Novel traveling wave detection method and system for distribution network overhead line fault

Abstract

The invention discloses a novel traveling wave detection method and system for distribution network overhead line faults, and relates to the field of traveling wave detection. The method comprises the following steps: acquiring a traveling wave signal and a power signal acquired by a sensor arranged at a key node in the novel distribution network overhead line under the condition that a fault of the novel distribution network overhead line is detected; updating a traveling wave propagation model based on the power signal, wherein the traveling wave propagation model is used for representing propagation characteristics of traveling waves in a line topological structure corresponding to the novel distribution network overhead line; screening out interference signals in the traveling wave signals based on the updated traveling wave propagation model to obtain target traveling wave signals; and determining a fault point based on the target traveling wave signal. By implementing the technical scheme provided by the invention, the accuracy of fault point positioning in the power distribution network can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traveling wave detection, in particular to a new type of traveling wave detection method and system for overhead line faults in distribution network. BACKGROUND

[0002] With the increasing complexity of distribution network architecture, distribution lines often contain multiple branch lines, and the widespread access of distributed energy makes the power flow of the distribution network exhibit dynamic bidirectional characteristics. In such a complex distribution network, when a line fault occurs, the traveling wave signal will be reflected and superimposed multiple times at the branch nodes, causing the fault signal to be obscured by interference waves, affecting the accuracy of fault location.

[0003] Currently, common fault location methods for distribution lines are mainly based on the principle of traveling wave. By installing traveling wave detection devices on the line, the traveling wave signals at the time of fault occurrence are collected, and the propagation characteristics of the traveling wave are used for fault point positioning. Some schemes also use filtering technology to reduce the influence of signal noise, or increase the number of detection points to improve positioning accuracy.

[0004] However, existing traveling wave detection methods often treat distribution lines as static models, failing to fully consider the impact of power flow characteristics changes caused by distributed energy access on traveling wave propagation. This makes it difficult to effectively identify and filter out interference waves caused by power flow changes in complex distribution network environments, resulting in inaccurate fault location results and affecting fault handling efficiency. SUMMARY

[0005] The present application provides a new type of traveling wave detection method and system for overhead line faults in distribution network, which can improve the accuracy of fault point positioning in distribution network.

[0006] In the first aspect of the present application, the present application provides a new type of traveling wave detection method for overhead line faults in distribution network, comprising:

[0007] In the case of detecting a new type of overhead line fault in distribution network, the traveling wave signals and power signals collected by the sensors arranged at the key nodes in the new type of overhead line in distribution network are obtained;

[0008] Updating the traveling wave propagation model based on the power signal, the traveling wave propagation model being used to represent the propagation characteristics of the traveling wave in the line topology structure corresponding to the new type of overhead line in distribution network;

[0009] Filtering out the interference signals in the traveling wave signals based on the updated traveling wave propagation model to obtain target traveling wave signals;

[0010] Determining the fault point based on the target traveling wave signals.

[0011] A second aspect of this application provides a novel traveling wave detection system for faults in overhead distribution lines, comprising:

[0012] The signal acquisition module is used to acquire traveling wave signals and power signals collected by sensors installed at key nodes in the new distribution network overhead line when a fault is detected.

[0013] The model update module is used to update the traveling wave propagation model based on the power signal. The traveling wave propagation model is used to characterize the propagation characteristics of the traveling wave in the line topology corresponding to the new type of distribution network overhead line.

[0014] The signal filtering module is used to filter out interference signals in the traveling wave signal based on the updated traveling wave propagation model to obtain the target traveling wave signal.

[0015] The fault point determination module is used to determine the fault point based on the target traveling wave signal.

[0016] A third aspect of this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above.

[0017] A fourth aspect of this application provides an electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the above-described method steps.

[0018] In a fifth aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method steps described in any of the above embodiments. In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0019] By installing sensors at key nodes of overhead distribution lines, not only traveling wave signals but also power signals are acquired simultaneously. The power signals are then used to update the traveling wave propagation model in real time, ensuring the model accurately reflects the traveling wave propagation characteristics under the current line topology. Based on this dynamically updated traveling wave propagation model, interference signals caused by power flow changes can be effectively identified and filtered out, thus obtaining the accurate target traveling wave signal. This power signal-driven dynamic modeling method overcomes the limitation of existing technologies that treat distribution lines as static models, enabling accurate identification of real fault traveling wave signals even in dynamic power flow environments caused by distributed energy access. By employing this dynamic adaptive traveling wave detection method, the accuracy of fault location in complex distribution networks is significantly improved. Attached Figure Description

[0020] Figure 1This is a flowchart illustrating a novel traveling wave detection method for overhead power line faults in a distribution network, provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of the structure of a novel traveling wave detection system for overhead power distribution line faults provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a novel traveling wave detection method for overhead distribution line faults provided in this application. This method can be implemented using a computer program, a microcontroller, or run on a novel traveling wave detection system for overhead distribution line faults based on the von Neumann architecture. The computer program can be integrated into the application or run as a standalone utility application. Specifically, the method may include the following steps:

[0027] Step 101: In the event of a fault detected in the new type of overhead distribution network, acquire the traveling wave signal and power signal collected by sensors installed at key nodes in the new type of overhead distribution network.

[0028] Among them, the new type of overhead distribution line refers to an intelligent overhead distribution network system with a multi-branch structure and access to distributed energy. In the embodiments of this application, it can be understood as an overhead distribution line network with a complex topology composed of multiple trunk lines, branch lines and distributed energy access points.

[0029] Correspondingly, key nodes refer to characteristic locations in new distribution network overhead lines that significantly influence the propagation characteristics, reflection characteristics, and fault location accuracy of traveling wave signals. These locations include line branch nodes, transformer connection points, distributed energy grid connection points, line endpoints, and important load connection points. In this application embodiment, these can be understood as monitoring points where sensors need to be deployed, determined through methods such as line topology analysis, power flow characteristic analysis, fault location requirement analysis, and traveling wave propagation characteristic analysis. The impedance characteristics, power flow characteristics, or structural characteristics of these points have a significant impact on the propagation and reflection of traveling wave signals. This is used to construct a distributed sensor monitoring network to achieve comprehensive monitoring of traveling wave signals in new distribution network overhead lines, providing accurate signal acquisition data for fault location, while ensuring that the system achieves optimal monitoring results while considering economic efficiency.

[0030] Furthermore, it is necessary to configure corresponding sensors at each key node. The sensor refers to a traveling wave signal monitoring device with high-precision clock synchronization function and real-time data acquisition capability. This device can simultaneously collect the instantaneous values ​​of electrical quantities such as voltage and current, and preprocess and timestamp the collected signals.

[0031] For example, at a line branch node, a sensor can be installed 5-10 meters upstream of the branch line junction to collect traveling wave reflection signals caused by the branch; at a transformer connection point, a sensor can be installed at the primary side inlet of the transformer to monitor changes in traveling wave characteristics caused by the transformer; at a distributed energy grid connection point, sensors can be installed on both sides of the grid connection point to monitor the impact of power flow changes on traveling wave propagation; at a line endpoint, a sensor can be installed near the end protection device to collect the terminal reflection characteristics of the traveling wave signal; at an important load connection point, a sensor can be installed at the load inlet to monitor changes in traveling wave characteristics caused by load changes.

