Traveling wave fault positioning system and method based on Beidou synchronization and intelligent filtering

Abstract

The invention discloses a traveling wave fault positioning system and method based on Beidou synchronization and intelligent filtering, and belongs to the technical field of power system fault detection. The system comprises a Beidou synchronization module used for acquiring a high-precision time service signal and realizing multi-terminal clock consistency; the traveling wave sampling module is used for synchronously collecting multi-point electric power signals and filtering the multi-point electric power signals; the wave head extraction module realizes wave head identification through a sliding window and dynamic threshold judgment; the time coding module constructs a time difference vector and maps a position relation in combination with a power grid topology; the fault positioning module estimates an initial position based on a nonlinear propagation model, and the intelligent filtering module carries out dynamic correction through state prediction and unscented Kalman filtering. The method realizes rapid and accurate positioning of fault points, has the advantages of high synchronization precision, strong anti-interference capability, small positioning error and the like, and is suitable for traveling wave fault positioning requirements in various electric power scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault detection, and particularly to a traveling wave fault location system and method based on Beidou synchronization and intelligent filtering. Background Art

[0002] With the development of smart grids, higher requirements are put forward for the rapid response and accurate location of power system faults. As a common transmission line fault location technology, the traveling wave method relies on multiple sampling terminals to synchronously collect current or voltage signals when a fault occurs, and estimates the fault location through the time difference of the wavefront arrival time. It has the advantages of fast propagation speed and rapid location response, and has been widely used in actual power systems.

[0003] However, the existing traveling wave fault location technology still faces several key technical challenges in engineering practice. First, the traditional timing method is limited by GPS drift, single-point failure or synchronization delay, resulting in clock deviation between terminals, which affects the accuracy of time difference calculation; second, the fault waveform is seriously interfered by factors such as noise, oscillation, and equipment error, resulting in unstable wavefront extraction and easy introduction of misjudgment; third, the existing filtering and location algorithms are mostly static or linear models, which are difficult to dynamically adapt to the changes in the power grid structure and complex signal characteristics, affecting the accuracy and stability of the final location result.

[0004] Therefore, there is an urgent need for a traveling wave fault location system that integrates a high-precision Beidou synchronization mechanism, intelligent wavefront detection, and adaptive filtering algorithm to improve the clock consistency, anti-interference ability, and global optimization ability of location, and solve the core problems existing in the prior art. Summary of the Invention

[0005] The present invention aims to solve the problems of insufficient timing accuracy, poor anti-interference ability of wavefront detection, low fault location accuracy, and lack of adaptive dynamic filtering mechanism in the existing traveling wave fault location system, and provides a traveling wave fault location system and method with high-precision timing ability, strong robustness wavefront recognition mechanism, and dynamic filtering correction model, so as to improve the accuracy, real-time performance, and adaptability of fault location.

[0006] To achieve the above object, the present invention is realized through the following technical solutions:

[0007] A traveling wave fault location system based on Beidou synchronization and intelligent filtering, comprising:

[0008] A Beidou synchronization module, configured to receive the ephemeris data of multiple Beidou satellites, construct a clock correction model based on orbital parameters, clock deviation, and propagation delay information, and generate a standard time signal to broadcast to multiple traveling wave sampling terminals;

[0009] A traveling wave sampling module, which is used to control each sampling terminal to synchronously collect three-phase voltage or current signals based on the received standard time signal, and eliminate noise and abnormal data through filtering and verification;

[0010] A wavefront extraction module, which is used to perform a sliding window process on the sampling signal under the synchronous time base, and determine the wavefront arrival time through a dynamic threshold, and generate wavefront information with timestamps;

[0011] A time encoding module, which is used to sort the wavefront arrival time differences between multiple sampling terminals according to the power grid topology structure, construct a time difference vector and convert it into a power grid position parameter;

[0012] A fault location module, which is used to construct a propagation model based on the time difference vector and the power grid spatial topology data, estimate the initial position of the fault point, and optimize the positioning result by using an error correction mechanism;

[0013] An intelligent filtering module, which is used to predict the evolution state of the fault position based on a dynamic system model, fuse the observation values, and output the final accurate fault location result through a non-linear filtering method.

[0014] Preferably, the Beidou synchronization module includes:

[0015] An ephemeris receiving unit, which is used to receive the ephemeris data of at least four Beidou satellites in parallel, including orbital parameters, clock bias and propagation delay information, and calculate the standard time signal accordingly;

[0016] A time synchronization unit, which is used to broadcast the standard time signal to multiple traveling wave sampling terminals, and correct the local clocks of each terminal through a synchronization calibration method;

[0017] A time calibration feedback unit, which is used to collect the local time synchronization deviation of each terminal, and perform dynamic adjustment by using a PID control algorithm to suppress clock drift;

[0018] An ephemeris consistency correction unit, which is used to establish a clock error model based on the ephemeris data of multiple satellites, and calculate the error correction values of each satellite through a least squares optimization algorithm. The correction results are used to unify the time synchronization reference of each terminal and ensure that the overall synchronization accuracy of the system meets the positioning requirements.

