A method and device for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction.

Abstract

This invention relates to a dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction, comprising: synchronously acquiring conductor temperature at the fault location; calculating the measured traveling wave propagation velocity of each line segment; establishing and updating a wave velocity-temperature mapping model online; obtaining the equivalent traveling wave propagation velocity at the fault moment; calculating the distance from the fault point to the distributed monitoring node; outputting the tower segment where the fault is located and the distance to the nearest tower, and simultaneously outputting the location confidence interval. This invention directly measures the wave velocity without relying on any theoretical formulas for conductor parameters, fundamentally eliminating modeling errors; achieving continuous correction around the clock; realizing online automatic detection of wave velocity drift, timely alarming and preventing abnormal data from polluting the model, ensuring long-term location accuracy; requiring no additional acquisition equipment, exhibiting good engineering economics; and outputting location results with confidence intervals to assist maintenance personnel in accurately judging line patrols.

Description

Technical Field

[0001] This invention relates to the field of power system transmission line fault diagnosis technology, and in particular to a method and device for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction. Background Technology

[0002] Overhead transmission lines are the core transmission backbone of the power system, and rapid and accurate fault location is crucial for shortening power outage time and reducing economic losses. The double-ended traveling wave fault location method is widely used in high-voltage and ultra-high-voltage transmission lines due to its advantages such as being unaffected by system operating conditions and high location accuracy. However, the double-ended traveling wave fault location method has the following significant drawbacks:

[0003] First, the traveling wave velocity is fixed: existing traveling wave positioning devices generally fix the traveling wave velocity of overhead lines at approximately [value missing]. (about (speed of light), completely ignoring the effect of conductor temperature changes on wave speed, under high load current or winter-summer temperature difference exceeding In such scenarios, the systematic positioning error can exceed ;

[0004] Second, the wave velocity calibration lacks real-time capability: some devices use offline or periodic manual testing to obtain wave velocity correction values, and the calibration cycle is usually several months to a year. This cannot reflect the real-time operating temperature of the line, and the correction effect is limited when the weather changes suddenly or when there is a large load impact.

[0005] Third, existing wave velocity corrections rely on theoretical models: existing schemes calculate wave velocity corrections using sag and temperature theoretical formulas, but due to uncertainties in conductor parameters, the calculation error is typically within [a certain range]. The magnitude of the error is still significant for long-distance lines, and its accuracy cannot be verified through actual measurements.

[0006] Fourth, the lack of a closed-loop verification mechanism: the existing system cannot automatically verify the accuracy of the wave velocity parameters online. Once the wave velocity value used drifts, the positioning error will continue to accumulate and is difficult to detect and correct automatically.

[0007] Therefore, there is an urgent need to propose a new fault location method that utilizes existing distributed monitoring nodes, directly measures the traveling wave propagation speed by injecting calibration pulses, and establishes a dynamic mapping model between wave speed and conductor temperature, so as to fundamentally solve the location error problem caused by inaccurate wave speed. Summary of the Invention

[0008] To address the systematic positioning errors caused by the fixed traveling wave velocity and lack of a real-time correction mechanism in existing technologies, the primary objective of this invention is to provide a dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction, which directly measures the wave velocity, eliminates theoretical model errors, establishes a wave velocity-temperature mapping, and achieves continuous correction around the clock.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction, the method comprising the following sequential steps:

[0010] (1) Multiple distributed monitoring nodes are set up along the transmission line at preset intervals, and each distributed monitoring node synchronously collects the conductor temperature at its location. and current traveling wave signal ;

[0011] (2) At a preset time interval, a calibration pulse signal is injected into the transmission line at a designated location by a calibration pulse generator, and the measured traveling wave propagation velocity of each line segment is calculated. and equivalent traveling wave propagation speed along the entire line ;

[0012] (3) The conductor temperature of each distributed monitoring node The independent variable is the measured traveling wave propagation speed of the corresponding line segment. As the dependent variable, the least squares method is used for linear regression to establish a wave velocity-temperature mapping model and update it online;

[0013] (4) When any distributed monitoring node detects a sudden change in the fault traveling wave, calculate the average temperature across the entire line. Obtain the equivalent traveling wave propagation speed at the moment of failure. ;

[0014] (5) Perform phase mode transformation and wavelet transform on the current traveling wave signal of each distributed monitoring node to extract the time when the fault traveling wave arrives at each distributed monitoring node. Select the nearest distributed monitoring nodes at both ends of the fault range. and Calculate the distance from the fault point to the distributed monitoring node. distance :

[0015] (6) Distance With towers The database comparison outputs the tower segment where the fault is located and the distance to the nearest tower, as well as the location information interval.