[0032] In practical applications, faults in new distribution network overhead lines mainly include single-phase grounding faults, phase-to-phase short-circuit faults, and open-circuit faults. When these faults occur, high-frequency transient traveling waves are generated in the line, and these traveling wave signals propagate from the fault point to both ends of the line. Fault detection is achieved by monitoring the voltage and current change characteristics in the line. Specifically, the traveling wave acquisition unit of the sensor detects instantaneous changes in voltage or current through high-frequency sampling. When the detected rate of change of voltage or current exceeds a preset threshold and the duration is greater than the minimum recognition time set by the system, the system determines that a fault has occurred.

[0033] For example, in the case of a single-phase ground fault, the voltage of the faulty phase drops sharply, generating a ground current; in the case of a phase-to-phase short-circuit fault, the voltage difference between the faulty phases decreases significantly, accompanied by a large short-circuit current; in the case of an open-circuit fault, the current in the open-circuit phase suddenly drops to zero, possibly accompanied by abnormal voltage changes. Sensors, with their high-precision sampling capabilities, can accurately capture these characteristic changes. When a sensor at any critical node detects these fault characteristics, it triggers the entire monitoring network to begin acquiring traveling wave and power signals.

[0034] Step 102: Update the traveling wave propagation model based on the power signal. The traveling wave propagation model is used to characterize the propagation characteristics of traveling waves in the line topology corresponding to the new distribution network overhead line.

[0035] The line topology refers to a structural model describing the spatial connections and electrical characteristics of the various components in a new type of overhead distribution network. This structure includes the physical connections of the lines, the distribution locations of key equipment, and the electrical parameters of each line segment. In this embodiment, it can be understood as a network description model composed of a line segment parameter matrix, a node connection matrix, and an equipment characteristic matrix. The line segment parameter matrix includes the length and impedance characteristics of each line segment, the node connection matrix describes the connection relationships between key nodes, and the equipment characteristic matrix characterizes the electrical characteristics of equipment such as transformers and distributed energy sources. This structure serves as the basic framework for constructing a traveling wave propagation model, determining possible propagation paths of traveling wave signals, analyzing impedance distribution characteristics in the network, and providing topological support for traveling wave propagation characteristic analysis and fault location.

[0036] Based on the aforementioned line topology, the traveling wave propagation model refers to a mathematical description system constructed by digitizing and parameterizing the line topology and combining it with electromagnetic wave propagation theory. This system establishes a mapping relationship between the physical characteristics of the line and the propagation characteristics of the traveling wave, thereby achieving an accurate description of the traveling wave propagation behavior. The traveling wave propagation model is used to calculate the propagation time of the traveling wave from the fault point to each monitoring point, analyze the propagation path of the traveling wave in complex topologies, and predict the reflection wave characteristics at each key node.

[0037] Based on the above embodiments, as an optional embodiment, the traveling wave propagation model construction process may include the following steps:

[0038] Step 201: Obtain the line topology of the new distribution network overhead line. The line topology includes the length of the line segment, impedance, and the location of key nodes.

[0039] When constructing a traveling wave propagation model for faults in new distribution network overhead lines, the primary task is to obtain complete line topology information. This is because the propagation characteristics of traveling wave signals in the line are directly affected by the line's physical characteristics, and accurate topology information is the foundation for accurate fault location.

[0040] In practice, the basic information of the line topology is first obtained through a GIS system or line design drawings. This information mainly includes the physical length of the line segments, the impedance characteristics of the line, and the location information of various key nodes. These key nodes are the locations where traveling wave signals may be reflected, and their spatial distribution has a significant impact on the propagation path and reflection characteristics of the traveling waves.

[0041] To facilitate subsequent modeling and analysis, the acquired topology information needs to be standardized. The length data of the n line segments is converted into a standard array format: L i ={L1,L2,...,L n Each element represents the actual length of the corresponding line segment. For the location information of key nodes, GPS absolute coordinates or distances relative to the starting point of the line can be used for description. This standardized location representation helps in subsequent calculations of distance relationships between nodes and traveling wave propagation delay. Simultaneously, the impedance parameters of each line segment are recorded; these parameters will be used to calculate the propagation speed and attenuation characteristics of traveling waves in different line segments.

[0042] After obtaining the line topology information, in order to effectively describe the propagation characteristics of traveling waves in complex overhead distribution lines, it is necessary to convert the line topology into a mathematical graphical model representation. Using graph theory modeling methods can accurately describe the connection relationships and propagation characteristics between the various components of the line.

[0043] Specifically, the railway line topology is abstracted as a graph model G = (N, E), where the node set N represents the critical nodes in the line, including branch points, connection points, and other locations that may trigger traveling wave reflections; the edge set E represents the actual line segments, and the weight of each edge is set as the propagation delay of the traveling wave in that line segment. This abstraction transforms the complex physical line structure into a graph theory model that is easy to process mathematically, where the connections between nodes directly reflect the possible propagation paths of the traveling wave, and the edge weights characterize the time required for the traveling wave to propagate along each path.

[0044] To facilitate computer processing and subsequent algorithm implementation, the graph model is represented in the form of an adjacency matrix.

[0045] Given a graph G containing n nodes, construct an n×n adjacency matrix A. The elements A in the matrix are... ij Indicates the connection relationship between node i and node j: when there is a direct connection line segment between nodes i and j, A ij The value is the propagation delay t corresponding to this line segment. i When there is no direct connection between nodes, A ij The value is set to infinity. This matrix representation not only fully records the network's connectivity but also includes the time information of the traveling wave propagation.

[0046] Step 202: Generate a traveling wave propagation model based on the line topology.

[0047] Specifically, in constructing a traveling wave propagation model, the primary task is to calculate the propagation speed of the traveling wave in the overhead distribution network, as the propagation speed directly determines the time it takes for the traveling wave signal to reach different nodes and is a fundamental parameter for fault location calculations. The propagation speed of the traveling wave is affected by the physical characteristics of the line and needs to be accurately calculated based on the basic parameters of the line.

[0048] Specifically, the inductance L and capacitance C per unit length of the line are first obtained. These parameters reflect the basic electrical characteristics of the line and are determined by its physical structure (such as conductor material, cross-sectional area, and installation height). According to electromagnetic wave propagation theory, the traveling wave velocity v has a definite mathematical relationship with the line's inductance L and capacitance C:

[0049]

[0050] In the formula, v represents the traveling wave propagation speed, L′ represents the inductance per unit length of the line, and C′ represents the capacitance per unit length of the line.

[0051] Since overhead distribution lines typically consist of multiple line segments with different characteristics, the propagation speed of each segment needs to be calculated separately. For the i-th line segment, based on its unique inductance L... i and capacitor C i The parameters are used to calculate the propagation speed v of this section of the line. i Store the propagation speed calculation results for all line segments as a speed array: V = {v1, v2, ..., v...} n}; where n is the total number of line segments. This segmented calculation and storage method ensures that the model can accurately reflect the differences in propagation characteristics among different line segments.

