shortest distance between nodes is obtained through Floyd. The Floyd algorithm starts from the weight matrix L. Its basic idea is to i to n j The shortest path is the shortest of the following possible paths:

[0121] like <n i ,n j > exists, then the path exists <n i ,n j >(the path does not contain other vertices);

[0122] like <n i ,n 1 >、 <n 1 ,n j > exists, then the path exists <n i ,n 1 ,n j >(the vertex number contained in the path is not greater than 1);

[0123] like <n i ,…,n 2 >、 <n 2 ,…,n j > exists, then there is a path <n i,…,n 2 ,…,n j >(The vertex number contained in the path is not greater than 2)(The vertex number contained in the path is not greater than 2).

[0124] By analogy, n i to n j The shortest path should be the one with the shortest path length among the above paths. After traversing the weight matrix, the shortest path matrix D is obtained.

[0125] The specific steps of S2 are: based on EMTP / ATP simulation analysis and high-voltage shock experiments, the traveling wave head changes ΔU p , traveling wave head rise time t 1 And the traveling wave head amplitude U p Identify the initial traveling wave head. If the setting value satisfies formula (1), it is determined to be the initial traveling wave head of the fault. The head detection of each end detection device is independent, and the head calibration time t can be obtained. The number of elements in t is the number of terminal points of the distribution system, that is, the number of traveling wave monitoring devices.

[0126]

[0127] The specific steps of S3 are:

[0128] 1. Analysis of traveling wave transmission characteristics:

[0129] Figure 2 For a simple T-type distribution network, T 1 , T 2 , T 3 is the traveling wave detection device at the end of the line, and point O is the branch node. 1 When a fault occurs and the fault location is F, the initial fault traveling wave will take the paths l 1 and l 2 , l 3 Incoming T 1 , T 2 , T 3 . Let T 1 , T 2 , T 3 The time when the initial wave head is detected is t 1 ,t 2 ,t 3 , if the time error is not considered, then:

[0130]

[0131] Where: t 0 is the fault start time, l FT1 , l FT2 , l FT3 The fault point F is connected to the detection device T 1 , T2 , T 3 The shortest distance, v 1 、v 2 、v 3 is the wave velocity of the traveling wave when it is transmitted on the corresponding line. When the traveling wave passes through the overhead line and the cable, the wave velocity is quite different. In order to reduce the complexity of the method, the distribution network is normalized according to formula (3) based on the wave velocity of the two.

[0132]

[0133] v ol 、v cable are the empirical values ​​of the speed of traveling waves in overhead lines and cables, respectively. The present invention takes 2.981×10 5 km / s and 1.987×10 5 km / s. L cable is the length of a certain cable section, L eq This section of cable is equivalent to the length of the overhead line. At this time, for the same fault, it can be considered that v 1 =v 2＝ v 3＝ v.

[0134] In fact, the traveling wave will attenuate when it is transmitted in the power line, resulting in the initial mutation waveform of the traveling wave being between the ramp function and the step function. Therefore, the wave head calibration time is a certain point in the mutation process, which is often inconsistent with the initial mutation point. In addition, intercept error, noise, and time synchronization of the device will further affect the accuracy of the wave head calibration time. Therefore, formula (2) is not strictly equal in practice.

[0135] 2. Time deviation model construction:

[0136] From the above analysis, we can know that the traveling wave head calibration time of each detection point is determined by the fault location, traveling wave velocity and fault start time. Define the time deviation y of traveling wave detection device i i for:

[0137]

[0138] It represents the difference between the actual initial wave front arrival time after the fault occurs and the calibrated initial wave front arrival time t at device i i The error between FTi is the shortest distance from the fault point to the detection device i.

[0139] When the time error is not considered, the time deviation has a minimum value of 0. The smaller the time deviation, the more the solved fault location, wave velocity, and fault occurrence time are consistent with the actual values, and the higher the positioning reliability.