[0016] Step (1) specifically refers to: along the 500kV overhead transmission line, deploying distributed monitoring nodes at intervals of 15 to 25km, denoted as... Adjacent nodes and Line length between Depend on The database provides the following: The distributed monitoring nodes are deployed along each tower of the transmission line. Each distributed monitoring node includes a dual-range Rogowski coil current sensor, a conductor temperature sensor, a first signal conditioning circuit, a second signal conditioning circuit, a high-speed ADC, a low-speed ADC, an edge computing processing unit, a wireless communication module, and a BeiDou / GPS module. The output of the dual-range Rogowski coil current sensor is connected to the input of the first signal conditioning circuit, and the output of the first signal conditioning circuit is connected to the input of the high-speed ADC. The conductor temperature sensor is connected to the input of the second signal conditioning circuit, and the output of the second signal conditioning circuit is connected to the input of the low-speed ADC. The outputs of both the high-speed and low-speed ADCs are connected to the input of the edge computing processing unit, and the output of the edge computing processing unit communicates bidirectionally with the wireless communication module and the BeiDou / GPS module, respectively.

[0017] Step (2) specifically includes the following steps in sequence:

[0018] (2a) Distributed monitoring nodes at the substation at the beginning of the line A calibration pulse generator is installed at the location to inject calibration pulse signals into the line at preset intervals. The parameters of the calibration pulse signal include rise time, amplitude, duration, and peak value of the pulse spectrum; the rise time is less than or equal to... The amplitude is the rated current. The duration is The peak of the pulse spectrum is concentrated in ;

[0019] (2b) Each distributed monitoring node detects the calibration pulse characteristics in real time, and records the precise arrival time when the calibration pulse is detected. And report to the monitoring center via 4G / 5G;

[0020] (2c) After receiving the time data from each distributed monitoring node, the monitoring center calculates the adjacent nodes. and The propagation delay of the calibration pulse between the two points is used to calculate the measured traveling wave propagation velocity of each line segment using the following formula. :

[0021] ;

[0022] ;

[0023] In the formula, Adjacent nodes and The length of the line between them; The time difference of arrival;

[0024] (2d) Calculate the equivalent traveling wave propagation velocity along the entire line. :

[0025] ;

[0026] In the formula, n is the total number of distributed monitoring nodes; This represents the total number of distributed monitoring nodes. ∈n.

[0027] Step (3) specifically includes the following steps in sequence:

[0028] (3a) After each calibration process is completed, the average conductor temperature of the entire line is recorded. Equivalent wave velocity across the entire line as measured Composition data pairs Data pairs Create a dataset and store the dataset. Database; Average conductor temperature across the entire line The ambient temperature measured by each distributed monitoring node The mean;

[0029] (3b) When the accumulated data is... quantity When the value is greater than or equal to 30, the least squares method is used to perform linear regression on the dataset to establish a wave speed-temperature mapping model:

[0030] ;

[0031] In the formula: For reference temperature; Reference temperature The reference wave velocity below; This is a wave velocity temperature correction factor, reflecting the change in conductor temperature. The change in the wave velocity of the traveling wave; The ambient temperature is The wave velocity corresponding to the time;

[0032] (3c) Subsequently, each time new calibration data is added, the sliding window least squares method is used to update the data. and This enables the wave velocity temperature mapping model to continuously track the long-term aging and seasonal changes of the line;

[0033] (3d) If the measured equivalent wave velocity of the entire line in a certain calibration is... Compared with model predictions deviation Exceeding the threshold This will trigger a wave speed anomaly alarm. for The data was marked as suspicious and excluded from the regression update. Simultaneously, maintenance personnel were notified to check the line status. (Model predicted value) It is The input is obtained from the wave velocity-temperature mapping model;

[0034] The calculation formula is:

[0035] .

[0036] Step (4) specifically refers to: when a line fault occurs, reading the real-time conductor temperature of each distributed monitoring node at the time of fault triggering. Calculate the average temperature of the entire line. :

[0037] ;

[0038] In the formula, This represents the total number of distributed monitoring nodes.