[0019] Furthermore, the optimization process of the ephemeris consistency correction unit includes the following steps:

[0020] Receive ephemeris data from at least four Beidou satellites, including satellite orbital parameters, clock biases, and signal propagation delay information, and construct a timestamp set for each satellite based on the ephemeris data as the basis for subsequent clock synchronization and correction; establish a clock error model according to the ephemeris data of each satellite, calculate the clock error of each satellite at a specific moment based on satellite orbital changes, signal propagation delay, and clock drift factors, and form an error function, the expression of which is:

[0021] ΔT 卫星i (t) = f(satellite orbital parameters, signal propagation delay, satellite clock drift);

[0022] Use the least squares optimization algorithm to correct the clock errors of multiple satellites. The optimization objective function is:

[0023]

[0024] where: ω i is the dynamic weight of the i-th satellite, dynamically adjusted according to the signal quality and orbital stability of the satellite, ΔT 卫星i (t) is the clock error of the i-th satellite, T 同步i (t) is the ideal synchronization time;

[0025] Update the clock of each traveling wave sampling terminal according to the optimized correction value, monitor the clock error of each terminal in real time, and dynamically adjust the correction value to ensure the clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction value according to the feedback signal and optimizes the traveling wave signal; perform intelligent filtering on the collected traveling wave signals, adjust the filtering parameters in real time according to the signal quality, and remove the noise.

[0026] Preferably, the traveling wave sampling module includes:

[0027] A sampling control unit for setting the sampling period according to the time synchronization signal received by the Beidou synchronization module. The setting of the sampling period is based on the signal acquisition requirements and the real-time state of the system to ensure that the time of each traveling wave sampling terminal is consistent with that of the Beidou synchronization module;

[0028] A data acquisition unit for collecting three-phase current or voltage signals according to the preset sampling accuracy and sampling rate, and storing the collected data in a specified format. The sampling accuracy and sampling rate are configured according to the signal acquisition requirements;

[0029] A signal verification unit for preliminarily verifying the collected data, removing abnormal data and high-frequency noise signals that do not meet the standards. The standards for abnormal data include amplitude and frequency threshold judgments, and at the same time verify the collected data according to the data consistency standard;

[0030] The signal denoising unit uses a wavelet transform filtering algorithm to smooth the sampled signal and remove high-frequency noise interference. The filter window size and standard deviation of the Gaussian filtering algorithm are adjusted according to the characteristics of the sampled signal.

[0031] Preferably, the wavefront extraction module includes:

[0032] A sliding window processing unit for performing sliding window segmentation processing on the sampled signal under the Beidou time reference to extract transient amplitude change observation values. The window size is dynamically adjusted according to the signal main frequency characteristics and the system operation state;

[0033] A wavefront detection unit for comparing the current amplitude change observation value with a dynamic threshold and determining the wavefront arrival time accordingly, generating a timestamp with the Beidou time mark;

[0034] A threshold determination and optimization unit for constructing a signal feature correlation radius related to the Beidou time service error. Based on this correlation radius and the historical amplitude change reference sequence, the mean shift algorithm is used to dynamically generate a fault feature convergence threshold, and through docking with the intelligent filtering module, the Beidou-noise joint kernel function is used to dynamically smooth the threshold to improve the sensitivity and anti-noise performance of wavefront detection.

[0035] Furthermore, the construction process of the threshold determination and optimization unit is as follows:

[0036] Calculate the signal feature correlation radius, combine the Beidou time service error and the signal main frequency characteristics to ensure that the threshold adjustment matches the time synchronization accuracy and the signal frequency domain characteristics. The expression is:

[0037]

[0038] Where: R BNC Is the signal feature correlation radius, used to constrain the scope of the kernel function; f signal Is the signal main frequency, analyzed in real time by the sliding window processing unit; σ BNC Is the Beidou time service error, dynamically corrected according to the clock jitter;

[0039] Based on the Beidou synchronization historical data and the joint kernel function, iteratively calculate the fault feature convergence threshold to suppress non-fault amplitude change interference and improve the sensitivity of wavefront detection. The formula is:

[0040]

[0041] Where: T conv (t) is the fault feature convergence threshold, reflecting the statistical consistency between the current signal and the historical wavefront features; ΔA obs (t) is the transient amplitude change observation value, extracted in real time by the sliding window processing unit; Δ Aref(k) is the historical amplitude change reference sequence, stored aligned by Beidou timestamp; K BNC is the Beidou-noise joint kernel function, fusing the timing error and noise distribution characteristics; N sync The number of Beidou synchronization reference points is dynamically configured by the timing interval and sampling rate;

[0042] The threshold is dynamically smoothed through the intelligent filtering module to suppress instantaneous noise disturbances and maintain the continuity of fault characteristics. The expression is:

[0043] T final T(t) = β × T conv (t) + (1 - β) × T final (t - 1);

[0044] Where: T final (t) is the dynamic threshold for real-time wavefront detection and determination; β is the fault feature attenuation factor, adaptively optimized by the intelligent filtering module according to historical fault data.

[0045] Preferably, the time coding module includes:

[0046] A time difference calculation unit for calculating the wavefront arrival time difference between multiple sampling terminals. The time difference calculation is based on the timestamp data of the wavefront signals received by the sampling terminals;

[0047] A sorting unit for sorting the time differences according to the power grid topology structure. The sorting process generates an ordered time difference vector based on the positions of the sampling terminals and the topological connection relationship of the power grid. The sorted time difference vector accurately reflects the spatial distribution of the power grid;

[0048] An encoding generation unit for converting the sorted time difference vector into position parameters. The position parameters are obtained by combining the time difference and the power grid position mapping relationship through a linear mapping rule based on the power grid topology.

[0049] Preferably, the fault location module includes:

[0050] A spatio-temporal propagation modeling unit for constructing a non-linear propagation model based on the wavefront arrival time differences and spatial positions of multiple sampling terminals. The model considers factors such as propagation speed, path characteristics, and power grid topology;

[0051] An initial location unit for inputting the time difference vector and spatial data into the propagation model and calculating the initial position of the fault point through numerical iteration;

[0052] An error correction unit for correcting the initial position estimate according to factors such as synchronization deviation and power grid structure disturbance;

[0053] A non - linear optimization unit is used to model the location problem as a minimization problem of the following objective function, and the formula is:

[0054]

[0055] Where: P f is the fault point location vector; P i is the location of the i - th traveling - wave sampling terminal; Δt i is the time - difference of the wave - front arrival at the i - th terminal; v(P f , P i ) is the traveling - wave propagation speed; is the Beidou time - service error; ω i is the observation weight of the i - th terminal; Φ(P f , T grid ) is the power - grid topology constraint function; T grid is the topology structure description; Ψ(P f , H filter ) is the filtering correction function; H filter is the historical filtering state; λ and γ are the weight coefficients of the constraint terms respectively;

[0056] The optimization process combines non - linear propagation paths, sampling noise, time - service deviation and power - grid structure information to globally estimate and dynamically correct the fault location, and outputs the final accurate location result.