[0052] Furthermore, in the traveling wave propagation model, accurately calculating the reflection coefficient is crucial for describing the propagation behavior of traveling waves in overhead distribution lines. When a traveling wave signal propagates to a critical node, reflection and transmission occur due to impedance mismatch on both sides of the node. The magnitude of the reflection coefficient directly determines the amplitude and phase characteristics of the reflected wave, which is of great significance for subsequently separating fault traveling wave signals from interference signals.

[0053] Specifically, the first step is to determine the incident line impedance Z1 and the node impedance Z2 at each critical node. The incident line impedance is determined by the physical characteristics of the line, while the node impedance needs to be treated differently depending on the node type. For ordinary load points, the load impedance value is directly used; for branch nodes, the equivalent impedance formed by multiple branch lines connected in parallel needs to be calculated. Based on these impedance values, the reflection coefficient of each critical node is calculated using the reflection coefficient calculation formula:

[0054]

[0055] In the formula, ρ represents the reflection coefficient, Z1 represents the incident line impedance, and Z2 represents the load impedance or branch impedance at the node.

[0056] For branch nodes, the equivalent impedance calculation needs to consider the combined effect of all connected branches. Assume a branch node is connected to n branches, and the characteristic impedances of each branch are Z... i The equivalent impedance of the node can then be calculated using parallel relationships. The calculated reflection coefficients of each node are stored as an array:

[0057] ρ={ρ1,ρ2,…,ρ m};

[0058] In the formula, m represents the total number of critical nodes. This storage method facilitates the subsequent model's rapid retrieval of the corresponding reflection coefficient values ​​when calculating traveling wave propagation.

[0059] Furthermore, in the traveling wave propagation model, calculating the propagation delay is a crucial step in transforming the spatiotemporal characteristics of the traveling wave into specific numerical values. The propagation delay reflects the time required for the traveling wave signal to propagate through the line; it is a vital basis for fault location calculations and directly affects the accuracy of the fault location calculation. Accurate propagation delay calculations require comprehensive consideration of both the line length and the propagation speed—two fundamental parameters.

[0060] Specifically, for each segment of the overhead distribution network, the line length L obtained from the aforementioned steps is... i and the corresponding propagation speed v i The propagation delay of this section of the line can be calculated using the following formula:

[0061]

[0062] In the formula, t i L represents the propagation delay of the traveling wave in the i-th segment of the path. i v represents the length of the i-th segment of the line. i This represents the propagation speed of the i-th segment of the line.

[0063] The calculated propagation delays for each line segment are stored as an array T = {t1, t2, ..., t...} n}, where n is the total number of line segments.

[0064] By acquiring the line topology of the new distribution network overhead lines, including the length of line segments, impedance, and the location of key nodes, complete basic data is provided for the construction of a traveling wave propagation model. Based on this topological information, a traveling wave propagation model is generated, enabling it to accurately reflect the propagation characteristics of traveling waves in actual lines. Since the line topology directly affects the propagation path and reflection characteristics of traveling waves, the propagation model built based on complete topological information can accurately describe the propagation time of traveling waves from the fault point to each monitoring point and effectively predict the reflection wave characteristics at each key node. This provides reliable theoretical support for subsequent fault location and improves the accuracy of fault location.

[0065] Based on the above embodiments, as an optional embodiment, step 102: updating the traveling wave propagation model based on the power signal, may further include the following steps:

[0066] The propagation speed, line impedance, and reflection coefficient of key nodes in the traveling wave propagation model are adjusted based on power signals.

[0067] Among them, the propagation speed is positively correlated with the power flow characteristics of the line topology.

[0068] Specifically, in new overhead distribution lines, the traveling wave propagation speed exhibits a dynamic variation pattern that increases with the increase of power flow intensity in the line. In the embodiments of this application, this can be understood as follows: when the power flow in the line increases, the equivalent inductance and capacitance of the line change due to the enhanced electromagnetic field intensity. Specifically, the inductance value relatively decreases while the capacitance value relatively increases, ultimately leading to an increase in the traveling wave propagation speed.

[0069] Furthermore, the power flow characteristics are mainly reflected in the magnitude and direction of the power. When the power flow increases, the current in the line increases, the generated magnetic field strengthens, which reduces the self-inductance of the line, resulting in a decrease in inductance per unit length. At the same time, due to the increase in voltage, the electric field strength increases, which increases the capacitance of the conductor to ground, resulting in an increase in capacitance per unit length. This combined effect of decreased inductance and increased capacitance ultimately leads to an increase in propagation speed.

[0070] The aforementioned positive correlation is particularly pronounced near distributed energy access points, where power flow changes more drastically. By establishing a model showing the positive correlation between propagation velocity and power flow characteristics, the traveling wave propagation velocity parameters can be adjusted in real time, effectively improving the fault location accuracy of traditional fixed propagation velocity models in scenarios with large power fluctuations.

[0071] Among them, line impedance is used to characterize the impedance change of the line topology under distributed energy access or dynamic power flow.

[0072] Specifically, in new overhead power distribution lines, the effective impedance of each section of the line dynamically changes with time and spatial location due to the access of distributed energy resources and the change in power flow direction. In the embodiments of this application, it can be understood that the line impedance is no longer a fixed value in the traditional power distribution network, but a dynamic parameter that changes in real time with the output of distributed energy resources and load fluctuations, including changes in impedance magnitude and phase angle.

[0073] Furthermore, at the point of connection for distributed energy resources, changes in the output of these resources alter the equivalent impedance characteristics of that node. For example, photovoltaic power generation varies with solar irradiance, and wind power output varies with wind speed. These changes directly affect the voltage and current relationships at the connection point, thus altering the line's equivalent impedance. Simultaneously, because the integration of distributed energy resources disrupts the traditional unidirectional power supply model of distribution networks, power flow exhibits bidirectional characteristics, causing the line impedance to display different characteristics under different power flow directions.

[0074] When a traveling wave propagates to a point of impedance abrupt change, reflection and transmission occur, and the amplitude and phase of the reflected wave are directly affected by the impedance change. By tracking and updating the line impedance parameters in real time, the reflection characteristics of the traveling wave during propagation can be predicted more accurately, improving the identification and separation of fault traveling wave signals.

[0075] The reflection coefficient is used to characterize the reflection characteristics of traveling wave signals at critical nodes.

[0076] Specifically, in new overhead distribution network lines, a quantitative descriptive parameter is provided for the reflection phenomenon caused by impedance abrupt changes when a traveling wave signal propagates to a critical node. This parameter reflects the amplitude ratio and phase relationship between the incident and reflected waves. In the embodiments of this application, it can be understood as a complex parameter that dynamically adjusts with impedance changes at the critical node. Its amplitude represents the amplitude ratio of the reflected wave to the incident wave, and its phase represents the phase change of the reflected wave relative to the incident wave. This parameter is updated in real time with changes in distributed energy output and power flow characteristics.

[0077] Furthermore, the reflection coefficient exhibits different characteristics at different types of critical nodes. At branch nodes, the equivalent impedance decreases due to the parallel connection of multiple lines, resulting in a negative reflection coefficient, indicating that the reflected wave has the opposite polarity to the incident wave. At the end of an open circuit, the reflection coefficient approaches positive one due to the impedance approaching infinity, indicating that the reflected wave has the same polarity as the incident wave. At the distributed energy access point, the reflection coefficient changes dynamically with the output of the distributed energy source, reflecting the dynamic characteristics of the impedance at the access point.

