Fault location positioning method and device based on multiple acquisition devices, equipment and medium

Abstract

The invention relates to the technical field of fault positioning, and discloses a fault position positioning method and device based on multiple acquisition devices, equipment and a medium, and the method comprises the steps: obtaining a plurality of device positions of the acquisition devices of a plurality of traveling wave signals, the time difference of arrival of the traveling wave signals acquired by every two acquisition devices, and the propagation speed of the traveling wave signals; a plurality of hypersphere equations are established according to the positions of every two devices, the time difference of arrival and the propagation speed, and the hypersphere equations are used for reflecting the difference value relation of the distances between the positions of every two devices and the positions of the fault points corresponding to the traveling wave signals; and using a preset particle swarm optimization algorithm and a hyperspherical equation to carry out fault point position positioning processing to obtain an optimal fault point position. Through the above mode, a plurality of acquisition devices can be adopted to capture traveling wave signals, then the hyperspherical equation is established, and finally, the particle swarm optimization algorithm and the hyperspherical equation are used to carry out fault point position positioning processing, so that the optimal fault point position is obtained, and the fault positioning precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and in particular to a fault location method and device, equipment and medium based on multiple acquisition devices. Background Art

[0002] Hidden faults (such as tree obstructions, line room faults, insulator breakdowns, and lightning arrester breakdowns) often occur at locations on overhead power lines that are difficult to directly observe. While traditional traveling wave fault location methods can initially detect faults, they suffer from low accuracy due to the signal's weakness and the fact that propagation is affected by various factors. The propagation speed and waveform characteristics of fault signals vary under different line conditions, making it difficult to accurately combine data collected at different times and locations, thus affecting fault location accuracy. Summary of the Invention

[0003] The main purpose of the present invention is to provide a fault location method and device based on multiple acquisition devices, equipment and medium, which can solve the problem of low fault location accuracy in the prior art.

[0004] To achieve the above objectives, the present invention provides a first aspect of a fault location method based on multiple acquisition devices, the method comprising: Acquiring multiple device positions of multiple traveling wave signal acquisition devices, arrival time differences between traveling wave signals acquired by each of the acquisition devices, and propagation speeds of the traveling wave signals; Establishing multiple hypersphere equations based on the positions of the two devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between the positions of the two devices and the fault point positions corresponding to the traveling wave signal; The preset particle swarm optimization algorithm and the hypersphere equation are used to locate the fault point to obtain the optimal fault point location.

[0005] To achieve the above-mentioned object, the second aspect of the present invention provides a fault location device based on multiple acquisition devices, the device comprising: Data acquisition module: used to acquire multiple device positions of multiple collection devices of traveling wave signals, the arrival time difference between the traveling wave signals collected by each of the collection devices, and the propagation speed of the traveling wave signals; An equation building module is used to build multiple hypersphere equations based on the positions of the two devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between the positions of the two devices and the fault point positions corresponding to the traveling wave signals; Positioning module: used to perform fault point location processing using a preset particle swarm optimization algorithm and the hypersphere equation to obtain the optimal fault point location.

[0006] To achieve the above-mentioned purpose, the third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method shown in the first aspect.

[0007] To achieve the above-mentioned purpose, the fourth aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method shown in the first aspect.

[0008] The embodiments of the present invention have the following beneficial effects: The present invention provides a fault location method based on multiple acquisition devices. The method includes: obtaining multiple device positions of multiple traveling wave signal acquisition devices, the arrival time difference of the traveling wave signals collected by each of the acquisition devices, and the propagation speed of the traveling wave signals; establishing multiple hypersphere equations based on the device positions, arrival time differences, and propagation speeds; the hypersphere equations are used to reflect the difference in distance between each of the device positions and the fault point positions corresponding to the traveling wave signals; and locating the fault point using a preset particle swarm optimization algorithm and the hypersphere equations to obtain the optimal fault point location. Through the above method, multiple acquisition devices can be used to capture traveling wave signals, then the hypersphere equations can be established. Finally, the particle swarm optimization algorithm and the hypersphere equations are used to locate the fault point to obtain the optimal fault point location, thereby improving the accuracy of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] in: Figure 1 This is a flow chart of a fault location method based on multiple acquisition devices in an embodiment of the present invention; Figure 2 This is a structural block diagram of a fault location device based on multiple acquisition devices in an embodiment of the present invention; Figure 3 4 is a structural block diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0012] See also Figure 1 , Figure 1 This is a flow chart of a fault location method based on multiple acquisition devices in an embodiment of the present invention. Figure 1 The fault location location method based on multiple acquisition devices is applied to a fault location location system based on multiple acquisition devices. The fault location location system based on multiple acquisition devices includes a terminal and a server. The terminal and the server are connected via a network. The terminal can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers. The method can be applied to both terminals and servers. This embodiment takes the application to the terminal as an example. Figure 1 The method shown includes the following steps: 101. Acquire multiple device positions of multiple traveling wave signal acquisition devices, arrival time differences between traveling wave signals acquired by each of the acquisition devices, and propagation speeds of the traveling wave signals; It should be noted that the present application uses multiple acquisition devices to collect traveling wave signals, wherein the acquisition devices may be sensors for collecting traveling wave signals, wherein the acquisition devices are pre-installed at various nodes of the transmission line, and the installation locations are recorded as the device locations. To reduce positioning errors, multiple acquisition devices are used to capture traveling wave signals. Sensors are deployed at multiple nodes (such as substations, branch points, and near fault points) to collect electromagnetic transient signals in real time, reducing subsequent positioning errors. To locate the fault point, the fault location is also determined by combining the propagation data of the traveling wave signals. Faults include, but are not limited to, various hidden faults. This propagation data includes, but is not limited to, the arrival time of the traveling wave signals and propagation characteristics, including, but not limited to, propagation speed.