[0057] Preferably, the intelligent filtering module includes:

[0058] A state prediction unit is used to input the initial location coordinates into the state equation and predict the current fault location according to the dynamic system model. The dynamic system model predicts the fault location at the next moment based on the physical characteristics of fault propagation, combined with the initial location, propagation time and power - grid topology structure;

[0059] A covariance estimation unit adopts a deep - learning adaptive mechanism to dynamically adjust the covariance matrix of state noise and observation noise according to the current operating environment and signal fluctuation conditions. The deep - learning mechanism optimizes the covariance matrix according to real - time feedback;

[0060] A filtering update unit adopts the Unscented Kalman Filter (UKF) method to fuse the state prediction result and the observation value and update the fault location result. The UKF method approximates the state space through non - linear expansion and accurately processes the measurement error and process noise in the non - linear dynamic system.

[0061] A location method for a traveling - wave fault location system based on Beidou synchronization and intelligent filtering includes the following steps:

[0062] Step 1: Receive the ephemeris data of multiple Beidou satellites, construct a clock correction model based on satellite orbit parameters, clock deviation, and signal propagation delay, generate a standard time signal, and broadcast it to multiple traveling wave sampling terminals;

[0063] Step 2: Based on the standard time signal, control each sampling terminal to synchronously collect three-phase voltage or current signals, filter and verify the collected signals, and eliminate noise and abnormal data;

[0064] Step 3: Under the synchronous time base, perform a sliding window process on the sampling signal, and determine the wavefront arrival time based on a dynamic threshold to generate wavefront information including timestamps;

[0065] Step 4: According to the power grid topology structure, sort the wavefront arrival time differences between multiple sampling terminals, construct a time difference vector, and convert it into power grid position parameters;

[0066] Step 5: Construct a propagation model based on the time difference vector and power grid spatial topology data, estimate the initial position of the fault point, and use an error correction mechanism to optimize the positioning result;

[0067] Step 6: Predict the evolution state of the fault position based on a dynamic system model, fuse the observed values, and output the final fault location result through a nonlinear filtering method.

[0068] The present invention constructs a clock correction model based on Beidou satellite ephemeris data, and combines multi-satellite consistency correction and PID feedback control mechanism to form a closed-loop synchronization system, which can effectively suppress the errors caused by satellite jitter or single-point drift, achieve microsecond-level timing accuracy, and significantly outperform the millisecond-level accuracy of traditional GPS or ground reference timing methods. In terms of wavefront extraction, the present invention first introduces the "Beidou-noise joint kernel function" dynamic threshold algorithm, which adaptively adjusts in combination with historical amplitude change sequences and timing error characteristics, improving the sensitivity and anti-interference ability of wavefront detection, and significantly reducing the misjudgment risk caused by non-fault disturbances. In terms of fault location modeling, the present invention integrates multiple factors such as power grid topology structure, propagation speed, and synchronization deviation, establishes a non-linear propagation optimization model, and accurately estimates the fault point location through numerical iteration, effectively breaking through the problem of limited accuracy of existing linear models under complex network structures. In order to further improve the stability and accuracy of the positioning results, the present invention introduces an intelligent filtering mechanism, which uses the unscented Kalman filter algorithm combined with a deep learning model to realize the fusion calculation of state prediction and observed values, has good dynamic adaptability and non-linear processing ability, and significantly enhances the stability and versatility of the system under various power grid operating environments. In addition, the overall system architecture of the present invention is clear, the module division of labor is clear, and it can be realized relying on the existing Beidou timing platform and intelligent sampling equipment, with good engineering implementation and promotion application value, and is especially suitable for the traveling wave fault location requirements in various complex scenarios such as transmission grids, ring networks, cable channels, and regional power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0070] Figure 1 It is a schematic diagram of the overall structure of the system of the present invention;

[0071] Figure 2 It is a flowchart of the method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention with reference to the drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0073] The traveling wave fault location system based on Beidou synchronization and intelligent filtering of the present invention includes the following modules: Beidou synchronization module, traveling wave sampling module, wave head extraction module, time coding module, fault location module and intelligent filtering module, which cooperate through network communication or time-triggered mechanism. When each module runs, it takes the Beidou synchronization time as the unified time reference, and constructs the following positioning process chain: time synchronization → signal sampling → wave head detection → time difference construction → initial positioning → filtering correction → output positioning result.

[0074] Embodiment 1

[0075] As Figure 1 shown, it is an embodiment of the present invention. This embodiment provides a traveling wave fault location system based on Beidou synchronization and intelligent filtering, including:

[0076] (1) Beidou synchronization module

[0077] It is used to receive the ephemeris data of multiple Beidou satellites, construct a clock correction model based on orbital parameters, clock deviation and propagation delay information, and generate a standard time signal to broadcast to multiple traveling wave sampling terminals;

[0078] The Beidou synchronization module includes:

[0079] Ephemeris receiving unit, which is used to receive the ephemeris data of at least four Beidou satellites in parallel, including orbital parameters, clock deviation and propagation delay information, and calculate the standard time signal accordingly;

[0080] Time synchronization unit, which is used to broadcast the standard time signal to multiple traveling wave sampling terminals, and correct the local clocks of each terminal through synchronous calibration;

[0081] Time calibration feedback unit, which is used to collect the local time calibration deviation of each terminal, and adopt the PID control algorithm for dynamic adjustment to suppress clock drift;

[0082] Ephemeris consistency correction unit, which is used to establish a clock error model based on the ephemeris data of multiple satellites, and calculate the error correction values of each satellite through the least squares optimization algorithm. The correction results are used to unify the time reference of each terminal and ensure that the overall synchronization accuracy of the system meets the positioning requirements.