[0140] When considering the time error, we have

[0141]

[0142] Δt i The time error of the initial wave head arrival time is calibrated for each end detection device. Considering the computational complexity, the present invention uses Δt i Simplified to Δt. Let t 0 ′=t 0 +Δt represents the wave head calibration time considering the wave head error.

[0143] Therefore, the problem of solving the fault location can be viewed as finding a set of variables (l, v, t 0 ′) to minimize the overall time deviation. In the present invention, the overall time deviation is represented by the mean square error H of the time deviations of all monitoring points, as shown in formula (6).

[0144]

[0145] When H is 0, the shortest distance from the fault point to each detection device is l FTi is the shortest distance to the actual fault, and the fault location is the actual fault location. Therefore, the fault location problem can be expressed as follows.

[0146]

[0147] Where: n is the total number of measuring points, l max is the longest distance between the two end points of the line, t min ,t max are the minimum and maximum values ​​of the arrival time of the initial traveling wave head in the detection device, respectively, max The speed of light is taken as 299.79 km / s in the present invention.

[0148] The specific steps of S4 are as follows: Since the shortest distance from the fault point to each detection device is a function of the fault location, it is necessary to first obtain the shortest distance matrix from the fault point to each detection device during the iterative solution. Any detection device is selected as the reference point, and the node close to the reference point in each branch is the sub-reference point. For any branch, the distance from the sub-reference node to each detection device can be uniquely determined by the shortest distance matrix D. For any fault point, the fault point is disconnected, then there is still a path between some measuring points and the sub-reference point, and there is no path between the other measuring points and the sub-reference point.

[0149] Assume that the fault point F is located in the branch where the sub-reference point j is located and the distance between F and j is d, then the distance from this fault point to each detection device Ti can be obtained by formula (8).

[0150]

[0151] The particle swarm algorithm (PSO) is used for solving the problem. Each particle in the PSO algorithm is a solution in the solution space. It adjusts its flight based on its own flight experience and the flight experience of its companions. The best position experienced by each particle during the flight process is the optimal solution found by the particle itself. The best position experienced by the entire group is the optimal solution currently found by the entire group. The former is called the individual extreme value (pBest), and the latter is called the global extreme value (gBest). In actual operation, the "goodness" of the particle is evaluated by the overall time deviation. Each particle continuously updates itself through the above two extreme values ​​to generate a new generation of the group.

[0152] If the size of the particle group is N, the position of the i-th (i = 1, 2, ..., N) particle can be expressed as X i The "best" position it has ever experienced is recorded as pBest[i], and its speed is recorded as V i Indicates that X i 、V i are defined as three-dimensional variables that characterize the speed of the traveling wave, the fault location, and the initial time of the fault. The index number of the position of the "best" particle in the group is represented by g, so particle i will update its speed and position according to the following formula:

[0153]

[0154] where c 1 , c 2 is a constant, called the learning factor; rand() and Rand() are random numbers on [0, 1], and w is the inertia weight. The formula consists of three parts. The first part is the previous speed of the particle, which describes the current state of the particle; the second part is the cognitive part (cognition modal, indicating the thinking of the particle itself; the third part is the social part (social modal). The three parts together determine the spatial search ability of the particle. The first part plays a role in balancing the ability of global and local searches. The second part enables the particle to have a strong enough global search ability to avoid local minima. The third part reflects the information sharing between particles. Only with the joint action of these three parts can the particle effectively reach the best position. In addition, when the particle constantly adjusts its position according to the speed, it is also subject to the maximum speed V max The limit when V i Exceed V max will be limited to V max The minimum time deviation is solved for each branch, and the fault position corresponding to the minimum value of the entire network is the actual fault position obtained, which is uniquely represented by the branch where it is located and the distance between the fault point and the sub-reference point of the branch where it is located.