[0013] For example, a multi-sensor network is deployed and data collected in advance. Specifically, high-precision sensors are installed at multiple key locations along overhead power lines (such as substations, branch points, and potential fault zones) to collect traveling wave signals in real time. Each sensor records the signal's arrival time and propagation characteristics.

[0014] Among them, sensor layout: the selected sensor should have high frequency response capability (for example, the frequency band of 10kHz~20kHz) and be able to accurately measure the arrival time of the signal.

[0015] Among them, signal acquisition: each sensor records the electromagnetic transient waveform in the power system in real time, and the signal acquisition system must have high synchronization to ensure the accuracy of the time difference.

[0016] For example, the signal propagation characteristic analysis may be performed by analyzing the propagation characteristics of the traveling wave signal in the power line and inferring the location of the fault point in combination with the physical properties of the line.

[0017] Propagation velocity estimation: The propagation velocity of traveling wave signals may vary along different line types, so an estimate must be made based on physical parameters such as line resistance, conductor type, and environmental factors. The propagation velocity of a specific line can be estimated through calibration experiments or from historical data.

[0018] Signal arrival time difference analysis involves recording the arrival times of the same traveling wave signal at multiple sensors and calculating the time difference. This time difference, combined with the known line propagation speed, allows for preliminary fault location.

[0019] Furthermore, in order to more accurately determine the propagation speed of the traveling wave signal, the present application obtains the propagation speed in the following manner: obtaining the physical parameters of the transmission line; and determining the propagation speed based on the physical parameters of the transmission line and a preset electromagnetic wave propagation model.

[0020] It should be noted that the propagation speed of traveling wave signals varies in different types of power lines, mainly affected by the following factors: Line resistance: The resistance of the line affects the propagation speed of electromagnetic waves in the conductor. The line with greater resistance has a slower propagation speed.

[0021] Wire type: Different wire materials (such as copper, aluminum, and steel-core aluminum stranded wire) have different electrical conductivity and electromagnetic wave propagation characteristics, which affect the signal propagation speed.

[0022] Line installation method: The line installation height, intersection points, tower type, etc. may affect the propagation path and speed of the traveling wave signal.

[0023] Environmental factors: Environmental factors such as temperature, humidity, and wind speed will also affect the propagation speed. Especially in high-voltage and ultra-high-voltage lines, the impact of environmental factors on the propagation speed of electromagnetic waves is more significant.

[0024] Model derivation: Based on the physical parameters of the line (such as resistance, conductor type, and grounding conditions), electromagnetic wave propagation models (such as the propagation constant model or the wave impedance model) are used to calculate the propagation velocity. For example, for long lines, a transmission line model can be used to infer the propagation characteristics of traveling waves. These models can provide a rough estimate of the propagation velocity, which can be used for subsequent fault location. Specifically, the propagation constant model estimates propagation velocity by describing the characteristics of electromagnetic wave transmission in power lines. The propagation constant, typically expressed as a complex number, incorporates information about signal attenuation and phase shift. It can be used to describe the speed and attenuation characteristics of signal propagation along the line.

[0025] The expression of the propagation constant γ is: ; in, α: Attenuation constant, indicating the attenuation of signal strength.

[0026] β: Phase constant, which indicates the change in signal phase.

[0027] v: Propagation velocity, which can be derived from the propagation constant γ and the physical characteristics of the line. For low-loss lines, the propagation velocity is related to the phase constant β ; Where ω = 2πf is the angular frequency of the signal, and f is the frequency of the signal.

[0028] The wave impedance model (Characteristic Impedance Model) considers the characteristic impedance of the circuit line, which describes the interaction between the traveling wave and the line during transmission. For overhead power lines, its wave impedance Z0 is usually determined by the line geometry and the electrical characteristics of the conductor.

[0029] The relationship between wave impedance and line propagation velocity can be obtained by the following formula:

[0030] in: L: Inductance per unit length (H / m), which depends on the shape and physical properties of the wire; C: Capacitance per unit length (F / m), which is related to the spacing between the conductors and the dielectric constant of the circumference.

[0031] By accurately measuring or estimating the inductance and capacitance values, the speed of travel waves can be calculated.

[0032] The transmission line model (also known as the T-circuit model) is a classic analysis method for long-distance power lines. The transmission line model treats the power line as a network composed of distributed resistance, inductance, capacitance, and admittance, and is used to describe the propagation characteristics of electromagnetic waves in the line. For a simple mismatched line (that is, without considering factors such as complex electromagnetic wave reflections), the propagation velocity v of the transmission line can be derived from the following relationship:

[0033] in: L: is the inductance per unit length (H / m); C: is the capacitance per unit length (F / m).

[0034] The transmission line model assumes that the line has uniform resistance and inductance, and is therefore suitable for calculating propagation velocity for most power transmission lines. It is particularly suitable for long-distance, high-voltage transmission lines.

[0035] Influence of conductor type: When designing parameters based on actual application, if the wire material, radius, spacing, etc. are used, the line resistance R, inductance L, capacitance C, and ground capacitance (capacitance of the conductor to the ground) and other key physical parameters can be calculated.

[0036] These parameters are key factors affecting the propagation speed.