[0083] Further, the optimization process of the ephemeris consistency correction unit includes the following steps:

[0084] Receiving ephemeris data from at least four Beidou satellites, including satellite orbital parameters, clock biases, and signal propagation delay information, and constructing a timestamp set for each satellite based on the ephemeris data as a basis for subsequent clock synchronization and correction; establishing a clock error model according to the ephemeris data of each satellite, calculating the clock error of each satellite at a specific moment based on satellite orbit changes, signal propagation delay, and clock drift factors, and forming an error function, the expression of which is:

[0085] ΔT 卫星i (t) = f(satellite orbital parameters, signal propagation delay, satellite clock drift);

[0086] Using the least squares optimization algorithm to correct the clock errors of multiple satellites, the optimization objective function is:

[0087]

[0088] where: ω i is the dynamic weight of the i-th satellite, dynamically adjusted according to the signal quality and orbit stability of the satellite, ΔT 卫星i (t) is the clock error of the i-th satellite, T 同步i (t) is the ideal synchronization time;

[0089] According to the optimized correction value, update the clock of each traveling wave sampling terminal, monitor the clock error of each terminal in real time and dynamically adjust the correction value to ensure the clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction value according to the feedback signal and optimizes the traveling wave signal; perform intelligent filtering on the collected traveling wave signal, adjust the filtering parameters in real time according to the signal quality, and remove the noise.

[0090] In this embodiment, when a fault occurs during the operation of the power grid, the system first obtains a standard time signal through Beidou satellites. The Beidou synchronization module can simultaneously receive the time information sent by multiple Beidou satellites, analyze these data, remove errors, and finally generate a very accurate unified "standard time".

[0091] This time signal is broadcast to each traveling wave sampling terminal deployed throughout the power grid, enabling them to "start synchronously", that is, all terminals start collecting data at the same moment, thus ensuring the consistency and reliability of subsequent processing.

[0092] (2) Traveling wave sampling module

[0093] Used to control each sampling terminal to synchronously collect three-phase voltage or current signals based on the received standard time signal, and remove noise and abnormal data through filtering and verification;

[0094] The traveling wave sampling module includes:

[0095] A sampling control unit, configured to set a sampling period according to the time synchronization signal received from the Beidou synchronization module. The setting of the sampling period is based on the acquisition requirements of the signals and the real-time state of the system, ensuring that the time of each traveling wave sampling terminal is consistent with that of the Beidou synchronization module;

[0096] A data acquisition unit, configured to acquire three-phase current or voltage signals according to a preset sampling accuracy and sampling rate, and store the acquired data in a specified format. The sampling accuracy and sampling rate are configured according to the signal acquisition requirements;

[0097] A signal verification unit, configured to perform preliminary verification on the acquired data, eliminate abnormal data and high-frequency noise signals that do not meet the standards. The standards for the abnormal data include amplitude and frequency threshold judgments, and at the same time verify the acquired data according to the data consistency standard;

[0098] A signal denoising unit, which uses a wavelet transform filtering algorithm to smooth the sampling signal and remove high-frequency noise interference. The filter window size and standard deviation of the Gaussian filtering algorithm are adjusted according to the characteristics of the sampling signal.

[0099] In this embodiment, after receiving the standard time, each terminal starts to acquire three-phase signals (voltage or current) at its location at a very high frequency (such as 20,000 times per second), and saves these raw data.

[0100] After the acquisition is completed, in order to avoid being interfered by "noise", the system uses filtering techniques (such as wavelet transform or Gaussian filtering) to remove the noise. At the same time, if there are problems such as abnormal amplitude or frequency mutation in the data, they will also be automatically eliminated by the system to ensure that only valid and clean signal data are retained.

[0101] (3) Wavefront extraction module

[0102] It is configured to perform a sliding window process on the sampling signal under the synchronous time base, and determine the wavefront arrival time through a dynamic threshold, generating wavefront information with a timestamp;

[0103] The wavefront extraction module includes:

[0104] A sliding window processing unit, configured to perform sliding window segmentation processing on the sampling signal under the Beidou time reference, and extract transient amplitude change observation values. The window size is dynamically adjusted according to the main frequency characteristics of the signal and the operating state of the system;

[0105] A wavefront detection unit, configured to compare the current amplitude change observation value with the dynamic threshold, and determine the wavefront arrival time accordingly, generating a timestamp with the Beidou time scale;

[0106] The threshold determination and optimization unit is used to construct the signal feature correlation radius related to the Beidou time service error. Based on this correlation radius and the historical amplitude change reference sequence, the mean shift algorithm is used to dynamically generate the fault feature convergence threshold. By docking with the intelligent filtering module, the Beidou-noise joint kernel function is used to dynamically smooth the threshold, improving the sensitivity and anti-noise performance of the wavehead detection.