[0039] Will Substitute the wave velocity-temperature mapping model to calculate the equivalent traveling wave propagation velocity at the moment of failure. :

[0040] ;

[0041] In the formula, Reference temperature The reference wave velocity below; This is the wave velocity temperature correction factor.

[0042] Step (5) specifically includes the following steps in sequence:

[0043] (5a) Phase mode transformation: Performed on three-phase current Transformation to extract the traveling wave component of the linear mode. Suppress common-mode interference;

[0044] (5b) Wavelet transform: for linear mode traveling wave components implement Fourth-order wavelet 5-level decomposition, detecting modulus maxima in the 3rd to 5th decomposition levels;

[0045] (5c) Using the current root mean square value of the power frequency current Based on this, set the traveling wave trigger threshold. :

[0046] ;

[0047] In the formula, For coefficients, ;

[0048] (5d) The first one to be satisfied The absolute timestamp of the BeiDou / GPS module corresponding to the modulus maximum point The accuracy of the BeiDou / GPS module in determining the arrival time of the traveling wave is better than that of the GPS module. ; for In the 4th order wavelet 5-level decomposition, the modulus maxima are detected in the decomposition layers;

[0049] (5e) Let the nearest distributed monitoring node at both ends of the fault interval be the distributed monitoring node. and distributed monitoring Distributed monitoring nodes and distributed monitoring The length of the line between them is ,but:

[0050] Distance of fault point from distributed monitoring node distance for:

[0051] ;

[0052] In the formula, , The arrival of the traveling wave head at the distributed monitoring node is respectively , The moment;

[0053] (5f) Distance of the fault point from the distributed monitoring node distance for:

[0054] ;

[0055] (5g) Legality verification: If If the location result is positive, the location result is valid; otherwise, it is extended to the adjacent interval for re-evaluation.

[0056] Another object of the present invention is to provide an electronic device comprising:

[0057] Processor; and

[0058] The memory stores computer program instructions that, when executed by the processor, cause the processor to perform the dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction as described above.

[0059] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the power transmission line fault dynamic location method based on real-time temperature feedback and wave velocity correction as described above.

[0060] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, direct measurement of wave velocity eliminates theoretical model errors: by injecting calibration pulses online and measuring the propagation delay between adjacent nodes, the actual traveling wave velocity is directly obtained without relying on any theoretical formulas for conductor parameters, fundamentally eliminating modeling errors; compared with existing theoretical calculation schemes, the measured accuracy is more than an order of magnitude higher; Second, establishing a wave velocity-temperature mapping enables continuous correction in all weather conditions: by using historical calibration data to establish a linear mapping model, even between two calibration cycles, the accurate wave velocity can be calculated in real time based on the real-time temperature, achieving continuous correction in all weather conditions. First, it provides continuous dynamic correction around the clock; second, it establishes a closed-loop verification and alarm mechanism: each calibration automatically compares the measured wave velocity with the predicted value of the wave velocity-temperature mapping model, realizing online automatic detection of wave velocity drift, timely alarming and preventing abnormal data from polluting the model, and ensuring long-term positioning accuracy; third, it fully reuses existing infrastructure: no additional acquisition equipment is required, which has good engineering economics; fourth, it provides quantitative measurement of positioning uncertainty: comprehensively considering wave velocity measurement error, time synchronization error, and GIS line length error, it outputs positioning results with confidence intervals to assist maintenance personnel in accurately judging line patrols. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention;

[0062] Figure 2 This is a structural block diagram of a distributed monitoring node.

[0063] Figure 3 This is a flowchart of the method of the present invention;

[0064] Figure 4 This is a schematic diagram of the wave velocity-temperature scatter plot and linear mapping curve. Detailed Implementation

[0065] like Figure 3 As shown, a dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction is described. This method includes the following sequential steps:

[0066] (1) Multiple distributed monitoring nodes are set up along the transmission line at preset intervals, and each distributed monitoring node synchronously collects the conductor temperature at its location. and current traveling wave signal ; conductor temperature Current traveling wave signal acquired by wire temperature sensor Data is collected by a dual-range Rogowski coil current sensor;

[0067] (2) At a preset time interval, a calibration pulse signal is injected into the transmission line at a designated location by a calibration pulse generator, and the measured traveling wave propagation velocity of each line segment is calculated. and equivalent traveling wave propagation speed along the entire line ;