[0037] Influence of measurement conditions Wire type: Common wire materials include aluminum, copper, and steel-core aluminum stranded wire. The difference in electrical conductivity of different materials will affect the propagation speed.

[0038] Wire Path: The effect of spacing between conductors on the capacitance of overhead power lines The calculation is crucial.

[0039] Grounding point conditions: Whether the line is grounded and the grounding resistance will also affect the signal propagation characteristics. These factors are especially important in high-compression circuits.

[0040] Measurement and calibration experiments Due to the complexity of power lines, the theoretically calculated propagation speed may have certain deviations.

[0041] Therefore, conducting calibration experiments is an important step to improve the accuracy of propagation velocity estimation. Commonly used calibration methods include: Experimental measurement method: Experiments are conducted on actual known power lines. Sensors with high-precision time steps are used to measure the response time of the signal from the fault point to each sensor, and then the propagation speed is calculated.

[0042] Historical data method: The propagation speed is inferred by combining the historical fault data with the propagation time difference of the running signal and the known parameters of the line.

[0043] Combining the propagation constant model with the transmission line model: By combining the propagation constant model, the wave impedance model, and the transmission line model, a more accurate propagation velocity estimate can be obtained. The specific steps are as follows: 1.1 Determine the circuit's resistance, wire type, inductance, and capacitance.

[0044] 1.2 Select an appropriate propagation model: Based on the characteristics of the line, select one of the propagation constant model, wave impedance model or transmission line model.

[0045] 1.3 Calculate the propagation velocity: Use the formula of the above model and combine it with the physical parameters of the line to calculate the propagation velocity v.

[0046] 1.4 Correction through calibration experiments: If possible, the time difference of signal propagation can be measured experimentally to correct the propagation speed estimate of the model to ensure its accuracy.

[0047] Perform the following steps to locate the fault: 2.1 Collect multi-point sensor data: The arrival time of signals recorded by multiple sensors is used to calculate the time difference.

[0048] 2.2 Positioning based on propagation speed and time difference: The location of the fault point is calculated by knowing the propagation speed and the time difference recorded by the sensor.

[0049] 2.3 The time it takes for the traveling wave signal to propagate from the fault point to different sensors is different. The time difference of arrival (TDOA) measured by the sensor is the key to fault location.

[0050] Therefore, different physical parameters of the transmission line will affect the propagation speed of the traveling wave signal. Therefore, the physical parameters of the transmission line are obtained; and the propagation speed is determined based on the physical parameters of the transmission line and the preset electromagnetic wave propagation model.

[0051] 102. Establish multiple hypersphere equations based on the positions of each of the devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between each of the positions of the devices and the fault point positions corresponding to the traveling wave signal; Furthermore, a hypersphere equation is constructed using the device locations, arrival time differences, and propagation speed. This hypersphere equation is based on distance relationships and reflects the distance difference between each of the device locations and the fault location corresponding to the traveling wave signal. The same traveling wave signal can be collected by multiple sensors, so a hypersphere equation can be constructed for every two sensors. Since the fault location is three-dimensional, at least three hypersphere equations are required.

[0052] In one possible implementation, the hypersphere equation includes the following mathematical expression:

[0053] Where, (x i ,y i ,z i ), (x j ,y j ,z j ) are the device positions of collection device i and collection device j respectively; is the arrival time difference of the traveling waves recorded by acquisition device i and acquisition device j; is the traveling wave propagation velocity, and (x, y, z) is the fault point location corresponding to the traveling wave signal.

[0054] It should be noted that the following steps can be used to preliminarily locate the fault point based on the time difference: Constructing a hypersphere: Based on the time difference, a hypothetical hypersphere (hypercircle) can be constructed. In three-dimensional space, the time difference between multiple sensors is equivalent to the difference in distance between the fault point and each sensor. By constructing these hyperspheres and solving for their intersection, the approximate location of the fault point can be obtained.

[0055] In a multi-sensor positioning system, the arrival time of the traveling wave signal recorded by each sensor will have a certain time difference (i.e., Δ ), these time differences reflect the relative time delays of signals propagating from the fault point to different sensors.

[0056] In three-dimensional space, assume that the distance between the fault point P and sensor i is , then the arrival time difference of each sensor Δ It can be expressed as: Δ ; in: v is the propagation speed of the signal.

[0057] and is the distance from the fault point to the two sensors i and j.

[0058] Therefore, a relationship can be obtained about the distance difference between the fault point and multiple sensors. This relationship essentially describes the position of a hypersphere in space.

[0059] Hypersphere positioning principle The construction of a hypersphere is a key step in calculating the fault location based on the distance difference obtained by time difference calculation. and ,Through time difference calculation, we can obtain the equation of a hyperplane (or hypercircle), and then locate the fault point.

[0060] Single hypersphere equation considering two sensors and , the distance difference between the fault point and the two sensors is , the propagation speed is , then the distance difference between the two sensors can be expressed as:

[0061] According to the sensor and Known location of and , the location of the fault point P is , then:

[0062] This is actually a , and The equation represents the distance difference between the fault point and the two sensors. This equation forms a "hypersphere".

[0063] In practical applications, multi-sensor hypersphere systems usually have multiple sensors Therefore, a similar distance equation can be established for each pair of sensors, and positioning can be performed through the synergy of multiple sensors.