[0155] The specific steps of S5 are as follows: since the system time of the line terminal traveling wave detection device is affected by the accuracy of the clock module and the timing mode, the fault traveling wave data actually detected by some line terminals may not be accurate in the time information calibration, which causes a certain synchronization error in the detected fault traveling wave arrival time, resulting in the generation of bad data. At this time, the position of the reference point obtained by the optimization algorithm will not coincide with the fault point, that is, there is a positioning error. In the case of no time synchronization error, the elements in the final time deviation are all 0. If the influence of the time synchronization error is considered, the elements of the time deviation matrix affected by the bad data will be significantly greater than 0, and the remaining normal elements will be equal to 0 or close to 0. To this end, the present invention reduces the influence of bad data by weighting each measuring point. It can be seen from S3 that the size of the time deviation can reflect the error of the positioning result. The contribution of the measuring point with a small error to the overall time deviation should be enhanced. After obtaining the first optimization result, the time deviation matrix Y of each measuring point can be obtained, and the deviation with a larger value in Y is assigned a smaller weight to reduce the influence of bad data. The present invention obtains it by the exponential weight method, that is, formula (9), and the k value in the present invention is 1. Then equation (7) can be modified to equation (10) to solve the fault location.

[0156] w i =e -kyi (9)

[0157]

[0158] After repeating the calculation of S5 1 to 2 times, the final positioning result can be obtained. The final positioning result can be obtained by performing denormalization using formula (10).

[0159]

[0160] Among them, L real is the actual fault distance from the fault point to the branch sub-reference point, L norm is the distance between the fault point and the sub-reference point of the branch obtained in the normalized line, L * For L norm The length of overhead lines included in.

[0161] Considering the wave head error and reducing the impact of the wave head error: The present invention introduces a wave head error correction mechanism and combines it with the weight distribution of different nodes to effectively reduce the positioning deviation caused by the wave head error in the traditional method, significantly improving the accuracy of fault location, especially in complex distributed distribution networks.

[0162] Enhance system robustness: The weight distribution strategy is adopted to make the fault location method more stable when dealing with different wave head errors at each measuring point, reduce the impact of external environment changes on the positioning effect, and improve the robustness of the system.

[0163] In order to further illustrate the beneficial effects of the method of the present invention, a comparative analysis is performed with reference to the protection schemes in the prior art. Figure 3 The topology shown is simulated and verified.

[0164] The branch where the fault point is located is cd, the distance from point c is 200m, the fault type is phase A grounding fault, the transition resistance is 35Ω, and the initial phase angle of the fault is 60°. In the simulation test results: the positioning results when multiple terminal detection devices have time synchronization errors, multiple terminal detection devices have time synchronization errors, all detection devices have detection errors, and the corresponding positioning results in Table 2 are shown in Tables 1, 2 and 3 respectively:

[0165] Table 1

[0166]

[0167] Table 2

[0168]

[0169] Table 3

[0170]

[0171] It can be seen from the results in Tables 1, 2 and 3 above that the existing positioning method has poor applicability when there is an error in the wave head calibration, and the method of the present invention has better protection performance.

[0172] Embodiment 2:

[0173] On the other hand, the present invention also proposes a distribution network fault location system based on wave head error and weight distribution, such as Figure 4 As shown, including:

[0174] The initial unit 201 is used to obtain a weight matrix of a target line of the distribution network, calculate the shortest distance between nodes of the target line based on the weight matrix, and record the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device;

[0175] A model building unit 202 is used to build a time deviation model based on the shortest distance between the nodes and the wave head error, according to the arrival time and the transmission characteristics of the traveling wave;

[0176] The solving unit 203 is used to assign weights to the importance of the traveling wave detection device, modify the time deviation model based on the weight assignment result, and locate the target line fault based on the modified time deviation model.

[0177] Among them, obtaining the weight matrix of the target line of the distribution network includes:

[0178] According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E);

[0179] Among them, V is the vertex set of the graph, and E is the edge set of the graph;

[0180] The edges of the constructed graph are given quantitative indices to generate a weight matrix.