[0068] (3) The conductor temperature of each distributed monitoring node The independent variable is the measured traveling wave propagation speed of the corresponding line segment. As the dependent variable, the least squares method is used for linear regression to establish a wave velocity-temperature mapping model and update it online;

[0069] (4) When any distributed monitoring node detects a sudden change in the fault traveling wave, calculate the average temperature across the entire line. Obtain the equivalent traveling wave propagation speed at the moment of failure. ;

[0070] (5) Perform phase mode transformation and wavelet transform on the current traveling wave signal of each distributed monitoring node to extract the time when the fault traveling wave arrives at each distributed monitoring node. Select the nearest distributed monitoring nodes at both ends of the fault range. and Calculate the distance from the fault point to the distributed monitoring node. distance :

[0071] (6) Distance With towers The database comparison outputs the faulty tower segment and its distance to the nearest tower, along with the location information interval. (Tower) The database stores the coordinates of each tower, the length of the line segment, and the cumulative mileage, for the conversion and verification of positioning results.

[0072] Step (1) specifically refers to: such as Figure 1 As shown, distributed monitoring nodes are deployed along the 500kV overhead transmission line at intervals of 15 to 25km, denoted as... Adjacent nodes and Line length between Depend on The database provides communication between distributed monitoring nodes and a central station, which serves as a monitoring center and communicates with a cloud platform. For example... Figure 2As shown, the distributed monitoring nodes are deployed along each tower of the transmission line. Each distributed monitoring node includes a dual-range Rogowski coil current sensor, a conductor temperature sensor, a first signal conditioning circuit, a second signal conditioning circuit, a high-speed ADC, a low-speed ADC, an edge computing processing unit, a wireless communication module, and a BeiDou / GPS module. The output terminal of the dual-range Rogowski coil current sensor is connected to the input terminal of the first signal conditioning circuit, and the output terminal of the first signal conditioning circuit is connected to the input terminal of the high-speed ADC. The conductor temperature sensor is connected to the input terminal of the second signal conditioning circuit, and the output terminal of the second signal conditioning circuit is connected to the input terminal of the low-speed ADC. The output terminals of both the high-speed and low-speed ADCs are connected to the input terminal of the edge computing processing unit, and the output terminal of the edge computing processing unit communicates bidirectionally with the wireless communication module and the BeiDou / GPS module, respectively.

[0073] The measurement range of the wire temperature sensor is: The accuracy is The collection cycle is The timing accuracy of the BeiDou / GPS module is better than that of the GPS module. The event-triggered wake-up time is less than or equal to The edge computing processing unit uses an ARM Cortex-A55 processor with a main frequency of 1.2GHz and supports real-time wavelet transform processing.

[0074] Step (2) specifically includes the following steps in sequence:

[0075] (2a) Distributed monitoring nodes at the substation at the beginning of the line A calibration pulse generator is installed at the location to inject calibration pulse signals into the line at preset intervals, with the default interval being once every 6 hours. The parameters of the calibration pulse signal include rise time, amplitude, duration, and peak value of the pulse spectrum; the rise time is less than or equal to... The amplitude is the rated current. The duration is The peak of the pulse spectrum is concentrated in ;

[0076] (2b) Each distributed monitoring node detects the calibration pulse characteristics in real time, and records the precise arrival time when the calibration pulse is detected. And report to the monitoring center via 4G / 5G;

[0077] (2c) After receiving the time data from each distributed monitoring node, the monitoring center calculates the adjacent nodes. and The propagation delay of the calibration pulse between the two points is used to calculate the measured traveling wave propagation velocity of each line segment using the following formula. :

[0078] ;

[0079] ;

[0080] In the formula, Adjacent nodes and The length of the line between them; The time difference of arrival;

[0081] (2d) Calculate the equivalent traveling wave propagation velocity along the entire line. :

[0082] ;

[0083] In the formula, n is the total number of distributed monitoring nodes; This represents the total number of distributed monitoring nodes. ∈n.

[0084] Numerical examples illustrate: a certain Line length between adjacent nodes The propagation delay between nodes, i.e., the time difference of arrival, was measured during a certain calibration. ,but:

[0085] ;

[0086] Compared with standard reference wave speed The deviation is only about The positioning error for this segment does not exceed .