[0064] For the case of multiple sensors, Sensors, sensors and The time difference between , the propagation speed is , then for any two sensors and , we can get a distance difference equation similar to the above: in: (x i ,y i ,z i ), (x j ,y j ,zj ) are the known coordinates of sensors i, j; is the arrival time difference of the traveling waves recorded by the two sensors; is the speed of traveling wave propagation (known and stable).

[0065] In three-dimensional space, the coordinates of the fault point ( ) has 3 unknowns, so at least 3 independent hypersphere equations (provided by 3 pairs (6) of sensors) are required to solve them together to find a unique solution.

[0066] For example, if there are 3 sensors , the following three sets of equations can be established: The equations of A and B are:

[0067] The equations of A and C are:

[0068] The equations of B and C are:

[0069] After these three equations are combined, algebraic operations (such as square elimination and substitution) can be used to solve for a unique (x, y, z) solution, which is the location of the fault. Each hypersphere equation corresponds to a "hyperboloid" in three-dimensional space (the difference between the two spherical shells is a constant). Ideally, the intersection of multiple hyperboloids has only one common point, which is the fault point.

[0070] 103. Perform fault point location processing using a preset particle swarm optimization algorithm and the hypersphere equation to obtain an optimal fault point location.

[0071] Furthermore, due to errors in actual situations, such as sensor acquisition errors, directly solving the equation will result in many approximate positions that are infinitely close to the actual fault point. Therefore, the particle swarm optimization algorithm is used to solve the hypersphere equation to obtain the best solution and the optimal fault point location.

[0072] Particle Swarm Optimization (PSO): The particle swarm optimization algorithm can be used to optimize the calculation of the fault point location within a given time difference range and iteratively calculate the minimum error location.

[0073] Specifically, in the PSO algorithm, the solution space of the optimization problem is assumed to be a multidimensional space, and each potential solution is a "particle." Each particle can be thought of as a point with a position and velocity, representing the current solution. The particle swarm continuously updates its position and velocity, ultimately finding the global optimal solution. In an ideal situation (without errors), the solution to the simultaneous hypersphere equations is unique, indicating the location of the fault point. However, in practice, errors can lead to multiple approximate solutions in the solution space (e.g., multiple points close to the true location). These approximate solutions may be distributed within a small range around the true solution.

[0074] Among them, the role of multiple particles: solution space under coverage error: The multiple particles in the PSO system act like a "search net" in the solution space. Each particle starts from a different initial position and evaluates its match (error) with all hypersphere equations. Through iterative updates, the particles gradually converge toward the area with the smallest error (the actual fault point), eventually converging to the same location (the global optimal solution).

[0075] For example: Initially, particle 1 is at position A (large error), particle 2 is at position B (small error), and particle 3 is at position C (medium error). During the iteration, the particle adjusts its speed and position according to its own historical best (pbest) and global best (gbest, such as the position B of particle 2); Eventually, all particles will move toward the position with the minimum error (the actual fault point) and converge to the unique solution; The particle's "position" and "speed" parameters are defined as follows: In the fault location problem, the solution space is a three-dimensional coordinate space (corresponding to the location of the fault point (x, y, z)), so: 1. The "position" of the particle: The particle's position x i =(x i ,y i ,z i ) represents a candidate fault point location (i.e., the coordinates of the fault point currently guessed by the algorithm), and each particle represents a "hypothetical fault point" whose position must satisfy the constraints of the hypersphere equation (i.e., the distance difference from each sensor is close to ).

[0076] 2. Particle "speed": The particle's velocity v i =(v ix ,v iy ,v iz ) represents the direction and speed of the particle's movement in the solution space (unit: coordinate unit / iteration step). The speed controls how the particle "explores" the solution space. The greater the speed, the faster the particle moves and the wider the area may be covered; the smaller the speed, the slower the particle moves and the more focused it is on local optimization.

[0077] In one possible implementation, step 103 includes steps A01 to A07: A01. Based on the actual line space of the transmission line as a constraint, the particle position and particle velocity of each particle in the particle swarm are randomly initialized, and the particle position represents the candidate fault point location; It can be understood that the particle swarm optimization algorithm is initialized with the actual line space of the transmission line as a constraint, and the particle position and particle velocity of each particle in the particle swarm are randomly initialized, wherein the particle position represents the candidate fault point position, and multiple candidate fault point positions with the actual line space as a constraint can be randomly generated.

[0078] The method for determining the initial value is as follows: To ensure that the PSO algorithm effectively searches the solution space (avoiding falling into local optimality), the initial position and velocity of the particles need to be randomly generated and cover the possible fault area. The specific rules are as follows: 1. Determination of initial position: According to the actual layout of the power line, set the search range of the particle initial position. For example, if the total length of the line is ,but The coordinates can be constrained to [0,L] (assuming the line is along Axis laying), the y,z coordinates are set in a reasonable range according to the tower height, conductor spacing, etc. (such as y∈[10m,30m],z∈[line lateral coverage range]).

[0079] Randomly generate initial positions within the search range to ensure uniform particle distribution. ~U(0,L) (uniform distribution), ~U(y min ,y max ), ~U(z min ,z max ).

[0080] 2. Determination of initial velocity: The initial velocity is usually set to a small value (such as ~U(-v max ,v max ), v max For the upper limit of speed, for example, take the search range ,To avoid the particles moving too fast at the beginning and causing "skipping" of the real solution, random generation, direction and size are random, ensuring the diversity of the particles' initial exploration.

[0081] A02. Obtaining the current fitness of the particle using the particle position and the hypersphere equation; Furthermore, in order to select the optimal fault point location, it is necessary to use the particle position and the hypersphere equation to obtain the current fitness of the particle.

