A system and method for locating a traveling wave fault based on Beidou synchronization and intelligent filtering

Abstract

The application discloses a kind of based on Beidou synchronization and intelligent filtering traveling wave fault location system and method, belong to power system fault detection technical field.The system includes: Beidou synchronization module is used to obtain high-precision timing signal and realizes the consistency of multi-terminal clock;Traveling wave sampling module is used to synchronously collect multi-point power signal and carry out filtering processing;Wave head extraction module realizes wave head identification by sliding window and dynamic threshold judgment;Time coding module constructs time difference vector and combines power grid topological mapping position relationship;Fault location module estimates initial position based on nonlinear propagation model, and intelligent filtering module is dynamically corrected by state prediction and unscented Kalman filtering.The application realizes the fast, accurate positioning of fault point, with high synchronization accuracy, strong anti-interference ability, small positioning error and other advantages, suitable for traveling wave fault location demand in a variety of power scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system fault detection, and in particular to a traveling wave fault location system and method based on Beidou synchronization and intelligent filtering. BACKGROUND

[0002] With the development of smart grid, higher requirements are put forward for the rapid response and accurate positioning of power system faults. As a common fault location technology for transmission lines, 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 wave head arrival, which has the advantages of fast propagation speed and rapid 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. Firstly, the traditional time service mode is limited by GPS drift, single point failure or synchronization delay, resulting in clock deviation between terminals, affecting the accuracy of time difference calculation; secondly, the fault waveform is seriously disturbed by noise, oscillation, equipment error and other factors, resulting in unstable wave head extraction and easy misjudgment; thirdly, the existing filtering and positioning algorithms are mostly static or linear models, which are difficult to dynamically adapt to the changes of power grid structure and complex signal characteristics, affecting the accuracy and stability of the final positioning result.

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

[0005] The present application aims to solve the problems of insufficient time service accuracy, poor wave head detection anti-interference ability, 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 time service ability, strong robustness wave head recognition mechanism and dynamic filtering correction model, thereby improving the accuracy, real-time performance and adaptability of fault location.

[0006] In order to achieve the above-mentioned purpose, the present application realizes the technical scheme as follows:

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

[0008] a Beidou synchronization module for receiving ephemeris data of multiple Beidou satellites, and constructing a clock correction model based on orbit parameters, clock deviation and propagation delay information, and generating a standard time signal broadcast to multiple traveling wave sampling terminals;

[0009] Traveling wave sampling module, for synchronously collecting three-phase voltage or current signals based on the received standard time signal, and removing noise and abnormal data through filtering and checking;

[0010] Wave front extraction module, for performing sliding window processing on the sampling signals under the synchronous time base, and determining the wave front arrival time through dynamic threshold, and generating wave front information with time stamp;

[0011] Time encoding module, for sorting the wave front arrival time difference between multiple sampling terminals according to the power grid topology structure, constructing a time difference vector and converting it into a power grid location parameter;

[0012] Fault location module, for constructing a propagation model based on the time difference vector and the power grid spatial topology data, estimating the initial location of the fault point, and optimizing the positioning result by using an error correction mechanism;

[0013] Intelligent filtering module, for predicting the evolution state of the fault location based on a dynamic system model, fusing observation values, and outputting the final accurate fault location result through a nonlinear filtering method.

[0014] Preferably, the Beidou synchronization module comprises:

[0015] Ephemeris receiving unit, for receiving ephemeris data of at least four Beidou satellites in parallel, including orbit parameters, clock bias and propagation delay information, and calculating a standard time signal therefrom;

[0016] Time synchronization unit, for broadcasting the standard time signal to multiple traveling wave sampling terminals, and correcting the local clock of each terminal through synchronous calibration;

[0017] Time correction feedback unit, for collecting the local timekeeping bias of each terminal, and dynamically adjusting it by using a PID control algorithm to suppress clock drift;

[0018] Ephemeris consistency correction unit, for establishing a clock error model based on the ephemeris data of multiple satellites, and calculating error correction values of each satellite through a least squares method optimization algorithm, the correction results being used to unify the timekeeping reference of each terminal and ensure that the overall synchronization accuracy of the system meets the positioning requirements.