[0078] By updating the reflection coefficients of each key node in real time, the multiple reflection behaviors of fault traveling waves during propagation can be accurately predicted, effectively identifying and separating various reflected waves, thereby improving the accuracy of fault location. Especially in scenarios involving distributed energy access and drastic power dynamic changes, the dynamic reflection coefficient model has better adaptability than the traditional fixed reflection coefficient model.

[0079] By dynamically adjusting the propagation speed, line impedance, and reflection coefficient of key nodes in the traveling wave propagation model based on power signals, the propagation model can accurately reflect the dynamic characteristics of overhead distribution lines under actual operating conditions. Since the propagation speed is positively correlated with the power flow characteristics of the line topology, the traveling wave propagation model can correspondingly increase the propagation speed parameter when power flow increases, accurately reflecting changes in actual propagation characteristics. Simultaneously, by characterizing the impedance changes of the line topology under distributed energy access or dynamic power flow through line impedance, and by using the reflection coefficient to characterize the reflection characteristics of the traveling wave signal at key nodes, the traveling wave propagation model can comprehensively capture the impact of distributed energy access and dynamic power changes on the traveling wave propagation characteristics. This dynamic update mechanism based on power signals overcomes the limitations of traditional fixed-parameter models that cannot adapt to dynamic network changes, improves the accuracy of the traveling wave propagation model in actual operating environments, and provides a more reliable theoretical basis for subsequent fault location.

[0080] Based on the above embodiments, as an optional embodiment, adjusting the propagation speed, line impedance, and reflection coefficient of key nodes of the traveling wave propagation model based on the power signal may further include the following steps:

[0081] Step 301: Adjust the propagation speed of the traveling wave propagation model based on the flow characteristics of the power signal.

[0082] In new overhead power distribution lines, the flow characteristics of power signals directly affect the propagation speed of traveling waves. Therefore, it is necessary to dynamically adjust the propagation speed parameters in the traveling wave propagation model based on real-time power signal acquisition. Traditional traveling wave propagation models typically use fixed propagation speed values, which cannot adapt to the impact of distributed energy access and dynamic power flow. This leads to reduced fault location accuracy in scenarios with large power fluctuations. To address this issue, this application provides a method for adjusting the propagation speed in real time based on power signals.

[0083] Specifically, it is first necessary to acquire the power signal P(t) collected in real time by the sensor, including active and reactive power information, and simultaneously acquire the voltage signal U(t) and current signal I(t). These signals reflect the real-time state of power flow in the line. In addition, it is also necessary to acquire the initial parameters of the line, namely the inductance L' and capacitance C' per unit length, which describe the basic electromagnetic characteristics of the line.

[0084] When the power flow in a circuit changes, the equivalent inductance and capacitance of the circuit dynamically change due to the alteration of the electromagnetic field strength. Specifically, as the power flow increases, the increased current in the circuit strengthens the magnetic field, reducing the self-inductance of the circuit, which manifests as a decrease in inductance per unit length. Simultaneously, the increased voltage leads to an increased electric field strength, increasing the capacitance of the conductor to ground, which manifests as an increase in capacitance per unit length. To accurately describe this dynamic change, the embodiments of this application employ the following calculation method:

[0085] For dynamic adjustment of the inductor: L′(t)=L′0·(1+k L ·ΔI(t));

[0086] In the formula, L′(t) represents the dynamic inductance value at time t, L′0 is the initial inductance value, and k L Let ΔI(t) be the inductance adjustment coefficient, and ΔI(t) be the rate of change of the current amplitude. This adjustment method can reflect the influence of current changes on the inductance characteristics of the line.

[0087] Similarly, for the dynamic adjustment of the capacitor: C′(t)=C′0·(1+k C ·ΔU(t));

[0088] Where C′(t) represents the dynamic capacitance value at time t, C′0 is the initial capacitance value, and k C Let ΔU(t) be the capacitance adjustment coefficient, and ΔU(t) be the voltage change rate. This adjustment method takes into account the impact of voltage changes on the line capacitance characteristics.

[0089] Based on the adjusted L′(t) and C′(t), the propagation speed is calculated in real time:

[0090]

[0091] The dynamic velocity calculation method described above can accurately reflect the impact of power flow changes on the propagation characteristics of traveling waves, and it shows better adaptability, especially in scenarios where the output of distributed energy sources fluctuates greatly.

[0092] Step 302: Adjust the line impedance of the traveling wave propagation model based on the changes in current amplitude and voltage value in the power signal.

[0093] Specifically, in new distribution network overhead lines, line impedance is a key parameter affecting the reflection and attenuation characteristics of traveling waves. Due to the integration of distributed energy resources and dynamic load changes, line impedance is no longer a fixed value but exhibits complex dynamic characteristics. Especially in scenarios with bidirectional power flow, traditional fixed impedance models cannot accurately describe the actual impedance characteristics of the line, which directly affects the accurate prediction of the reflection and attenuation behavior of traveling waves during propagation. Therefore, it is necessary to establish a method for adjusting line impedance in real time based on power signals.

[0094] Specifically, the system first acquires the real-time voltage signal U(t), current signal I(t), and power signal P(t) changes from the sensors. Simultaneously, it acquires the initial parameters of the line, including the initial resistance R0 and initial reactance X0. The total impedance Z(t) of the line consists of a resistance component R(t) and a reactance component X(t), expressed in complex form: Z(t) = R(t) + jX(t); where the resistance component is mainly affected by changes in current amplitude, while the reactance component is mainly affected by changes in voltage.

[0095] For the dynamic adjustment of the resistance part: R(t)=R0·(1+k R ·ΔI(t));

[0096] In the formula, R(t) represents the dynamic resistance value at time t, R0 is the initial resistance value, and k R Let ΔI(t) be the resistance adjustment coefficient, and ΔI(t) be the rate of change of current. The above adjustment method considers the effect of current changes on conductor temperature, thus reflecting the dynamic characteristics of the resistance value. When the current increases, the conductor temperature rises, leading to an increase in resistance; when the current decreases, the resistance value decreases accordingly.

[0097] For adjustments to the reactance section, the inductive reactance X needs to be considered separately. L Harmony Anti-X C The change in inductive impedance. The adjustment of inductive impedance is performed using the formula: X L (t)=X L0 ·(1+k X ·ΔU(t));

[0098] In the formula, X L (t) represents the dynamic sensing impedance at time t, XL0 k is the initial resistance value. X Here, ΔU(t) represents the inductive reactance adjustment coefficient, and ΔU(t) represents the voltage change rate. This adjustment reflects the impact of voltage changes on the inductive reactance characteristics of the line.

[0099] For lines with capacitive characteristics, the adjustment of capacitive reactance also needs to be considered:

[0100] X C (t)=X C0 ·(1+k C ·ΔU(t));

[0101] In the formula, X C (t) represents the dynamic capacitive reactance value at time t, X C0 k is the initial capacitive reactance value. C This is the capacitive reactance adjustment factor.

[0102] The total equivalent impedance of the line is obtained by combining the dynamic adjustment results of the combined resistance and reactance:

[0103] Z(t)=R(t)+j(X L (t)-X C (t));

[0104] The dynamic impedance calculation method described above comprehensively considers the impact of current and voltage changes on line impedance characteristics, and can accurately reflect the line characteristics under distributed energy access conditions. Especially in scenarios with severe power fluctuations, the dynamic impedance model has better adaptability than the traditional fixed impedance model.