[0087] Step (3) specifically includes the following steps in sequence:

[0088] (3a) After each calibration process is completed, the average conductor temperature of the entire line is recorded. Equivalent wave velocity across the entire line as measured Composition data pairs Data pairs Create a dataset and store the dataset. Database; Average conductor temperature across the entire line The ambient temperature measured by each distributed monitoring node The mean;

[0089] A calibration process includes: First, at a certain temperature, a pulse signal is injected into the line, and the average conductor temperature of the entire line is calculated based on the monitoring data from the distributed monitoring nodes. Equivalent wave velocity across the entire line as measured The second step is to repeat the first step at different temperatures to cover the actual operating temperature of the line as much as possible. The third step is to establish a wave velocity-temperature mapping model based on the dataset formed by the above two steps.

[0090] (3b) When the accumulated data is... quantity When the value is greater than or equal to 30, the least squares method is used to perform linear regression on the dataset to establish a wave speed-temperature mapping model:

[0091] ;

[0092] In the formula: For reference temperature; Reference temperature The reference wave velocity below; This is a wave velocity temperature correction factor, reflecting the change in conductor temperature. The change in the wave velocity of the traveling wave; The ambient temperature is The wave velocity corresponding to the time; in Figure 4 In the diagram, v represents the wave velocity, the blue dot represents the measured wave velocity at a certain temperature, and the red line is the fitted model, i.e., the wave velocity-temperature mapping model.

[0093] (3c) Subsequently, each time new calibration data is added, the sliding window least squares method is used to update the data. and This enables the wave velocity temperature mapping model to continuously track the long-term aging and seasonal changes of the line; the newly added calibration data refers to the need to add calibration data points as the seasonal temperature changes.

[0094] (3d) If the measured equivalent wave velocity of the entire line in a certain calibration is... Compared with model predictions deviation Exceeding the threshold This will trigger a wave speed anomaly alarm. for The data was marked as suspicious and excluded from the regression update. Simultaneously, maintenance personnel were notified to check the line status. (Model predicted value) It is The input is obtained from the wave velocity-temperature mapping model;

[0095] The calculation formula is:

[0096] .

[0097] Step (4) specifically refers to: when a line fault occurs, reading the real-time conductor temperature of each distributed monitoring node at the time of fault triggering. Calculate the average temperature of the entire line. :

[0098] ;

[0099] In the formula, This represents the total number of distributed monitoring nodes.

[0100] Will Substitute the wave velocity-temperature mapping model to calculate the equivalent traveling wave propagation velocity at the moment of failure. :

[0101] ;

[0102] In the formula, Reference temperature The reference wave velocity below; This is a wave velocity temperature correction factor, reflecting the change in conductor temperature. The change in the wave velocity of a traveling wave, typically on the order of magnitude of Reference temperature Pick . It comprehensively reflects the impact of the instantaneous line temperature state on the traveling wave velocity during a fault, and is a key parameter for achieving high-precision positioning.

[0103] Step (5) specifically includes the following steps in sequence:

[0104] (5a) Phase mode transformation: Performed on three-phase current Transformation to extract the traveling wave component of the linear mode. Suppress common-mode interference;

[0105] (5b) Wavelet transform: for linear mode traveling wave components implement Fourth-order wavelet 5-level decomposition, detecting modulus maxima in the 3rd to 5th decomposition levels;

[0106] (5c) Using the current root mean square value of the power frequency current Based on this, set the traveling wave trigger threshold. :

[0107] ;

[0108] In the formula, For coefficients, ;

[0109] (5d) The first one to be satisfied The absolute timestamp of the BeiDou / GPS module corresponding to the modulus maximum point The accuracy of the BeiDou / GPS module in determining the arrival time of the traveling wave is better than that of the GPS module. ; for In the 4th order wavelet 5-level decomposition, the modulus maxima are detected in the decomposition layers;

[0110] (5e) Let the nearest distributed monitoring node at both ends of the fault interval be the distributed monitoring node. and distributed monitoring Distributed monitoring nodes and distributed monitoring The length of the line between them is ,but:

[0111] Distance of fault point from distributed monitoring node distance for:

[0112] ;

[0113] In the formula, , The arrival of the traveling wave head at the distributed monitoring node is respectively , The moment;

[0114] (5f) Distance of the fault point from the distributed monitoring node distance for:

[0115] ;

[0116] (5g) Legality verification: If If the location result is positive, the location result is valid; otherwise, it is extended to the adjacent interval for re-evaluation.