[0019] Further, the optimization process of the ephemeris consistency correction unit comprises the following steps:

[0020] Ephemeris data is received from at least four BeiDou satellites, including satellite orbital parameters, clock deviation, and signal propagation delay information. A timestamp set is constructed for each satellite based on the ephemeris data, serving as the basis for subsequent clock synchronization and correction. A clock error model is established based on the ephemeris data of each satellite. The clock error of each satellite at a specific time is calculated based on satellite orbital changes, signal propagation delay, and clock drift factors, forming an error function, expressed as:

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

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

[0023]

[0024] Where: ω i The dynamic weight of the i-th satellite is dynamically adjusted based on the satellite's signal quality and orbital stability, ΔT. 卫星i (t) represents the clock error of the i-th satellite, T 同步i (t) represents the ideal synchronization time;

[0025] Based on the optimized correction value, the clock of each traveling wave sampling terminal is updated, the clock error of each terminal is monitored in real time and the correction value is dynamically adjusted to ensure clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction value and optimizes the traveling wave signal according to the feedback signal; the acquired traveling wave signal is intelligently filtered, and the filtering parameters are adjusted in real time according to the signal quality to remove noise.

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

[0027] The sampling control unit is used to set the sampling period according to the received time synchronization signal from the Beidou synchronization module. The setting of the sampling period is based on the signal acquisition requirements and the real-time status of the system, ensuring that the time of each traveling wave sampling terminal is consistent with that of the Beidou synchronization module.

[0028] The data acquisition unit is used to acquire three-phase current or voltage signals according to a preset sampling precision and sampling rate, and to store the acquired data in a specified format. The sampling precision and sampling rate are configured according to the signal acquisition requirements.

[0029] The signal verification unit is used to perform preliminary verification on the collected data, remove abnormal data and high-frequency noise signals that do not meet the standards. The standards for abnormal data include amplitude and frequency threshold judgment. At the same time, the collected data is verified according to the data consistency standard.

[0030] The signal denoising unit adopts a wavelet transform filtering algorithm to perform smoothing processing on the sampling signal, and removes high-frequency noise interference. The filtering window size and standard deviation value of the Gaussian filtering algorithm are adjusted according to the characteristics of the sampling signal.

[0031] Preferably, the wave head extraction module comprises:

[0032] The sliding window processing unit is configured to perform sliding window segmentation processing on the sampling signal under the Beidou time reference, extract transient amplitude variation observation values, and dynamically adjust the window size according to the signal main frequency characteristics and the system running state.

[0033] The wave head detection unit is configured to compare the current amplitude variation observation value with a dynamic threshold value, and determine the wave head arrival time according to the comparison, and generate a time stamp with the Beidou time mark.

[0034] The threshold determination and optimization unit is configured to construct a signal feature correlation radius related to the Beidou timing error, dynamically generate a fault feature convergence threshold value based on the correlation radius and a historical amplitude variation reference sequence by using a mean shift algorithm, and perform dynamic smoothing processing on the threshold value by using a Beidou-noise joint kernel function through the interface with the intelligent filtering module, so as to improve the sensitivity and noise resistance of the wave head detection.

[0035] Further, the threshold determination and optimization unit is constructed as follows:

[0036] The signal feature correlation radius is calculated, the Beidou timing error and the signal main frequency characteristics are combined, the threshold value adjustment is ensured to match the time synchronization accuracy and the signal frequency domain characteristics, and the expression is as follows:

[0037]

[0038] Wherein, R BNC is the signal feature correlation radius, used to constrain the action range 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 timing error, dynamically corrected according to the clock jitter;

[0039] Based on the Beidou synchronization historical data and the joint kernel function, the fault feature convergence threshold value is iteratively calculated, the non-fault amplitude variation interference is suppressed, and the sensitivity of the wave head detection is improved, and the formula is as follows:

[0040]

[0041] Wherein, T conv (t) is the fault feature convergence threshold value, reflecting the statistical consistency of the current signal and the historical wave head characteristics; ΔA obs (t) is the transient amplitude variation observation value, extracted in real time by the sliding window processing unit; Δ Aref(k) is a historical amplitude variation reference sequence, stored in alignment with the Beidou timestamp; K BNC is a Beidou-noise joint kernel function, which fuses the characteristics of timing error and noise distribution; N sync is the number of Beidou synchronization reference points, dynamically configured by the timing interval and the sampling rate;

[0042] The threshold value is dynamically smoothed by the intelligent filtering module to suppress instantaneous noise disturbance and maintain the continuity of fault features, and the expression is:

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

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

[0045] Preferably, the time encoding module comprises:

[0046] A time difference calculation unit for calculating the wave head arrival time difference between a plurality of sampling terminals, the time difference calculation being based on the timestamp data of the wave head signals received by the sampling terminals;

[0047] A sorting unit for sorting the time differences according to the power grid topology, the sorting process generating 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 reflecting the spatial distribution of the power grid;

[0048] An encoding generation unit for converting the sorted time difference vector into a position parameter, the position parameter being obtained by combining the time difference and the power grid position mapping relationship based on the linear mapping rule of the power grid topology.