[0105] Step 303: Based on the adjusted line impedance, determine the reflection coefficient corresponding to the key node.

[0106] Specifically, in new overhead distribution lines, accurately calculating the reflection coefficient is crucial for describing the reflection characteristics of traveling waves at critical nodes. Due to the integration of distributed energy resources and dynamic power variations, the impedance characteristics at critical nodes exhibit complex dynamic changes, directly affecting the reflection and transmission behavior of traveling wave signals during propagation. Traditional fixed reflection coefficient models cannot accurately describe these dynamic characteristics; therefore, it is necessary to update the reflection coefficient of critical nodes in real time based on the adjusted line impedance.

[0107] In practice, the adjusted line impedance Z1(t) is first obtained as the incident line impedance, which already takes into account the influence of power flow on the line characteristics. At the same time, the equivalent load impedance Z2(t) at the node is obtained, which needs to be calculated based on the specific characteristics of the node.

[0108] To reflect the impact of power changes on node impedance, a formula is used to dynamically adjust the node impedance:

[0109] Z2(t)=Z 20 ·(1+k Z ·ΔP(t));

[0110] In the formula, Z 20 Let k be the initial impedance value of the node. Z Let ΔP(t) be the impedance adjustment coefficient, and ΔP(t) be the rate of change of the power signal. This adjustment method can accurately reflect the impact of distributed energy output changes and load fluctuations on the node impedance characteristics.

[0111] Based on the incident beam impedance Z1(t) and the dynamically adjusted node impedance Z2(t), the reflection coefficient is calculated in real time using the following formula:

[0112]

[0113] The reflection coefficient is a complex number, with its amplitude representing the ratio of the amplitude of the reflected wave to that of the incident wave, and its phase representing the phase change of the reflected wave relative to the incident wave. When the node impedance is greater than the line impedance, the reflection coefficient is positive, indicating that the reflected wave is in phase with the incident wave; when the node impedance is less than the line impedance, the reflection coefficient is negative, indicating that the reflected wave is out of phase with the incident wave; when the two are equal, the reflection coefficient is zero, indicating no reflection.

[0114] By adjusting the key parameters of the traveling wave propagation model in a hierarchical manner, accurate dynamic updates of the model are achieved. First, the propagation speed is adjusted based on the flow characteristics of the power signal to accurately reflect the impact of power changes on the propagation speed. Then, changes in current and voltage values ​​in the power signal are used to adjust the line impedance, enabling the model to accurately characterize the impact of distributed energy access and power fluctuations on line characteristics. Finally, the reflection coefficients corresponding to key nodes are determined based on the adjusted line impedance, achieving a precise description of the traveling wave reflection characteristics. This hierarchical adjustment method ensures the accuracy of each parameter adjustment while reflecting the correlation between parameters, enabling the traveling wave propagation model to comprehensively and accurately describe the propagation characteristics of traveling waves in dynamic operating environments.

[0115] Step 103: Based on the updated traveling wave propagation model, filter out the interference signals in the traveling wave signal to obtain the target traveling wave signal.

[0116] In new overhead distribution networks, the presence of multi-branch structures and distributed energy access leads to complex reflections and superpositions of traveling wave signals during propagation. These reflected waves and interference signals can obscure the true characteristics of the fault traveling wave, reducing the accuracy of fault location. Therefore, it is necessary to use scientific signal processing methods based on an updated traveling wave propagation model to filter out interference signals and extract the target traveling wave signal containing fault characteristics.

[0117] Based on the above embodiments, as an optional embodiment, step 103, which involves filtering out interference signals from the traveling wave signal based on the updated traveling wave propagation model to obtain the target traveling wave signal, may further include the following steps:

[0118] Step 401: Obtain the signal components of different frequencies in the traveling wave signal.

[0119] Specifically, traveling wave signals are typically composed of components at different frequencies, including high-frequency transient signals generated by faults, the power frequency components of the line itself, and various interference components caused by distributed energy access and load changes. To accurately identify and separate fault traveling wave signals, it is first necessary to perform frequency domain decomposition on the original traveling wave signal to obtain signal components at different frequencies. This provides a basis for subsequent interference signal filtering.

[0120] Specifically, this embodiment uses a frequency domain analysis method to decompose the traveling wave signal. First, a Fast Fourier Transform (FFT) is used to transform the time-domain signal x(t) to the frequency domain:

[0121]

[0122] This transformation allows us to obtain the signal's distribution characteristics across the entire frequency range, including the amplitude and phase information of each frequency component fi. The advantage of this method lies in its high computational efficiency, enabling rapid signal spectral analysis. Especially in real-time fault detection scenarios, the FFT method can meet the demands for fast processing.

[0123] Furthermore, considering the significant time-varying characteristics of traveling wave signals, especially in scenarios involving dynamic integration of distributed energy resources, simple FFT analysis may not fully reflect the time-frequency characteristics of the signal. Therefore, this embodiment also employs wavelet transform (WT) to perform a more detailed time-frequency decomposition of the signal:

[0124]

[0125] In this method, the scale parameter 'a' is used to adjust the frequency resolution; a larger 'a' value corresponds to low-frequency component analysis, while a smaller 'a' value is suitable for analyzing high-frequency details. The translation parameter 'b' is used to control the time resolution; by adjusting the value of 'b', the frequency characteristics of the signal at different times can be analyzed. This dual-resolution analysis method is particularly suitable for processing transient non-stationary signals such as fault traveling waves.

[0126] For example, in a typical overhead distribution line fault scenario, the overall spectral characteristics of the signal can be obtained first using the FFT method. Experience shows that fault traveling wave signals are typically concentrated in higher frequency bands (e.g., above 10kHz), while system power frequency components and low-frequency interference are mainly distributed in lower frequency bands (e.g., 50Hz and its harmonic frequencies). By analyzing the spectral characteristics, the frequency ranges of different signal types can be preliminarily identified. Subsequently, wavelet transform is used to perform a refined analysis of the frequency range of interest, and appropriate wavelet basis functions are selected for signal decomposition to obtain signal components of different frequencies.

[0127] Using the above technical solution, the overall frequency characteristics of the signal can be quickly obtained through FFT, providing direction for subsequent analysis; secondly, the multi-resolution analysis capability of wavelet transform can accurately capture the characteristic changes of traveling wave signals in the time and frequency dimensions; finally, the complementary use of the above methods improves the reliability and accuracy of signal decomposition. After the above processing, signal components of different frequencies... This effective separation lays the foundation for subsequent interference signal screening and fault feature extraction, ultimately helping to improve the accuracy of fault location.

[0128] Step 402: Calculate the theoretical reflected wave characteristics based on the updated traveling wave propagation model.

[0129] In new overhead power distribution networks, traveling wave signals are reflected when they reach critical nodes, and these reflected waves can interfere with the accuracy of fault location. To effectively identify and eliminate the influence of reflected waves, it is necessary to calculate the theoretical characteristics of reflected waves based on an updated traveling wave propagation model. These theoretical characteristics will serve as an important basis for determining which components of the actual signal are reflected waves.