[0181] The shortest distance between nodes of the target line is calculated based on the weight matrix, including:

[0182] After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm;

[0183] The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

[0184] The model building unit 202 is further used to detect the traveling wave head of the target line by a traveling wave detection device to identify whether the target line has a fault. The fault judgment formula is as follows:

[0185]

[0186] Among them, ΔU P is the change of the traveling wave head, t 1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

[0187] Among them, the time deviation model is as follows:

[0188]

[0189] st0≤lFTi≤lmax

[0190] 0.9vmax≤v≤vmax

[0191] tmin-lmax / 0.9vmax≤t0≤tmin+lmax / 0.9vmax

[0192] Among them, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, and t′ 0=t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0193] The target line fault is located based on the modified time deviation model, including:

[0194] Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

[0195] Among them, the modified time deviation model is as follows:

[0196]

[0197] st0≤lFTi≤lmax

[0198] 0.9vmax≤v≤vmax

[0199] tmin-lmax / 0.9vmax≤t0≤tmin+lmax / 0.9vmax

[0200] Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measurement points, yi is the time deviation of the traveling wave detection device i, ti is the initial wave head arrival time calibrated by the detection device at each end, t′ 0 =t0+Δt, Δt is the time error of the initial wave head arrival time calibrated by the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the initial traveling wave head arrival time calibrated in the detection device, and t0 is the fault start time.

[0201] The calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows:

[0202]

[0203] Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

[0204] The present invention significantly improves the accuracy of fault location.

[0205] Embodiment 3:

[0206] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0207] Embodiment 4:

[0208] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0209] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0210] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0211] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0212] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0213] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0214] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A distribution network fault location method based on wave head error and weight distribution, characterized in that: include: Obtaining a weight matrix of a target line of the distribution network, and calculating the shortest distance between nodes of the target line based on the weight matrix, and recording the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device; Based on the shortest distance between the nodes and the wave head error, a time deviation model is constructed according to the arrival time and the transmission characteristics of the traveling wave; The importance of the traveling wave detection device is weighted, the time deviation model is corrected based on the weighted distribution result, and the target line fault is located based on the corrected time deviation model.

2. The method for locating a distribution network fault according to claim 1, characterized in that: The step of obtaining the weight matrix of the target line of the distribution network includes: According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E); Among them, V is the vertex set of the graph, and E is the edge set of the graph; The edges of the constructed graph are given quantitative indices to generate a weight matrix.

3. The method for locating a distribution network fault according to claim 1, characterized in that: The calculating the shortest distance between nodes of the target line based on the weight matrix includes: After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm; The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

4. The method for locating a distribution network fault according to claim 1, characterized in that: The method further includes: detecting the traveling wave head of the target line by a traveling wave detection device to identify whether the target line has a fault, and the fault judgment formula is as follows: Among them, ΔU P is the change of the traveling wave head, t1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

5. The method for locating a distribution network fault according to claim 1, characterized in that: The time deviation model is as follows: st0≤lFTi≤lmax 0.9v max≤v≤vmax t min-l max / 0.9v max≤t0≤t min+l max / 0.9vmax Wherein, H is the overall time deviation, n is the total number of measuring points, yi is the time deviation of traveling wave detection device i, ti is the calibrated initial wave head arrival time of the detection device at each end, t′0=t0+Δt, Δt is the time error of the calibrated initial wave head arrival time of the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the calibrated initial traveling wave head arrival time in the detection device, and t0 is the fault start time.

6. The method for locating a distribution network fault according to claim 1, characterized in that: The method of locating the target line fault based on the modified time deviation model includes: Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

7. The method for locating a distribution network fault according to claim 6, characterized in that: The modified time deviation model is as follows: st0≤lFTi≤lmax 0.9v max≤v≤vmax t min-l max / 0.9v max≤t0≤t min+l max / 0.9vmax Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measuring points, yi is the time deviation of the traveling wave detection device i, ti is the calibrated initial wave head arrival time of the detection device at each end, t′0=t0+Δt, Δt is the time error of the calibrated initial wave head arrival time of the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the calibrated initial traveling wave head arrival time in the detection device, and t0 is the fault start time.