[0107] Specifically, the construction process of the threshold determination and optimization unit is as follows:

[0108] Calculate the signal feature correlation radius, combine the Beidou time service error and the signal main frequency characteristics to ensure that the threshold adjustment matches the time synchronization accuracy and the signal frequency domain characteristics. The expression is:

[0109]

[0110] Where: R BNC is the signal feature correlation radius, used to constrain the scope of the kernel function; f signal is the signal main frequency, analyzed in real time by the sliding window processing unit; σ BNC is the Beidou time service error, dynamically corrected according to the clock jitter;

[0111] Based on the Beidou synchronization historical data and the joint kernel function, iteratively calculate the fault feature convergence threshold to suppress the non-fault amplitude change interference and improve the wavehead detection sensitivity. The formula is:

[0112]

[0113] Where: T conv (t) is the fault feature convergence threshold, reflecting the statistical consistency between the current signal and the historical wavehead features; ΔA obs (t) is the transient amplitude change observation value, extracted in real time by the sliding window processing unit; Δ Aref (k) is the historical amplitude change reference sequence, stored aligned according to the Beidou timestamp; K BNC is the Beidou-noise joint kernel function, integrating the time service error and the noise distribution characteristics; N sync Beidou synchronization reference points, dynamically configured by the time service interval and the sampling rate;

[0114] Dynamically smooth the threshold through the intelligent filtering module to suppress the instantaneous noise disturbance and maintain the continuity of the fault features. The expression is:

[0115] T final (t) = β × T conv (t) + (1 - β) × T final (t - 1);

[0116] Where: T final(t) is the dynamic threshold for real-time wavefront detection and determination; β is the fault feature attenuation factor, which is adaptively optimized by the intelligent filtering module according to historical fault data.

[0117] In this embodiment, the so-called "wavefront" is the small section of significantly changed signal generated at the moment when the power fault just occurs. The goal of the system is to accurately find the time of this moment.

[0118] The system continuously scans the signal for "mutations" in a sliding window manner. If the energy of a certain section of the signal suddenly increases and exceeds the dynamically calculated threshold, it is determined that this is the "wavefront", and the accurate time (timestamp) when it occurs is recorded.

[0119] This threshold is not fixed, but is automatically adjusted according to historical waveforms, timing errors, and noise conditions, so as to ensure that the system can accurately identify wavefronts in different environments.

[0120] (4) Time coding module

[0121] Used to sort the time differences of wavefront arrival times between multiple sampling terminals according to the power grid topology structure, construct a time difference vector, and convert it into a power grid position parameter;

[0122] The time coding module includes:

[0123] The time difference calculation unit is used to calculate the time differences of wavefront arrival times between multiple sampling terminals. The time difference calculation is based on the timestamp data of the wavefront signals received by the sampling terminals;

[0124] The sorting unit is used to sort the time differences according to the power grid topology structure. The sorting process generates an ordered time difference vector according to the positions of each sampling terminal and the topological connection relationship of the power grid. The sorted time difference vector accurately reflects the spatial distribution of the power grid;

[0125] The coding generation unit is used to convert the sorted time difference vector into a position parameter. The position parameter combines the time difference and the power grid position mapping relationship through a linear mapping rule based on the power grid topology.

[0126] In this embodiment, each terminal has found the time when it saw the wavefront. Now, these "seen moments" can be compared.

[0127] The system calculates the time differences of wavefront arrival times between each terminal, sorts and encodes them according to the structure of the power grid (which terminal is in which position), and finally forms a "time difference vector".

[0128] This vector is like a "time map", which can reflect the propagation order and speed of the power fault spreading from a certain point to each terminal.

[0129] (5) Fault Location Module

[0130] It is used to construct a propagation model based on the time difference vector and the grid spatial topology data, estimate the initial position of the fault point, and optimize the positioning result by using an error correction mechanism;

[0131] The fault location module includes:

[0132] A spatio-temporal propagation modeling unit, which is used to construct a non-linear propagation model based on the time difference of the wavefront arrival times of multiple sampling terminals and their spatial positions, and the model considers factors such as propagation speed, path characteristics, and grid topology influence;

[0133] An initial positioning unit, which is used to input the time difference vector and spatial data into the propagation model and calculate the initial position of the fault point through numerical iteration;

[0134] An error correction unit, which is used to correct the initial position estimate according to factors such as synchronization deviation and grid structure disturbance;

[0135] A non-linear optimization unit, which is used to model the positioning problem as a minimization problem of the following objective function, and the formula is:

[0136]

[0137] Where: P f is the fault point position vector; P i is the position of the i-th traveling wave sampling terminal; Δt i is the time difference of the wavefront arrival of the i-th terminal; v(P f ,P i ) is the traveling wave propagation speed; is the Beidou timing error; ω i is the observation weight of the i-th terminal; Φ(P f ,T grid ) is the grid topology constraint function; T grid is the topology structure description; Ψ(P f ,H filter ) is the filtering correction function; H filter is the historical filtering state; λ and γ are the constraint term weight systems respectively;

[0138] The optimization process combines the non-linear propagation path, sampling noise, timing deviation, and grid structure information to globally estimate and dynamically correct the fault position, and outputs the final accurate positioning result.

[0139] In this embodiment, the system inputs the time difference vector and the position data of each terminal in the grid into a positioning model. The model will calculate a preliminary fault position according to factors such as the radio wave propagation speed, the grid structure, and the timing error.

[0140] This process is optimized using a non - linear mathematical algorithm, that is, by iterative repetition, continuously adjusting the estimated value of the fault location until the error is minimized.

[0141] (6) Intelligent filtering module

[0142] It is used to predict the evolution state of the fault location based on the dynamic system model, fuse the observed values, and output the final accurate fault location result through non - linear filtering methods.

[0143] The intelligent filtering module includes:

[0144] A state prediction unit, which inputs the initial positioning coordinates into the state equation and predicts the current fault location according to the dynamic system model. The dynamic system model predicts the fault location at the next moment based on the physical characteristics of fault propagation, combined with the initial position, propagation time, and power grid topology structure.