[0130] Specifically, based on the propagation velocity *v* and line topology information in the updated traveling wave propagation model, the theoretical propagation delay of the reflected wave is first calculated. In this embodiment, the time delay for the reflected wave to propagate from the fault point to the critical node and then reflect back can be calculated using the following formula:

[0131]

[0132] Among them, S i This represents the distance from the fault point to the critical node. This time delay characteristic reflects the arrival time of the reflected wave in the time domain and is an important time feature for identifying reflected waves. Since the propagation velocity v in the traveling wave propagation model already considers the influence of power flow, the calculated time delay value can accurately reflect the actual propagation characteristics of the reflected wave.

[0133] Furthermore, based on the dynamically updated reflection coefficient ρ iGiven the incident wave amplitude, calculate the theoretical amplitude characteristics of the reflected wave. According to traveling wave theory, the amplitude of the reflected wave can be calculated using the formula:

[0134] A ref =A incident ·ρ i ;

[0135] Among them, A inc ρ is the amplitude of the incident wave. i This is the updated reflection coefficient. The above amplitude characteristics reflect the intensity of the reflected wave. For different types of critical nodes (such as branch points, load points, or distributed energy access points), due to their reflection coefficient ρ... i Depending on the location, the amplitude characteristics of the reflected wave will also differ. Especially at the point of access for distributed energy resources, due to the dynamic changes in power flow, the reflection coefficient ρ... i It will be updated in real time, so that the amplitude characteristics of the reflected wave also exhibit dynamic changes.

[0136] For example, for a typical distribution network branch node, assuming the fault point is 100 meters away from the node, and the updated traveling wave propagation speed is 2 × 10⁸ m / s, the theoretical reflection delay is 1 microsecond. If the incident wave amplitude is 1.0 per unit and the node's reflection coefficient is -0.3, then the theoretical reflected wave amplitude is -0.3 per unit. These theoretical characteristic values ​​provide an accurate reference standard for subsequent reflected wave identification. Especially in multi-branch lines, due to the existence of multiple reflection points, the calculated sets of theoretical characteristics can help distinguish reflected waves from different reflection points.

[0137] Step 403: Match the signal components of different frequencies with the theoretical characteristics of the reflected wave to determine the reflected wave signal.

[0138] Specifically, in new overhead distribution network lines, a scientific signal matching mechanism is needed to accurately distinguish between reflected wave components and fault traveling wave signals in traveling wave signals. This matching mechanism achieves accurate identification of reflected waves by quantitatively comparing the frequency components of the actual signal with the theoretical characteristics of the reflected wave. This is of great significance for improving fault location accuracy, especially in scenarios where distributed energy access leads to dynamic changes in signal characteristics.

[0139] Specifically, the embodiments of this application employ a matching algorithm based on similarity calculation. This algorithm uses traveling wave signals of each frequency component. Using theoretical reflected wave characteristics as input, the degree of matching between the two is evaluated through a complex metric function.

[0140] The formula for calculating similarity is:

[0141]

[0142] In the formula, e represents the frequency component to be evaluated. -j2πft The term A is used for frequency characteristic compensation. ref (tt ref The value represents the theoretical reflected wave characteristics, including amplitude and time delay properties. The above similarity calculation formula unifies time-domain characteristics, frequency-domain characteristics, and amplitude characteristics into a single measurement framework, enabling a comprehensive evaluation of the degree of matching between signal components and theoretical reflected waves.

[0143] Furthermore, to achieve automatic identification of reflected waves, this application introduces a similarity threshold ∈ as a judgment criterion. When the similarity calculation result of a certain frequency component is less than the threshold ∈, the component is determined to be a reflected wave signal. The selection of the threshold ∈ needs to strike a balance between sensitivity and reliability. Analysis of a large amount of experimental data shows that in typical overhead distribution lines, setting ∈ in the range of 0.15-0.25 can achieve good identification results. A smaller ∈ value will increase the strictness of identification and reduce the false judgment rate; a larger ∈ value will increase the sensitivity of identification, but may bring the risk of over-identification.

[0144] For example, when a certain frequency component The propagation delay characteristics and theoretical delay t ref It is close to the theoretical amplitude A, and its amplitude variation trend is similar to that of the theoretical amplitude A. ref When the match is high, the calculated similarity value will be low, indicating that the component is likely a reflected wave signal. Especially near distributed energy access points, the dynamic changes in power flow affect reflection characteristics. Matching with updated theoretical features can accurately capture these dynamic changes and improve the accuracy of identification.

[0145] Step 404: Remove the reflected wave signal from the traveling wave signal to obtain the target traveling wave signal.

[0146] In new overhead distribution networks, to ensure accurate fault location, it is necessary to extract the true fault characteristics from traveling wave signals containing various interferences. Due to the multi-branch structure of the lines and the dynamic integration of distributed energy sources, the original traveling wave signal x(t) often contains a large amount of reflected wave interference. These reflected waves superimpose with the fault traveling wave signal, interfering with the identification of fault characteristics. Therefore, scientific signal processing methods are needed to remove the reflected wave components from the original signal.

[0147] Specifically, in this embodiment, the reflected wave signal identified in the aforementioned steps is first synthesized and reconstructed. Based on the principle of linear superposition, the reflected wave signal with all frequency components is reconstructed. Accumulate:

[0148]

[0149] The cumulative summation comprehensively considers the distribution characteristics of the reflected wave across various frequency components, enabling accurate reconstruction of the complete reflected wave signal. Because each frequency component... All of these were determined by matching with theoretical characteristics, therefore the synthesized reflected wave signal x ref (t) has a high degree of credibility.

[0150] Furthermore, the reflected wave interference is filtered out from the original traveling wave signal using a signal subtraction method:

[0151] x tar (t)=x(t)-x ref (t);

[0152] Based on the linear superposition characteristic of traveling wave signals, the original characteristics of the fault traveling wave can be effectively restored by subtracting the identified reflected wave component. Because the reflected wave signal x... ref (t) is based on the updated traveling wave propagation model for identification and reconstruction, taking into account the influence of power flow on propagation characteristics. Therefore, the separated target signal x tar (t) can more accurately reflect the fault point information.

[0153] The system employs a signal processing approach to accurately remove interference components from traveling wave signals. First, it acquires signal components of different frequencies from the traveling wave signal, achieving effective frequency domain decomposition. Second, it calculates theoretical reflected wave characteristics based on an updated traveling wave propagation model, establishing a theoretical benchmark for reflected wave identification. Then, it matches the signal components of different frequencies with the theoretical reflected wave characteristics to accurately identify the reflected wave signals. Finally, by removing reflected wave signals from the traveling wave signal, the true target traveling wave signal is obtained. This progressive signal processing method fully utilizes the theoretical support provided by the updated traveling wave propagation model and achieves accurate identification of interference signals through frequency domain analysis and feature matching. The final target traveling wave signal accurately reflects the fault characteristics.

[0154] Step 104: Determine the fault point based on the target traveling wave signal.

[0155] In the process of traveling wave detection of faults in new distribution network overhead lines, accurately locating the fault point is the ultimate goal of the entire fault detection system. Due to the complex structure of distribution network overhead lines, especially after the integration of distributed energy resources, traditional fault location methods are difficult to adapt to dynamically changing network characteristics. Therefore, this application proposes a precise fault location method based on multi-sensor collaboration after completing interference signal filtering and obtaining the target traveling wave signal.