[0082] Exemplarily, step A02 may include steps B01 to B02: B01. Using the particle position and the hypersphere equation, obtain a matching degree between the particle position and the hypersphere equation; B02. Perform summation processing using the multiple matching degrees to obtain the current fitness of the particle.

[0083] The matching degree reflects the degree of match between the particle position and the hypersphere equation. Multiple hypersphere equations yield multiple matching degrees. A closer match indicates closer fulfillment of the hypersphere equation and a smaller error. Furthermore, the matching degrees are fused to obtain the particle's current fitness. This fusion can be performed using summation, weighted summation, or other methods, which are not limited here. The current fitness is obtained.

[0084] Among them, the hypersphere equation can evaluate the quality of particles. The position and velocity of the particles ultimately serve the constraint matching of the hypersphere equation. The specific relationship is as follows: Evaluate the match between the position and the hypersphere equation: The position of the particle (x i ,y i ,z i ) needs to be substituted into all hypersphere equations to calculate the degree of match (i.e., the error magnitude) with the time difference constraint of each sensor. For example, for a sensor pair (j, k), the degree of match is calculated using the following mathematical expression: ; Where, is the equation of particle i and the hypersphere jk Matching degree, ( , , ) is the particle position of particle i, ( , , ) is the device position of the collection device j, ( , , ) is the device position of the acquisition device k, v is the traveling wave propagation speed, is the arrival time difference of the traveling wave recorded by acquisition device j and acquisition device k. jk It refers to establishing a hypersphere equation using acquisition device j and acquisition device k.

[0085] The sum of the errors of all sensor pairs is the "fitness" of the particle (the smaller the error, the higher the fitness).

[0086] A03. If the current fitness is better than the historical fitness of the particle, updating the individual optimal position of the particle to the particle position of the particle; A04. Select the best individual position with the largest fitness from the best individual positions of all particles as the global best position, and increase the current number of iterations by 1. A05. Determine whether the current number of iterations and / or the global optimal position meet a preset iteration termination condition; A06. If the current number of iterations and / or the global optimal position do not satisfy a preset iteration termination condition, the particle velocity and particle position of each particle are updated using the global optimal position and the individual optimal position to obtain an updated particle velocity and particle position of each particle; and the process returns to the step of obtaining the current fitness using the particle position and the hypersphere equation. Among them, the velocity guides the particles to move towards the optimal solution: The particle's speed is dynamically adjusted based on its "personal best position (pbest)" and "global best position (gbest)": gbest ; : Inertia weight (controls the tendency of the particle to maintain its current speed); : Learning factor (controls the degree to which the particle approaches its own historical optimum and the global optimum); : Random number (increases randomness of exploration).

[0087] Through velocity updates, the particle will gradually move to the position with the smallest error (i.e., the fault point that best matches the hypersphere constraint), and eventually converge to the global optimal solution.

[0088] Among them, the update formula of PSO is as follows: 1) The update of particle velocity includes the following mathematical expression: ; Where, is the i-th particle at the next moment The particle velocity; and They are Particles at the current moment The particle velocity and particle position; It is The individual best position of each particle; is the global optimal position; is the inertia weight; and is the learning factor; and is a random number.

[0089] in, and is the learning factor, usually set to 2, which controls the degree to which the particle approaches the personal optimal position and the global optimal position respectively. and It is a random number between [0,1], used to introduce randomness and enhance search capabilities. It is the inertia weight, which is used to control the speed attenuation of the particle. Usually a value between 0.5 and 1 is selected.

[0090] 2) The update of particle position includes the following mathematical expression: ; Where, For the The particle at the next moment The particle positions where: and They are Particles at time speed and position.

[0091] in, It is The historical best position of a particle (i.e., its personal best position).

[0092] is the best historical position of the entire particle swarm (i.e., the global best position, gbest is the position with the highest fitness among the "best historical positions (pbest)" of all particles in the particle swarm (i.e., the point with the best match to the hypersphere equation constraint). Specifically for the fault location problem: The pbest of each particle is the "position with the minimum error" found during the particle's own iteration (the error refers to the deviation of the position from all hypersphere equation constraints); gbest is the position with the smallest error among all particles pbest, representing the current optimal solution of the particle swarm; The PSO iteration termination is usually determined by the following multiple conditions, and it stops if any of the conditions is met: 1. Reach the maximum number of iterations Even if the particles are not fully converged, the preset It can ensure that the algorithm outputs results within a reasonable time (such as the real-time requirement of fault location).

[0093] 2. The error converges to the threshold (fitness reaches the standard). When the error of the particle's global best position (gbest) is less than the preset threshold When , it is considered that a sufficiently accurate solution has been found. For example, if the positioning accuracy requirement is m, then set Meters, when the error of gbest (distance from the actual fault point) Terminate at 10:00 am.

[0094] 3. Particle swarm "stagnation" (no significant optimization). If the particle's global best position (gbest) remains unchanged (or changes less than the minimum value) over several consecutive iterations, the swarm has fallen into a local optimum or converged, and can be terminated early. For example, if "gbest has not been updated for 10 consecutive iterations," the swarm will terminate.

[0095] A07. If the current number of iterations and / or the global optimal position meet a preset iteration termination condition, the global optimal position is output, and the optimal fault point position includes the global optimal position.

[0096] Finally, if the current number of iterations and / or the global optimal position meets the preset iteration termination condition and the global optimal position is obtained, the global optimal position is output, and the optimal fault point position includes the global optimal position.