[0145] A covariance estimation unit, which adopts a deep - learning adaptive mechanism to dynamically adjust the covariance matrix of state noise and observation noise according to the current operating environment and signal fluctuation conditions. The deep - learning mechanism optimizes the covariance matrix according to real - time feedback.

[0146] A filtering update unit, which uses the Unscented Kalman Filter (UKF) method to fuse the state prediction result and the observed values, and updates the fault location result. The UKF method approximates the state space through non - linear extension and accurately processes the measurement error and process noise in the non - linear dynamic system.

[0147] In this embodiment, due to the complex actual power grid environment and the influence of various interferences on signals, the result of the preliminary positioning may still have deviations.

[0148] Therefore, the system uses prediction and filtering technologies for a "second correction". It will predict where the fault point should be at the next moment, then compare the predicted value with the just - observed position, and take the weighted average of the two to obtain a more reliable positioning result.

[0149] This filtering process uses the Unscented Kalman Filter (UKF) and introduces a deep - learning mechanism to dynamically adjust the parameters in the algorithm according to the current environment.

[0150] As Figure 2 shown, it is another embodiment of the present invention. This embodiment provides a positioning method for a traveling - wave fault location system based on Beidou synchronization and intelligent filtering, including the following steps:

[0151] Step 1: Receive the ephemeris data of multiple Beidou satellites, construct a clock correction model based on satellite orbit parameters, clock deviation, and signal propagation delay, generate a standard time signal, and broadcast it to multiple traveling - wave sampling terminals.

[0152] Step 2: Based on the standard time signal, control each sampling terminal to synchronously collect three-phase voltage or current signals, and filter and verify the collected signals to eliminate noise and abnormal data;

[0153] Step 3: Under the synchronous time base, perform a sliding window process on the sampling signals, and determine the arrival time of the wavefront based on a dynamic threshold to generate wavefront information containing timestamps;

[0154] Step 4: According to the power grid topology structure, sort the time differences of wavefront arrival between multiple sampling terminals, construct a time difference vector, and convert it into power grid position parameters;

[0155] Step 5: Based on the time difference vector and power grid spatial topology data, construct a propagation model, estimate the initial position of the fault point, and optimize the positioning result using an error correction mechanism;

[0156] Step 6: Based on the dynamic system model, predict the evolution state of the fault position, and fuse the observed values to output the final fault positioning result through a non-linear filtering method.

[0157] Embodiment 2

[0158] In a typical application scenario, the traveling wave fault location system based on Beidou synchronization and intelligent filtering of the present invention is deployed in a 110 kV double-circuit overhead transmission project in a certain area. A traveling wave sampling terminal is installed at each end of each line, and a Beidou timing module is configured to obtain the standard time signal in the B1 frequency band, with a timing accuracy better than 500 nanoseconds. The sampling terminal sets the sampling rate to 20 kHz, the sampling window length to 1024 points, the filtering algorithm uses joint filtering of wavelet decomposition and Gaussian function, and dynamically adjusts the filtering threshold according to the real-time signal quality. In this actual operating environment, a single-phase grounding fault is simulated. The wavefront extraction module can detect an effective wavefront signal and accurately mark the Beidou timestamp within 10 ms after the fault occurs. The time coding module generates a time difference vector based on the on-site topological relationship, and the fault location module estimates the position by combining the topological structure and the propagation speed model, and then performs dynamic correction through the intelligent filtering module using the UKF method. The final fault location error is less than 250 meters, and the overall system response time is less than 30 ms, fully verifying the synchronization accuracy, wavefront recognition accuracy, and fault location ability of the present invention under typical working conditions of high-voltage transmission lines, and having good engineering adaptability and practicality.

[0159] Embodiment 3

[0160] In another anti-interference application scenario, to verify the robustness of the system of the present invention in a complex interference environment, the system is deployed in a 35 kV urban ring network line, and various interference factors are simulated by signal injection, including: arc oscillation, power frequency harmonic superposition, high-frequency transient spikes, and communication link interruption (simulating a reporting delay of up to 5 ms for the sampling terminal). Under the above conditions, the traditional fixed-threshold wavefront detection method fails or makes misjudgments, while the wavefront extraction module of the present invention constructs a dynamic threshold by combining the historical amplitude change trend and the Beidou synchronization error, and adaptively identifies the mutant waveform based on the "Beidou-noise joint kernel function", which can effectively suppress the interference of non-fault transient disturbances. Under the test condition where the high-frequency noise signal intensity is up to 45 dB, the system can still stably extract the effective wavefront, and uses the time coding module to construct a time difference vector. The positioning module completes the initial positioning through non-linear propagation modeling and error correction mechanism. In the filtering stage, the intelligent filtering module combines the UKF algorithm with the deep learning dynamic covariance adjustment mechanism to automatically discriminate and weigh abnormal observed values, significantly enhancing the robustness of the system against non-ideal factors such as timing drift and observation deviation. The final experimental results show that under interference conditions, the fault location error of the system is controlled within 300 meters, and the wavefront recognition accuracy rate exceeds 92%, fully demonstrating that the present invention still has the ability of stable operation and high-precision positioning under adverse conditions such as limited signal quality and complex power waveform disturbances.

[0161] In summary, the present invention can effectively solve the core problems of the existing traveling wave positioning technology, such as low timing accuracy, unstable wavefront recognition, poor anti-interference ability, and insufficient positioning accuracy. By constructing a high-precision Beidou clock synchronization mechanism, an adaptive wavefront detection algorithm, a non-linear propagation modeling integrating topology, and an intelligent filtering and positioning correction module, the present invention realizes the high accuracy, high robustness, and high response efficiency of fault point positioning. The system architecture is clear, the module functions are independent and highly collaborative, and it has a good engineering implementation foundation and expansion ability, and is applicable to the fault rapid diagnosis and positioning requirements in various complex power systems such as high-voltage transmission networks, urban ring networks, and regional distribution networks. It is innovative and practical in key links such as system synchronization, data acquisition, fault identification, modeling, and output, and has important technical promotion value and industrial application prospects.