[0117] In summary, this invention directly measures wave velocity, eliminating theoretical model errors: by injecting calibration pulses online and measuring the propagation delay between adjacent nodes, the actual traveling wave velocity is directly obtained without relying on any theoretical formulas for conductor parameters, fundamentally eliminating modeling errors; compared with existing theoretical calculation schemes, the measured accuracy is more than an order of magnitude higher; a wave velocity-temperature mapping is established to achieve continuous correction around the clock: using historical calibration data to establish a linear mapping model, even between two calibration cycles, the accurate wave velocity can be calculated in real time based on the real-time temperature, achieving continuous dynamic correction around the clock; a closed-loop verification and alarm mechanism is formed: the measured wave velocity is automatically compared with the predicted value of the wave velocity-temperature mapping model each time, realizing online automatic detection of wave velocity drift, timely alarm and preventing abnormal data from polluting the model, ensuring long-term positioning accuracy; it fully reuses existing infrastructure: no additional acquisition equipment is required, resulting in good engineering economy; and it provides quantitative measurement of positioning uncertainty: comprehensively considering wave velocity measurement error, time synchronization error, and GIS line length error, it outputs positioning results with confidence intervals to assist maintenance personnel in accurately judging line inspections.

[0118] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction, characterized in that: The method includes the following steps in sequence: (1) Multiple distributed monitoring nodes are set up along the transmission line at preset intervals, and each distributed monitoring node synchronously collects the conductor temperature at its location. and current traveling wave signal ; (2) At a preset time interval, a calibration pulse signal is injected into the transmission line at a designated location by a calibration pulse generator, and the measured traveling wave propagation velocity of each line segment is calculated. and equivalent traveling wave propagation speed along the entire line ; (3) The conductor temperature of each distributed monitoring node The independent variable is the measured traveling wave propagation speed of the corresponding line segment. As the dependent variable, the least squares method is used for linear regression to establish a wave velocity-temperature mapping model and update it online; (4) When any distributed monitoring node detects a sudden change in the fault traveling wave, calculate the average temperature across the entire line. Obtain the equivalent traveling wave propagation speed at the moment of failure. ; (5) Perform phase mode transformation and wavelet transform on the current traveling wave signal of each distributed monitoring node to extract the time when the fault traveling wave arrives at each distributed monitoring node. ; Select the nearest distributed monitoring nodes at both ends of the fault range and Calculate the distance from the fault point to the distributed monitoring node. distance : (6) Distance With towers The database comparison outputs the tower segment where the fault is located and the distance to the nearest tower, as well as the location information interval.

2. The method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction according to claim 1, characterized in that: Step (1) specifically refers to: along the 500kV overhead transmission line, deploying distributed monitoring nodes at intervals of 15 to 25km, denoted as... Adjacent nodes and Line length between Depend on The database provides the following: The distributed monitoring nodes are deployed along each tower of the transmission line. Each distributed monitoring node includes a dual-range Rogowski coil current sensor, a conductor temperature sensor, a first signal conditioning circuit, a second signal conditioning circuit, a high-speed ADC, a low-speed ADC, an edge computing processing unit, a wireless communication module, and a BeiDou / GPS module. The output of the dual-range Rogowski coil current sensor is connected to the input of the first signal conditioning circuit, and the output of the first signal conditioning circuit is connected to the input of the high-speed ADC. The conductor temperature sensor is connected to the input of the second signal conditioning circuit, and the output of the second signal conditioning circuit is connected to the input of the low-speed ADC. The outputs of both the high-speed and low-speed ADCs are connected to the input of the edge computing processing unit, and the output of the edge computing processing unit communicates bidirectionally with the wireless communication module and the BeiDou / GPS module, respectively.