[0097] For example, after data collection, data fusion and spatiotemporal feature joint optimization can be performed. Specifically, data fusion technology is used to comprehensively process data from different sensors, and spatiotemporal information and propagation characteristics are used to further optimize fault location.

[0098] Among them, the data fusion method: adopts multi-sensor data fusion method, such as Kalman Filter, to correct the data collected by the sensor in real time and eliminate the impact of noise on positioning accuracy.

[0099] Then, an optimization algorithm is used: combining the signal propagation time difference and using global optimization methods such as particle swarm optimization (PSO) or genetic algorithm (GA) to accurately calculate the fault point location. The algorithm gradually optimizes the positioning results to minimize the prediction error.

[0100] Error correction and optimization are possible. In actual applications, due to environmental factors, line conditions, etc., simple time difference analysis may lead to positioning errors. Therefore, error correction methods are required: Error modeling: Based on historical fault location error data, an error model is established to correct the actual fault point location.

[0101] Multi-sensor data fusion: Use methods such as Kalman filtering and particle swarm optimization to fuse data from multiple sensors, correct the local errors of each sensor, and ultimately obtain more accurate fault location results.

[0102] Specifically: Fault location errors mainly come from the following aspects: Changes in signal propagation speed: Factors such as line type, electrical characteristics (such as resistance, wire type), climatic conditions, and line aging may affect the propagation speed of traveling wave signals.

[0103] Sensor measurement error: The accuracy of the sensor itself, synchronization error, noise, etc. may affect the calculation of the time difference.

[0104] Noise and interference: Electromagnetic interference (EMI) may exist in the power system, making the signals recorded by the sensor inaccurate.

[0105] Environmental factors: such as humidity and temperature may cause signal attenuation or errors.

[0106] Error model establishment In order to correct these errors, an error model can be constructed by analyzing historical fault location error data.

[0107] Common error models include: Linear error model: Based on historical data, it is assumed that the error is linearly related to the positioning error of the sensor or environmental factors. For example:

[0108] in, is the positioning error, is the time difference error, and is the constant to be estimated (which can be obtained by least squares fitting).

[0109] Nonlinear error model: For complex environmental changes, nonlinear models (such as high-order polynomials, curve fitting, etc.) can be used to better represent the variation pattern of errors.

[0110] Statistical error model: Statistical methods can be used to analyze error distribution and build a more accurate error model. The key to error modeling is to use historical fault data to extract error characteristics, thereby improving positioning accuracy.

[0111] Because each sensor has a certain local error when locating a fault, relying solely on a single sensor may result in inaccurate positioning results. Multi-sensor data fusion can combine the results of multiple sensors to reduce the overall error.

[0112] Kalman Filter is an optimal estimation method based on a recursive algorithm. It is suitable for estimating the state of a dynamic system. Especially in multi-sensor data fusion, Kalman Filter can effectively reduce noise and measurement errors. Its basic steps are as follows: 1. Initialization: Set the initial state (estimated location of the fault point ) and the initial error covariance matrix .

[0113] 2. Prediction: Predict the current state based on the estimated state at the previous moment and the system model:

[0114] in: is the state transition matrix, is the control input matrix, Is the control input. 3. Update: When new sensor data Upon arrival, update the state estimate:

[0115]

[0116]

[0117] in, is the observation matrix, is the covariance of the sensor noise, is the Kalman gain, is the estimated state at the current moment, is the error covariance. The advantage of Kalman filtering is that it can process sensor data in real time and dynamically update the estimated value of the fault point location while suppressing noise interference; 1. Sensor Data Scope: All sensor data, or new data input by time step, is processed sequentially (any or part of the sensor data). If sensor data arrives asynchronously (e.g., due to dispersed sensors, the arrival times of the signals are not synchronized), the Kalman filter can process the newly arrived sensor data one by one in chronological order. For example, the measurement value of sensor A arrives first, triggering the first update; the measurement value of sensor B arrives later, triggering the second update.

[0118] The sensor data is not the traveling wave signal itself (such as the original waveform), but the sensor's quantitative measurement of the traveling wave signal, including: The arrival time of the traveling wave signal (key data): The core function of the sensor is to record the precise time of arrival of the traveling wave signal , Kalman filter needs these time values to calculate the time difference , which is then related to the distance difference between the fault point and the sensor . .

[0119] Assuming that three sensors A, B, and C are deployed, the "update" steps of the Kalman filter may be as follows: 1) Initial state: The initial fault point position is obtained by PSO algorithm or hypersphere equation. , initial error covariance ; 2) Sensors Data arrival (time ): Calculate the time difference between A and a known reference point (such as a substation) , get the height difference ; Kalman filter update: Use the distance difference to correct the initial state and get a new estimated position , error covariance .

[0120] 3) Sensor B data arrives (time ): Calculate the time difference between B and A , get the height difference ; Update the status again and get , error covariance (Compare Smaller, higher precision).

[0121] 4) Sensor C data arrival (time ): Calculate the time difference between C and B , get the height difference ; Finally, the updated status is (close to the actual fault location), error covariance Further reduce.

[0122] Among them, in the multi-sensor positioning problem, PSO can be used for global optimization, while Kalman filtering can be used for real-time update of local data. The combination of the two can provide different levels of optimization at different stages: PSO performs global optimization: The particle swarm optimization algorithm is used to find the global optimal solution (the approximate location of the fault point) and correct the error based on the data from multiple sensors.