[0162] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0163] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, without being in the order shown or discussed.

[0164] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various changes or substitutions, and these should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A traveling wave fault location system based on Beidou synchronization and intelligent filtering, characterized in that, Including: A Beidou synchronization module, which is used to receive the ephemeris data of multiple Beidou satellites, construct a clock correction model based on orbital parameters, clock deviation and propagation delay information, and generate a standard time signal to broadcast to multiple traveling wave sampling terminals; A traveling wave sampling module, which is used to control each sampling terminal to synchronously collect three-phase voltage or current signals based on the received standard time signal, and eliminate noise and abnormal data through filtering and verification; A wavefront extraction module, which is used to perform a sliding window process on the sampling signal under the synchronous time base, and determine the wavefront arrival time through a dynamic threshold, and generate wavefront information with a timestamp; A time coding module, which is used to sort the wavefront arrival time differences between multiple sampling terminals according to the power grid topology structure, construct a time difference vector and convert it into a power grid position parameter; A fault location module, which is used to construct a propagation model based on the time difference vector and the power grid spatial topology data, estimate the initial position of the fault point, and optimize the positioning result by using an error correction mechanism; An intelligent filtering module, which is used to predict the evolution state of the fault position based on a dynamic system model, fuse the observed values, and output the final accurate fault location result through a non-linear filtering method.

2. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, wherein The Beidou synchronization module includes: An ephemeris receiving unit, which is used to receive the ephemeris data of at least four Beidou satellites in parallel, including orbital parameters, clock deviation and propagation delay information, and calculate the standard time signal accordingly; A time synchronization unit, which is used to broadcast the standard time signal to multiple traveling wave sampling terminals, and correct the local clocks of each terminal through a synchronization calibration method; A time calibration feedback unit, which is used to collect the local time calibration deviation of each terminal, and perform dynamic adjustment using a PID control algorithm to suppress clock drift; an ephemeris consistency correction unit, which is used to establish a clock error model based on the ephemeris data of multiple satellites, and calculate the error correction values of each satellite through a least squares optimization algorithm, and the correction results are used to unify the time calibration reference of each terminal and ensure that the overall system synchronization accuracy meets the positioning requirements.

3. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 2, characterized in that, The optimization process of the ephemeris consistency correction unit includes the following steps: Receive ephemeris data from at least four Beidou satellites, including the orbital parameters, clock deviation, and signal propagation delay information of the satellites, and construct a timestamp set for each satellite based on the ephemeris data as the basis for subsequent clock synchronization and correction; according to the ephemeris data of each satellite, establish a clock error model, calculate the clock error of each satellite at a specific moment based on satellite orbit changes, signal propagation delays, and clock drift factors, and form an error function, the expression of which is: ΔT 卫星i (t) = f(satellite orbit parameters, signal propagation delay, satellite clock drift); Use the least squares optimization algorithm to correct the clock errors of multiple satellites, and the optimization objective function is: where: ω i is the dynamic weight of the i-th satellite, dynamically adjusted according to the signal quality and orbital stability of the satellite, ΔT 卫星i (t) is the clock error of the i-th satellite, T 同步i (t) is the ideal synchronization time; According to the optimized correction values, update the clocks of each traveling wave sampling terminal, monitor the clock errors of each terminal in real time and dynamically adjust the correction values to ensure the clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction values according to the feedback signal and optimizes the traveling wave signal; perform intelligent filtering on the collected traveling wave signals, adjust the filtering parameters in real time according to the signal quality, and remove the noise.

4. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, wherein The traveling wave sampling module includes: A sampling control unit, which is used to set a sampling period according to the time synchronization signal received from the Beidou synchronization module. The setting of the sampling period is based on the acquisition requirements of the signal and the real-time state of the system, ensuring that the time of each traveling wave sampling terminal is consistent with that of the Beidou synchronization module; A data acquisition unit, which is used to acquire three-phase current or voltage signals according to the preset sampling accuracy and sampling rate, and store the acquired data in a specified format. The sampling accuracy and sampling rate are configured according to the signal acquisition requirements; A signal verification unit, which is used to perform preliminary verification on the acquired data, eliminate abnormal data that does not meet the standards and high-frequency noise signals. The standards for the abnormal data include amplitude and frequency threshold judgments, and at the same time verify the acquired data according to the data consistency standard; A signal denoising unit, which uses a wavelet transform filtering algorithm to smooth the sampling signal and remove high-frequency noise interference. The filter window size and standard deviation of the Gaussian filtering algorithm are adjusted according to the characteristics of the sampling signal.

5. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, wherein The wavefront extraction module includes: A sliding window processing unit, which is used to perform sliding window segmentation processing on the sampling signal under the Beidou time reference, and extract transient amplitude change observation values. The window size is dynamically adjusted according to the main frequency characteristics of the signal and the operating state of the system; A wavefront detection unit, which is used to compare the current amplitude change observation value with a dynamic threshold, and determine the wavefront arrival time accordingly, generating a timestamp with Beidou time mark; A threshold determination and optimization unit, which is used to construct a signal feature correlation radius related to the Beidou time service error. Based on this correlation radius and the historical amplitude change reference sequence, use the mean shift algorithm to dynamically generate a fault feature convergence threshold, and through docking with the intelligent filtering module, use the Beidou-noise joint kernel function to perform dynamic smoothing processing on the threshold, improving the sensitivity and anti-noise performance of wavefront detection.

6. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 5, wherein The construction process of the threshold determination and optimization unit is as follows: Calculate the signal feature correlation radius, combine the Beidou time service error and the main frequency characteristics of the signal to ensure that the threshold adjustment matches the time synchronization accuracy and the signal frequency domain characteristics. The expression is: Where: R BNC is the signal feature correlation radius, used to constrain the action range of the kernel function; f signal is the main signal frequency, which is analyzed in real time by the sliding window processing unit; σ BNC is the Beidou time synchronization error, which is dynamically corrected according to the clock jitter; Based on the Beidou synchronization historical data and the joint kernel function, iteratively calculate the fault feature convergence threshold to suppress non-fault amplitude change interference and improve the wavefront detection sensitivity. The formula is: Among them: T conv (t) is the fault feature convergence threshold, reflecting the statistical consistency between the current signal and the historical wavefront features; ΔA obs (t) is the transient amplitude change observation value, which is extracted in real time by the sliding window processing unit; Δ Aref (k) is the historical amplitude change reference sequence, stored aligned according to the Beidou timestamp; K BNC is the Beidou-noise joint kernel function, fusing the timing error and the noise distribution characteristics; N sync The number of Beidou synchronization reference points, dynamically configured by the timing interval and the sampling rate; Perform dynamic smoothing on the threshold through the intelligent filtering module to suppress instantaneous noise disturbances and maintain the continuity of fault features. The expression is: T final y(t) = β×T conv y(t)+(1 - β)×T final y(t - 1); where: T final (t) is the dynamic threshold for real-time wavefront detection and determination; β is the fault feature attenuation factor adaptively optimized by the intelligent filtering module according to historical fault data.

7. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, characterized in that, The time coding module includes: A time difference calculation unit, which is used to calculate the wavefront arrival time difference between multiple sampling terminals. The time difference calculation is based on the timestamp data of the wavefront signals received by the sampling terminals; A sorting unit, which is used to sort the time differences according to the power grid topology structure. The sorting process generates an ordered time difference vector according to the positions of the sampling terminals and the topological connection relationship of the power grid. The sorted time difference vector accurately reflects the spatial distribution of the power grid; An encoding generation unit, which is used to convert the sorted time difference vector into a position parameter. The position parameter combines the time difference and the power grid position mapping relationship through a linear mapping rule based on the power grid topology.

8. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, characterized in that The fault location module includes: A spatio-temporal propagation modeling unit, which is used to construct a non-linear propagation model based on the time difference of arrival of wavefronts and the spatial positions of multiple sampling terminals, and the model takes into account factors such as propagation speed, path characteristics and power grid topology influence factors; An initial positioning unit, which is used to input the time difference vector and spatial data into the propagation model and calculate the initial position of the fault point through numerical iteration; An error correction unit, which is used to correct the initial position estimation value according to factors such as synchronization deviation and power grid structure disturbance; A non-linear optimization unit, which is used to model the positioning problem as a minimization problem of the following objective function, and the formula is: Where: P f is the fault point location vector; P i is the location of the i-th traveling wave sampling terminal; Δt i is the arrival time difference of the wavefront at the i-th terminal; v(P f , P i ) is the traveling wave propagation speed; δ i BDS is the Beidou timing error; ω i is the observation weight of the i-th terminal; Φ(P f , T grid ) is the power grid topology constraint function; T grid is the topology structure description; Ψ(P f , H filter ) is the filtering correction function; H filter is the historical filtering state; λ and γ are the constraint term weight systems respectively; The optimization process combines non-linear propagation paths, sampling noise, timing deviation and power grid structure information to globally estimate and dynamically correct the fault location, and outputs the final accurate positioning result.

9. The traveling wave fault location system based on Beidou synchronization and intelligent filtering according to claim 1, characterized in that, The intelligent filtering module includes: A state prediction unit, which is used to input the initial positioning coordinates into the state equation and predict the current fault location according to the dynamic system model. The dynamic system model is based on the physical characteristics of fault propagation, combines the initial position, propagation time and power grid topology structure to predict the fault location at the next moment; A covariance estimation unit, which adopts a deep learning adaptive mechanism to dynamically adjust the covariance matrix of state noise and observation noise according to the current operating environment and signal fluctuation conditions, and the deep learning mechanism optimizes the covariance matrix according to real-time feedback; A filtering update unit, which uses the unscented Kalman filter (UKF) method to fuse the state prediction result and the observation value and update the fault location result. The UKF method approximates the state space through non-linear expansion and accurately processes the measurement error and process noise in the non-linear dynamic system.

10. A positioning method of a traveling wave fault location system based on Beidou synchronization and intelligent filtering according to any one of claims 1-9, characterized in that, It includes the following steps: Step 1: Receive the ephemeris data of multiple Beidou satellites, and construct a clock correction model based on satellite orbit parameters, clock deviation and signal propagation delay, generate a standard time signal and broadcast it to multiple traveling wave sampling terminals; Step 2: Based on the standard time signal, control each sampling terminal to synchronously collect three-phase voltage or current signals, and filter and verify the collected signals to eliminate noise and abnormal data; Step 3: Under the synchronous time base, perform a sliding window process on the sampling signal, and determine the wavefront arrival time based on a dynamic threshold to generate wavefront information containing time stamps; Step 4: According to the power grid topology structure, sort the time differences of wavefront arrival between multiple sampling terminals, construct a time difference vector, and convert it into power grid position parameters; Step 5: Construct a propagation model based on the time difference vector and power grid spatial topology data, estimate the initial position of the fault point, and optimize the positioning result using an error correction mechanism; Step 6: Predict the evolution state of the fault location based on the dynamic system model, fuse the observation values, and output the final fault location result through a non-linear filtering method.

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