[0156] Based on the above embodiments, as an optional embodiment, step 104, the step of determining the fault point based on the target traveling wave signal, may further include the following steps:

[0157] Step 501: Obtain the target time of the target traveling wave signal collected by the sensor.

[0158] In the fault location process of new distribution network overhead lines, obtaining the initial arrival time of the traveling wave signal at the sensor is the foundation and key to fault location. Because the fault traveling wave signal undergoes multiple reflections and superpositions during propagation due to the multi-branch structure of the line and the access of distributed energy sources, these complex propagation phenomena pose a challenge to the accurate identification of the wavefront time. Especially in scenarios with dynamic power changes, traditional wavefront detection methods may result in misjudgments or omissions. Therefore, this application proposes a wavefront detection method based on the rate of energy change to accurately obtain the arrival time of the target traveling wave signal.

[0159] Specifically, firstly, the target traveling wave signal x collected by the sensor is acquired. tar (t). Considering the high-frequency characteristics of the traveling wave signal, the sampling frequency f s Sufficiently high f is required to ensure complete acquisition of signal characteristics. In practical applications, based on the frequency characteristics of traveling wave signals, f is typically... s Setting the frequency above 1MHz ensures accurate capture of microsecond-level changes in the traveling wave front. The accuracy of the sampling data and the choice of sampling frequency directly affect the temporal resolution of wavefront detection, and thus the accuracy of fault location.

[0160] Furthermore, in order to identify the arrival time of the wavefront of the traveling wave signal, embodiments of this application calculate the rate of change of energy of the target traveling wave signal:

[0161] Δx tar (t)=x tar (t)-x tar (t-1);

[0162] This calculation method based on the difference between adjacent sampling points can effectively amplify the abrupt changes in the signal, making the wavefront easier to detect. Compared with the traditional method of directly comparing signal amplitude, the calculation of the energy change rate is insensitive to the dynamic range of the signal and has better adaptability, especially showing a significant advantage in scenarios where distributed energy access causes fluctuations in the signal baseline.

[0163] After obtaining the energy change rate, the wavefront moment is identified by setting a reasonable detection threshold. The detection threshold in this embodiment needs to comprehensively consider the system's background noise level and practical engineering application requirements. When the energy change rate Δx tar (t) The moment when the wavefront first exceeds the set threshold is the wavefront arrival time. Where i represents the i-th sensor. To improve the reliability of detection, it can be required that the energy change rate of multiple consecutive sampling points exceeds a threshold before it is considered a valid wavefront detection. This can effectively avoid false triggering caused by random noise.

[0164] Step 502: Based on the position of the sensor and the corresponding target time, obtain the propagation delay of the target traveling wave signal.

[0165] In the fault location process of new distribution network overhead lines, accurate calculation of the propagation delay of traveling wave signals is a crucial link connecting wavefront detection and fault location. Because the propagation path of the traveling wave signal from the fault point to each sensor is different, and it is affected by distributed energy access and power flow, the propagation characteristics exhibit dynamic changes. Therefore, this application proposes a propagation delay calculation method based on multi-sensor collaboration. By establishing a mapping relationship between the fault location and sensor detection time, accurate calculation of the propagation delay is achieved.

[0166] Specifically, this method first obtains the installation location information d of each sensor. i These locations are typically calibrated relative to the starting point of the line and expressed as relative distances. To ensure positioning accuracy, the sensor installation positions need to be precisely measured and calibrated. Simultaneously, the arrival time of the wavefront detected by each sensor is acquired. These time data have been processed by the wavefront detection algorithm described in the previous steps, and have a high time resolution.

[0167] Furthermore, assume the location of the fault is d. f According to the traveling wave propagation theory, the theoretical time delay required for a traveling wave to propagate from the fault point to sensor i can be expressed as:

[0168]

[0169] Where v represents the propagation velocity in the updated traveling wave propagation model, which already accounts for the influence of power flow on propagation characteristics. Because traveling wave signals exhibit bidirectional propagation in overhead distribution lines, the scenario where the fault point is on either side of the sensor needs to be considered. Therefore, the absolute value operation |d| is used in the above calculation formula. f -d i |

[0170] To improve the accuracy of time delay calculation, this application adopts a relative time delay calculation method. A suitable reference time t0 is selected (usually the time when the earliest wavefront is detected), and the wavefront arrival times of each sensor are converted into relative time delays:

[0171]

[0172] The above method for calculating relative delay has significant advantages: First, it eliminates the influence of common system errors, such as clock synchronization errors; second, the calculation of relative delay reduces the accuracy requirements of absolute time measurement; and finally, this method facilitates the use of subsequent fault location algorithms.

[0173] Step 503: Determine the fault point based on the propagation delay and the traveling wave propagation speed in the updated traveling wave propagation model.

[0174] In the fault location process of new distribution network overhead lines, accurately determining the fault location based on propagation delay and traveling wave propagation speed is the ultimate goal of the entire fault detection system. Due to the multi-branch structure of distribution network overhead lines and the dynamic changes in power flow caused by the integration of distributed energy sources, traditional single-point location methods are insufficient to meet the requirements for accurate location. Therefore, this application proposes a multi-point collaborative fault location method that combines a dynamic propagation model.

[0175] In one feasible implementation, for the most basic two-end ranging scenario, assume that the installation positions of sensors A and B are d respectively. A and d B The relative propagation delay they detected is Δt A and Δt B By combining the propagation velocity v in the updated traveling wave propagation model, the location of the fault point can be calculated using the time delay difference localization formula:

[0176]

[0177] In the formula, the time delay difference reflects the distance difference between the fault point and the two sensors. The time difference is converted into a distance difference by the traveling wave propagation speed v, and the location of the fault point can be determined by combining the sensor position information.

[0178] In another feasible implementation, considering that new overhead power distribution lines typically have multiple monitoring points, this application embodiment constructs a system of nonlinear equations based on multiple sensors. For the case of N sensors, the relationship between each sensor and the fault point can be expressed as:

[0179]

[0180] The above equation reflects the location d of the fault point. f With each sensor position d i and the corresponding propagation delay Δt i The mathematical relationship between them. Due to the nonlinear characteristics of the system of equations, the least squares method combined with an iterative optimization algorithm is used to solve it.

[0181] In solving this optimization problem, special attention needs to be paid to the selection of initial values. This embodiment uses the distance measurements at both ends as the initial values ​​for iteration. This selection method ensures both the convergence of the calculation and improves the solution speed. Simultaneously, considering the physical constraints of the actual line, the following constraint is added to the solution process: 0 ≤ df ≤ L, where L is the total length of the line. This avoids physically unreasonable solutions.

[0182] The system's fault location process achieves precise fault point determination. First, the target time of the target traveling wave signal acquired by the sensors is obtained, ensuring the accuracy of wavefront detection. Then, based on the sensor position and the corresponding target time, the propagation delay of the target traveling wave signal is obtained, establishing a correlation between the fault location and signal propagation characteristics. Finally, the fault location is determined based on the propagation delay and the traveling wave propagation velocity in the updated traveling wave propagation model. This multi-sensor collaborative location method not only fully utilizes the accurate propagation velocity parameters provided by the updated traveling wave propagation model but also establishes a mapping relationship between the fault point and observation data through precise calculation of the propagation delay. This overcomes the accuracy limitations of traditional location methods in distributed energy access scenarios, achieving accurate fault point location determination.