[0049] Preferably, the fault location module comprises:

[0050] A space-time propagation modeling unit for constructing a nonlinear propagation model based on the wave head arrival time difference and the spatial position of a plurality of sampling terminals, the model taking into account the propagation speed, path characteristics and power grid topology influencing factors;

[0051] An initial positioning unit for inputting the time difference vector and the spatial data into the propagation model to calculate the initial position of the fault point by 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 nonlinear optimization unit for modeling the location problem as a minimization problem of an objective function as follows:

[0054]

[0055] where: P f is the fault location vector; P i is the location of the ith traveling wave sampling terminal; Δt i is the time difference of wave head arrival at the ith 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 ith 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 filter correction function; H filter is the historical filter state; λ, γ are constraint term weights, respectively;

[0056] The optimization process combines nonlinear propagation path, 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.

[0057] Preferably, the intelligent filtering module comprises:

[0058] a state prediction unit for inputting the initial positioning coordinates into the state equation, and predicting the current fault location according to a dynamic system model, the dynamic system model being based on the physical characteristics of fault propagation, combining the initial position, propagation time and power grid topology structure to predict the fault location at the next time;

[0059] a covariance estimation unit adopting 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, the deep learning mechanism optimizing the covariance matrix according to real-time feedback;

[0060] a filter updating unit adopting an unscented Kalman filter (UKF) method to fuse the state prediction result and the observation value, and update the fault location result, the UKF method approximating the state space through nonlinear extension, and accurately processing the measurement error and process noise in the nonlinear dynamic system.

[0061] A positioning method of a traveling wave fault location system based on Beidou synchronization and intelligent filtering, comprising the following steps:

[0062] Step 1: Receive ephemeris data of multiple Beidou satellites, and construct a clock correction model based on satellite orbit parameters, clock bias 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, and filter and verify the collected signals to eliminate noise and abnormal data;

[0064] Step 3: Under the synchronous time base, the sampling signal is processed by sliding window, and the wave head arrival time is determined based on the dynamic threshold to generate the wave head information containing the time stamp;

[0065] Step 4: According to the power grid topology structure, the wave head arrival time difference between multiple sampling terminals is sorted, a time difference vector is constructed, and it is converted into power grid location parameters;

[0066] Step 5: Based on the time difference vector and the power grid spatial topology data, a propagation model is constructed to estimate the initial location of the fault point, and an error correction mechanism is used to optimize the positioning result;

[0067] Step 6: Based on the dynamic system model, the evolution state of the fault location is predicted, and the observation value is fused, and the final fault positioning result is output by the nonlinear filtering method.

[0068] The application forms a closed loop synchronization system by constructing a clock correction model based on Beidou satellite ephemeris data, combining multi-satellite consistency correction and PID feedback control mechanism, which can effectively suppress the error caused by satellite jitter or single point drift, realize microsecond level time service accuracy, and is significantly better than the millisecond level accuracy of traditional GPS or ground reference time service mode. In the wave head extraction aspect, the application first introduces the "Beidou-noise joint kernel function" dynamic threshold algorithm, which combines historical amplitude variation sequence and time service error characteristics for adaptive adjustment, improves the sensitivity and anti-interference ability of wave head detection, and significantly reduces the risk of misjudgment caused by non-fault disturbance. In the fault positioning modeling aspect, the application combines multiple factors such as power grid topology, propagation speed and synchronization deviation, establishes a nonlinear propagation optimization model, accurately estimates the fault point position through numerical iteration, and effectively breaks through the problem of limited accuracy of existing linear model under complex network structure. In order to further improve the stability and accuracy of the positioning result, the application introduces an intelligent filtering mechanism, which uses unscented Kalman filtering algorithm combined with deep learning model to realize the fusion calculation of state prediction and observation value, has good dynamic adaptive ability and nonlinear processing ability, and significantly enhances the stability and universality of the system under various power grid operating environments. In addition, the overall system architecture of the application is clear, the module division is clear, and it can be realized relying on the existing Beidou time service platform and intelligent sampling equipment, has good engineering implementation and popularization and application value, and is especially suitable for the traveling wave fault location demand in various complex scenes such as power transmission network, loop network, cable channel and regional power grid. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating laboriousness. Among them:

[0070] Figure 1 It is the overall structure schematic diagram of the system of the application.

[0071] Figure 2 It is the method flow chart of the embodiment of the application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme of the embodiments of the application will be described clearly and completely in the following with reference to the drawings of the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the described embodiments of the application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the application.