3. The method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction according to claim 1, characterized in that: Step (2) specifically includes the following steps in sequence: (2a) Distributed monitoring nodes at the substation at the beginning of the line A calibration pulse generator is installed at the location to inject calibration pulse signals into the line at preset intervals. The parameters of the calibration pulse signal include rise time, amplitude, duration, and peak value of the pulse spectrum. The rise time is less than or equal to The amplitude is the rated current. The duration is The peak of the pulse spectrum is concentrated in ; (2b) Each distributed monitoring node detects the calibration pulse characteristics in real time, and records the precise arrival time when the calibration pulse is detected. And report to the monitoring center via 4G / 5G; (2c) After receiving the time data from each distributed monitoring node, the monitoring center calculates the adjacent nodes. and The propagation delay of the calibration pulse between the two points is used to calculate the measured traveling wave propagation velocity of each line segment using the following formula. : ； ； In the formula, Adjacent nodes and The length of the line between them; The time difference of arrival; (2d) Calculate the equivalent traveling wave propagation velocity along the entire line. : ； In the formula, n is the total number of distributed monitoring nodes; This represents the total number of distributed monitoring nodes. ∈n.

4. The method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction according to claim 1, characterized in that: Step (3) specifically includes the following steps in sequence: (3a) After each calibration process is completed, the average conductor temperature of the entire line is recorded. Equivalent wave velocity across the entire line as measured Composition data pairs Data pairs Create a dataset and store the dataset. Database; Average conductor temperature across the entire line The ambient temperature measured by each distributed monitoring node The mean; (3b) When the accumulated data is... quantity When the value is greater than or equal to 30, the least squares method is used to perform linear regression on the dataset to establish a wave speed-temperature mapping model: ； In the formula: For reference temperature; Reference temperature The reference wave velocity below; This is a wave velocity temperature correction factor, reflecting the change in conductor temperature. The change in the wave velocity of the traveling wave; The ambient temperature is The wave velocity corresponding to the time; (3c) Subsequently, each time new calibration data is added, the sliding window least squares method is used to update the data. and This enables the wave velocity temperature mapping model to continuously track the long-term aging and seasonal changes of the line; (3d) If the measured equivalent wave velocity of the entire line in a certain calibration is... Compared with model predictions deviation Exceeding the threshold This will trigger a wave speed anomaly alarm. for The data was marked as suspicious and excluded from the regression update. Simultaneously, maintenance personnel were notified to check the line status. (Model predicted value) It is The input is obtained from the wave velocity-temperature mapping model; The calculation formula is: 。 5. The method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction according to claim 1, characterized in that: Step (4) specifically refers to: when a line fault occurs, reading the real-time conductor temperature of each distributed monitoring node at the time of fault triggering. Calculate the average temperature of the entire line. : ； In the formula, This represents the total number of distributed monitoring nodes. Will Substitute the wave velocity-temperature mapping model to calculate the equivalent traveling wave propagation velocity at the moment of failure. : ； In the formula, Reference temperature The reference wave velocity below; This is the wave velocity temperature correction factor.

6. The method for dynamic fault location of transmission lines based on real-time temperature feedback and wave velocity correction according to claim 1, characterized in that: Step (5) specifically includes the following steps in sequence: (5a) Phase mode transformation: Performed on three-phase current Transformation to extract the traveling wave component of the linear mode. Suppress common-mode interference; (5b) Wavelet transform: for linear mode traveling wave components implement Fourth-order wavelet 5-level decomposition, detecting modulus maxima in the 3rd to 5th decomposition levels; (5c) Using the current root mean square value of the power frequency current Based on this, set the traveling wave trigger threshold. : ； In the formula, For coefficients, ; (5d) The first one to be satisfied The absolute timestamp of the BeiDou / GPS module corresponding to the modulus maximum point The accuracy of the BeiDou / GPS module in determining the arrival time of the traveling wave is better than that of the GPS module. ; for In the 4th order wavelet 5-level decomposition, the modulus maxima are detected in the decomposition layers; (5e) Let the nearest distributed monitoring node at both ends of the fault interval be the distributed monitoring node. and distributed monitoring Distributed monitoring nodes and distributed monitoring The length of the line between them is ,but: Distance of fault point from distributed monitoring node distance for: ； In the formula, , The arrival of the traveling wave head at the distributed monitoring node is respectively , The moment; (5f) Distance of the fault point from the distributed monitoring node distance for: ； (5g) Legality verification: If If the location result is positive, the location result is valid; otherwise, it is extended to the adjacent interval for re-evaluation.

7. An electronic device, comprising: processor; as well as A memory storing computer program instructions, which, when executed by the processor, cause the processor to perform the dynamic fault location method for transmission lines based on real-time temperature feedback and wave velocity correction as described in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the method for dynamic location of transmission line faults based on real-time temperature feedback and wave velocity correction as described in any one of claims 1-6.

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