[0123] Kalman filtering performs local corrections: When sensor data arrives, the Kalman filter updates the current fault point location based on the new data, improving real-time positioning accuracy.

[0124] By combining these two methods, optimization can be performed at the global and local levels, effectively improving the accuracy of fault location.

[0125] Error correction process: Initial positioning: Use time difference analysis and preliminary multi-sensor positioning results to obtain a rough fault point location.

[0126] Error modeling: Build error models based on historical data and analyze potential sources of errors.

[0127] Data fusion: Data from multiple sensors are fused through methods such as Kalman filtering, weighted averaging, and particle swarm optimization to correct local errors of the sensors.

[0128] Refined optimization: By combining error models and multi-sensor data, the accuracy of fault location is further improved through optimization algorithms.

[0129] Real-time update: When new sensor data arrives, the positioning results are updated in real time using data fusion methods to maintain high-precision fault location.

[0130] This paper proposes a fault location method based on multiple acquisition devices. This method is equivalent to a method for accurately locating hidden faults based on multi-sensor data fusion and signal propagation characteristic analysis. The core is as follows: Multi-sensor data fusion: By deploying sensors at multiple nodes (such as substations, branch points, and near fault points), electromagnetic transient signals are collected in real time, and multi-point data is synthesized through data fusion methods to improve positioning accuracy. Signal propagation characteristic analysis: The location of the fault point is estimated by analyzing the propagation speed, propagation time difference, and line characteristics (such as resistance, conductor type, grounding conditions, etc.) of the traveling wave signal in the power line. Joint optimization positioning of spatiotemporal features: Particle swarm optimization is used to accurately locate the fault point by combining the time difference and spatial differences between sensors.

[0131] Accurate fault location and real-time prediction are achieved: The optimal result after data fusion is used for real-time fault location, combined with a predictive model to provide a high-precision fault location. Real-time fault location: By combining the time difference obtained by multiple sensors with propagation speed information, the specific location of the fault point is calculated in real time and the precise coordinates of the fault are given. Fault prediction: Combining historical fault data with real-time signals, faults can be predicted in advance, fault trends can be identified, and early warnings can be issued.

[0132] Scope of application: This technical solution is suitable for accurately locating hidden faults in overhead power lines, especially in complex environments such as high-voltage lines and long-distance transmission lines, and can provide high-precision fault location. In addition, this method can also be applied to other fields, such as smart grids, railway power supply systems, aviation power systems, etc., to provide support for fault diagnosis and location in complex lines. First, the present invention deploys high-precision sensors at multiple key nodes to collect traveling wave signals in real time. It then uses multi-sensor data fusion techniques (such as Kalman filtering and particle swarm optimization) to effectively eliminate noise and errors, thereby significantly improving the accuracy of fault location. Traditional single-sensor positioning methods are limited by factors such as signal attenuation and environmental interference. Multi-sensor fusion can overcome these limitations, correct local errors in real time, and accurately locate the fault point. The present invention combines particle swarm optimization (PSO) and Kalman filtering algorithms, performing global and local optimization at different stages. The PSO algorithm provides a global optimal solution for initial positioning, while the Kalman filter corrects local positioning errors in real time. By establishing and dynamically updating the error model, it can effectively address the impact of environmental changes, line status changes, and other factors on positioning accuracy. This dynamic optimization and error correction capability enables the method to maintain high positioning accuracy even in complex environments. Finally, the signal propagation speed of the present invention is affected by the physical parameters of the power line (such as resistance, conductor type, grounding conditions, etc.) and environmental factors (such as temperature, humidity, etc.). By accurately modeling the propagation characteristics of the traveling wave signal and combining the line characteristics and environmental factors to estimate the propagation speed in real time, the positioning accuracy is further improved. Combined with the multivariate analysis methods of the propagation constant model, wave impedance model and transmission line model, it can cope with overhead lines and complex working environments, and is widely applicable to various application scenarios such as high-voltage, ultra-high-voltage and long-distance transmission lines.

[0133] The present invention provides a fault location method based on multiple acquisition devices. The method includes: obtaining multiple device positions of multiple traveling wave signal acquisition devices, the arrival time difference of the traveling wave signals collected by each of the acquisition devices, and the propagation speed of the traveling wave signals; establishing multiple hypersphere equations based on the device positions, arrival time differences, and propagation speeds; the hypersphere equations are used to reflect the difference in distance between each of the device positions and the fault point positions corresponding to the traveling wave signals; and locating the fault point using a preset particle swarm optimization algorithm and the hypersphere equations to obtain the optimal fault point location. Through the above method, multiple acquisition devices can be used to capture traveling wave signals, then the hypersphere equations can be established. Finally, the particle swarm optimization algorithm and the hypersphere equations are used to locate the fault point to obtain the optimal fault point location, thereby improving the accuracy of fault location.

[0134] See also Figure 2 , Figure 2 FIG. 1 is a structural block diagram of a fault location device based on multiple acquisition devices in an embodiment of the present invention. Figure 2 The apparatus shown comprises: Data acquisition module 201: used to acquire multiple device positions of multiple traveling wave signal acquisition devices, arrival time differences between each of the traveling wave signals acquired by the acquisition devices, and propagation speeds of the traveling wave signals; Equation establishment module 202: used to establish multiple hypersphere equations based on the positions of the two devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between the positions of the two devices and the fault point positions corresponding to the traveling wave signals; Positioning module 203: used to perform fault point location processing using a preset particle swarm optimization algorithm and the hypersphere equation to obtain the optimal fault point location.