8. The method for locating a distribution network fault according to claim 6, characterized in that: The calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows: Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

9. A distribution network fault location system based on wave head error and weight distribution, characterized in that: include: An initial unit, used to obtain a weight matrix of a target line of the distribution network, calculate the shortest distance between nodes of the target line based on the weight matrix, and record the arrival time of the initial traveling wave after a fault occurs in the target line based on a traveling wave detection device; A model building unit, configured to build a time deviation model based on the shortest distance between the nodes and the wave head error, according to the arrival time and the transmission characteristics of the traveling wave; The solving unit is used to assign weights to the importance of the traveling wave detection device, modify the time deviation model based on the weight assignment result, and locate the target line fault based on the modified time deviation model.

10. The distribution network fault location system according to claim 9, characterized in that: The step of obtaining the weight matrix of the target line of the distribution network includes: According to the topological structure of the target line of the distribution network, the busbar / feeder in the target line is used as the node, the overhead line / cable between the busbar / feeder is used as the edge, and the line length is used as the edge feature to construct a graph G = (V, E); Among them, V is the vertex set of the graph, and E is the edge set of the graph; The edges of the constructed graph are given quantitative indices to generate a weight matrix.

11. The distribution network fault location system according to claim 9, characterized in that: The calculating the shortest distance between nodes of the target line based on the weight matrix includes: After obtaining the weight matrix, the shortest distance between nodes of the target line is calculated based on the weight matrix through the Floyd algorithm; The shortest distance is the distance of the path with the shortest length among multiple possible paths between two nodes.

12. The distribution network fault location system according to claim 9, characterized in that: The model building unit is also used to: detect the traveling wave head of the target line through a traveling wave detection device to identify whether the target line has a fault. The fault judgment formula is as follows: Among them, ΔU P is the change of the traveling wave head, t1 is the rising time of the traveling wave head, U P is the traveling wave head amplitude.

13. The distribution network fault location system according to claim 9, characterized in that: The time deviation model is as follows: st0≤lFTi≤lmax 0.9v max≤v≤vmax t min-l max / 0.9v max≤t0≤t min+l max / 0.9vmax Wherein, H is the overall time deviation, n is the total number of measuring points, yi is the time deviation of traveling wave detection device i, ti is the calibrated initial wave head arrival time of the detection device at each end, t′0=t0+Δt, Δt is the time error of the calibrated initial wave head arrival time of the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the calibrated initial traveling wave head arrival time in the detection device, and t0 is the fault start time.

14. The distribution network fault location system according to claim 9, characterized in that: The method of locating the target line fault based on the modified time deviation model includes: Obtain the distance lFTi from the fault point to each detection device, and based on the particle swarm algorithm, solve the modified time deviation model according to the distance lFTi from the fault point to each detection device to obtain the solution as the result of locating the fault of the target line.

15. The distribution network fault location system according to claim 14, characterized in that: The modified time deviation model is as follows: st0≤lFTi≤lmax 0.9v max≤v≤vmax t min-l max / 0.9v max≤t0≤t min+l max / 0.9vmax Where wi = e -kyi , k is the weight sensitivity coefficient, H is the overall time deviation, n is the total number of measuring points, yi is the time deviation of the traveling wave detection device i, ti is the calibrated initial wave head arrival time of the detection device at each end, t′0=t0+Δt, Δt is the time error of the calibrated initial wave head arrival time of the detection device at each end, lFTi is the shortest distance from the fault point to each detection device, v is the wave velocity, lmax is the longest distance between the two end points of the line, vmax is the speed of light, tmin is the minimum value of the calibrated initial traveling wave head arrival time in the detection device, and t0 is the fault start time.

16. The distribution network fault location system according to claim 14, characterized in that: The calculation formula for obtaining the distance lFTi from the fault point to each detection device is as follows: Wherein, d is the distance between the fault point and the reference point when the fault point is located in the branch where the sub-reference point j is located, Ti is the i-th traveling wave detection device, and lij is the distance between Ti and j.

17. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 8 is implemented.

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