[0183] Reference Figure 2 This application also provides a novel traveling wave detection system for faults in overhead distribution lines, comprising:

[0184] The signal acquisition module is used to acquire traveling wave signals and power signals collected by sensors installed at key nodes in the new distribution network overhead line when a fault is detected.

[0185] The model update module is used to update the traveling wave propagation model based on the power signal. The traveling wave propagation model is used to characterize the propagation characteristics of the traveling wave in the line topology corresponding to the new type of distribution network overhead line.

[0186] The signal filtering module is used to filter out interference signals in the traveling wave signal based on the updated traveling wave propagation model to obtain the target traveling wave signal.

[0187] The fault point determination module is used to determine the fault point based on the target traveling wave signal.

[0188] Based on the above embodiments, as an optional embodiment, the traveling wave detection system for faults in the novel distribution network overhead line may further include a model building module, used to obtain the line topology of the novel distribution network overhead line, the line topology including the length of the line segment, impedance and the location of key nodes; and to generate a traveling wave propagation model based on the line topology.

[0189] Based on the above embodiments, as an optional embodiment, the model update module is further used to adjust the propagation speed, line impedance, and reflection coefficient of the traveling wave propagation model based on the power signal; wherein, the propagation speed is positively correlated with the power flow characteristics of the line topology; the line impedance is used to characterize the impedance change of the line topology under distributed energy access or dynamic power flow; and the reflection coefficient is used to characterize the reflection characteristics of the traveling wave signal at the critical node.

[0190] Based on the above embodiments, as an optional embodiment, the model update module is further configured to adjust the propagation speed of the traveling wave propagation model based on the flow characteristics of the power signal; adjust the line impedance of the traveling wave propagation model based on the changes in current amplitude and voltage value in the power signal; and determine the reflection coefficient corresponding to the key node based on the adjusted line impedance.

[0191] Based on the above embodiments, as an optional embodiment, the signal filtering module is further configured to acquire signal components of different frequencies in the traveling wave signal; calculate theoretical reflected wave characteristics based on the updated traveling wave propagation model; match the signal components of different frequencies with the theoretical reflected wave characteristics to determine the reflected wave signal; and filter out the reflected wave signal in the traveling wave signal to obtain the target traveling wave signal.

[0192] Based on the above embodiments, as an optional embodiment, the fault point determination module is further configured to obtain the target time of the target traveling wave signal collected by the sensor; obtain the propagation delay of the target traveling wave signal based on the position of the sensor and the corresponding target time; and determine the fault point according to the propagation delay and the traveling wave propagation speed in the updated traveling wave propagation model.

[0193] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0194] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded and executed by a processor using the novel traveling wave detection method for overhead power line faults in the above embodiments. The specific execution process can be referred to the detailed description of the embodiments shown, which will not be repeated here.

[0195] This application also discloses an electronic device. (See reference...) Figure 3, Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0196] The communication bus 302 is used to enable communication between these components.

[0197] The user interface 303 may include a display interface, and optionally, the user interface 303 may also include a standard wired interface or a wireless interface.

[0198] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0199] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 301.

[0200] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a novel traveling wave detection method for faults in overhead power distribution lines.

[0201] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a novel traveling wave detection method for overhead power line faults in a distribution network. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0202] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the novel traveling wave detection method for overhead power line faults in the above-mentioned methods.

[0203] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0204] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0206] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0207] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0208] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0209] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A novel traveling wave detection method for faults in overhead distribution lines, characterized in that, include: In the event of a fault detected in a new type of overhead distribution network line, the traveling wave signal and power signal collected by sensors installed at key nodes in the new type of overhead distribution network line are obtained. The traveling wave propagation model is updated based on the power signal. The traveling wave propagation model is used to characterize the propagation characteristics of the traveling wave in the line topology corresponding to the new type of distribution network overhead line. The target traveling wave signal is obtained by filtering out interference signals from the traveling wave signal based on the updated traveling wave propagation model. The fault point is determined based on the target traveling wave signal.

2. The novel traveling wave detection method for overhead distribution line faults according to claim 1, characterized in that, The process of constructing the traveling wave propagation model includes: Obtain the line topology of the new type of overhead distribution network, the line topology including the length of the line segment, impedance and the location of key nodes; Based on the aforementioned line topology, a traveling wave propagation model is generated.

3. The novel traveling wave detection method for overhead distribution line faults according to claim 1, characterized in that, The updating of the traveling wave propagation model based on the power signal includes: The propagation speed, line impedance, and reflection coefficient of key nodes of the traveling wave propagation model are adjusted based on the power signal. The propagation speed is positively correlated with the power flow characteristics of the line topology; The line impedance is used to characterize the impedance change of the line topology under distributed energy access or dynamic power flow. The reflection coefficient is used to characterize the reflection characteristics of traveling wave signals at critical nodes.

4. The traveling wave detection method for faults in overhead distribution lines according to claim 3, characterized in that, The adjustment of the propagation speed, line impedance, and reflection coefficient of key nodes of the traveling wave propagation model based on the power signal includes: Based on the flow characteristics of the power signal, the propagation speed of the traveling wave propagation model is adjusted; Based on the changes in current amplitude and voltage value in the power signal, the line impedance of the traveling wave propagation model is adjusted; Based on the adjusted line impedance, the reflection coefficient corresponding to the key node is determined.

5. The novel traveling wave detection method for overhead distribution line faults according to claim 1, characterized in that, The step of filtering out interference signals from the traveling wave signal based on the updated traveling wave propagation model to obtain the target traveling wave signal includes: Obtain the signal components of different frequencies in the traveling wave signal; The theoretical characteristics of the reflected wave are calculated based on the updated traveling wave propagation model. The signal components of different frequencies are matched with the theoretical reflected wave characteristics to determine the reflected wave signal; The reflected wave signal in the traveling wave signal is filtered out to obtain the target traveling wave signal.

6. The novel traveling wave detection method for overhead distribution line faults according to claim 1, characterized in that, The method of determining the fault point based on the target traveling wave signal includes: The target time for acquiring the target traveling wave signal corresponding to the sensor is obtained; Based on the position of the sensor and the corresponding target time, the propagation delay of the target traveling wave signal is obtained; The fault point is determined based on the propagation delay and the traveling wave propagation speed in the updated traveling wave propagation model.

7. A novel traveling wave detection system for faults in overhead distribution lines, characterized in that, include: The signal acquisition module is used to acquire traveling wave signals and power signals collected by sensors installed at key nodes in the new distribution network overhead line when a fault is detected. The model update module is used to update the traveling wave propagation model based on the power signal. The traveling wave propagation model is used to characterize the propagation characteristics of the traveling wave in the line topology corresponding to the new type of distribution network overhead line. The signal filtering module is used to filter out interference signals in the traveling wave signal based on the updated traveling wave propagation model to obtain the target traveling wave signal. The fault point determination module is used to determine the fault point based on the target traveling wave signal.

8. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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