[0135] It should be noted that Figure 2 The functions of each module in the device shown are Figure 1 The contents of each step in the method shown are similar, so they are not described here to avoid repetition. For details, please refer to Figure 1 The content of each step in the method shown.

[0136] The present invention provides a fault location device based on multiple acquisition devices. The device includes: a data acquisition module for acquiring the positions of multiple acquisition devices for multiple traveling wave signals, the arrival time differences of the traveling wave signals collected by each acquisition device, and the propagation speed of the traveling wave signals; an equation establishment module for establishing multiple hypersphere equations based on the position of each device, the arrival time differences, and the propagation speed. The hypersphere equations are used to reflect the difference in distance between each device position and the fault point location corresponding to the traveling wave signal; and a position location module for locating the fault point using a preset particle swarm optimization algorithm and the hypersphere equation to obtain the optimal fault point location. With the above-mentioned device, multiple acquisition devices can be used to capture traveling wave signals, then the hypersphere equations can be established. Finally, the particle swarm optimization algorithm and the hypersphere equations can be used to locate the fault point to obtain the optimal fault point location, thereby improving the accuracy of fault location.

[0137] Figure 3 FIG1 shows an internal structure diagram of a computer device in an embodiment. The computer device can be a terminal or a server. Figure 3 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the above method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the above method. It will be understood by those skilled in the art that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0138] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following Figure 1 Steps of the method shown.

[0139] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor is caused to execute the following Figure 1 Steps of the method shown.

[0140] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0141] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A fault location method based on multiple acquisition devices, characterized in that: The method comprises: Acquiring multiple device positions of multiple traveling wave signal acquisition devices, arrival time differences between traveling wave signals acquired by each of the acquisition devices, and propagation speeds of the traveling wave signals; Establishing multiple hypersphere equations based on the positions of the two devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between the positions of the two devices and the fault point positions corresponding to the traveling wave signal; The preset particle swarm optimization algorithm and the hypersphere equation are used to locate the fault point to obtain the optimal fault point location.

2. The method according to claim 1, characterized in that The method of performing fault point location processing using a preset particle swarm optimization algorithm and the hypersphere equation to obtain an optimal fault point location includes: Based on the actual line space of the transmission line as a constraint, the particle position and particle velocity of each particle in the particle swarm are randomly initialized, and the particle position represents the candidate fault point position; Obtaining a current fitness of the particle using the particle position and the hypersphere equation; If the current fitness is better than the historical fitness of the particle, updating the personal optimal position of the particle to the particle position of the particle; Select the best individual position with the largest fitness from the best individual positions of all particles as the global best position, and increase the current number of iterations by 1; Determining whether the current number of iterations and / or the global optimal position meets a preset iteration termination condition; If the current number of iterations and / or the global optimal position do not meet the preset iteration termination condition, the particle velocity and particle position of each particle are updated using the global optimal position and the personal optimal position to obtain the updated particle velocity and particle position of each particle; and the step of obtaining the current fitness using the particle position and the hypersphere equation is returned to execution. If the current number of iterations and / or the global optimal position meets a preset iteration termination condition, the global optimal position is output, and the optimal fault point position includes the global optimal position.

3. The method according to claim 2, characterized in that The obtaining of the current fitness of the particle by using the particle position and the hypersphere equation includes: Using the particle position and the hypersphere equation, obtaining a matching degree between the particle position and the hypersphere equation; The current fitness of the particle is obtained by summing up the multiple matching degrees.

4. The method according to claim 1, characterized in that The hypersphere equation includes the following mathematical expression: Where, (x i ,y i ,z i ), (x j ,y j ,z j ) are the device positions of collection device i and collection device j respectively; is the arrival time difference of the traveling waves recorded by acquisition device i and acquisition device j; is the traveling wave propagation velocity, and (x, y, z) is the fault point location corresponding to the traveling wave signal.

5. The method according to claim 2, characterized in that: The particle velocity update includes the following mathematical expression: ； Where, is the i-th particle at the next moment The particle velocity; and They are Particles at the current moment The particle velocity and particle position; It is The individual best position of each particle; is the global optimal position; is the inertia weight; and is the learning factor; and is a random number; The updating of the particle position includes the following mathematical expression: ； Where, is the i-th particle at the next moment The particle position.

6. The method according to claim 3, characterized in that: The matching degree includes: ； Where, is the matching degree between particle i and hypersphere equation jk, ( , , ) is the particle position of particle i, ( , , ) is the device position of the collection device j, ( , , ) is the device position of the acquisition device k, v is the traveling wave propagation speed, is the arrival time difference of the traveling waves recorded by acquisition device i and acquisition device k.

7. The method according to claim 1, characterized in that Get the propagation speed, including: Obtain physical parameters of transmission lines; The propagation speed is determined based on the physical parameters of the transmission line and the preset electromagnetic wave propagation model.

8. A fault location device based on multiple acquisition devices, characterized in that: The device comprises: Data acquisition module: used to acquire multiple device positions of multiple collection devices of traveling wave signals, the arrival time difference between the traveling wave signals collected by each of the collection devices, and the propagation speed of the traveling wave signals; An equation building module is used to build multiple hypersphere equations based on the positions of the two devices, the arrival time difference, and the propagation speed, wherein the hypersphere equations are used to reflect the difference in distance between the positions of the two devices and the fault point positions corresponding to the traveling wave signals; Positioning module: used to perform fault point location processing using a preset particle swarm optimization algorithm and the hypersphere equation to obtain the optimal fault point location.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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