[0073] The Beidou synchronization and intelligent filtering based traveling wave fault location system of the application comprises the following modules: a Beidou synchronization module, a traveling wave sampling module, a wave head extraction module, a time coding module, a fault location module and an intelligent filtering module, which are coordinated through network communication or time triggering mechanism. Each module takes Beidou synchronization time as the unified time reference when running, and constructs the following positioning process chain: time synchronization→ signal sampling→ wave head detection→ time difference construction→ initial positioning→ filtering correction→ output of positioning result.

[0074] Embodiment 1

[0075] As Figure 1 shown, an embodiment of the application provides a Beidou synchronization and intelligent filtering based traveling wave fault location system, comprising:

[0076] (1) Beidou synchronization module

[0077] used for receiving ephemeris data of multiple Beidou satellites, and constructing a clock correction model based on orbital parameters, clock bias and propagation delay information, and generating a standard time signal broadcast to multiple traveling wave sampling terminals;

[0078] The Beidou synchronization module comprises:

[0079] an ephemeris receiving unit used for receiving ephemeris data of at least four Beidou satellites in parallel, including orbital parameters, clock bias and propagation delay information, and calculating a standard time signal therefrom;

[0080] a time synchronization unit used for broadcasting the standard time signal to multiple traveling wave sampling terminals, and correcting local clocks of each terminal through synchronization calibration;

[0081] a time correction feedback unit used for collecting local time bias of each terminal, and dynamically adjusting by using a PID control algorithm to suppress clock drift;

[0082] an ephemeris consistency correction unit used for establishing a clock error model based on ephemeris data of multiple satellites, and calculating error correction values of each satellite by using a least square method optimization algorithm, wherein 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 comprises the following steps:

[0084] Ephemeris data is received from at least four BeiDou satellites, including satellite orbital parameters, clock deviation, and signal propagation delay information. A timestamp set is constructed for each satellite based on the ephemeris data, serving as the basis for subsequent clock synchronization and correction. A clock error model is established based on the ephemeris data of each satellite. The clock error of each satellite at a specific time is calculated based on satellite orbital changes, signal propagation delay, and clock drift factors, forming an error function, expressed as:

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

[0086] The least squares optimization algorithm is used to correct the clock errors of multiple satellites. The objective function is:

[0087]

[0088] Where: ω i The dynamic weight of the i-th satellite is dynamically adjusted based on the satellite's signal quality and orbital stability, ΔT. 卫星i (t) represents the clock error of the i-th satellite, T 同步i (t) represents the ideal synchronization time;

[0089] Based on the optimized correction value, the clock of each traveling wave sampling terminal is updated, the clock error of each terminal is monitored in real time and the correction value is dynamically adjusted to ensure clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction value and optimizes the traveling wave signal according to the feedback signal; the acquired traveling wave signal is intelligently filtered, and the filtering parameters are adjusted in real time according to the signal quality to remove noise.

[0090] In this embodiment, when a fault occurs during power grid operation, the system first obtains a standard time signal via BeiDou satellites. The BeiDou synchronization module can simultaneously receive time information transmitted by multiple BeiDou satellites, analyze this data, remove errors, and ultimately generate a highly accurate unified "standard time".

[0091] This time signal is broadcast to every traveling wave sampling terminal deployed throughout the power grid, enabling them to "start synchronously." In other words, all terminals begin collecting data at the same time, thereby ensuring the consistency and reliability of subsequent processing.

[0092] (2) Traveling wave sampling module

[0093] It is used to control each sampling terminal to synchronously acquire three-phase voltage or current signals based on the received standard time signal, and to 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 a received time synchronization signal of the Beidou synchronization module, the setting of the sampling period being based on a signal collection requirement and a system real-time state, to ensure that each traveling wave sampling terminal is consistent with the time of the Beidou synchronization module;

[0096] a data collection unit configured to collect three-phase current or voltage signals according to a preset sampling accuracy and sampling rate, and store the collected data in a specified format, the sampling accuracy and sampling rate being configured according to a signal collection requirement;

[0097] a signal verification unit configured to preliminarily verify the collected data, and eliminate abnormal data and high-frequency noise signals that do not meet a standard, the standard of the abnormal data including amplitude and frequency threshold judgments, and the collected data being verified according to a data consistency standard;

[0098] a signal denoising unit configured to use a wavelet transform filtering algorithm to smooth the sampling signals and remove high-frequency noise interference, a filtering window size and a standard deviation value of the Gaussian filtering algorithm being adjusted according to characteristics of the sampling signals.

[0099] In the embodiment, after receiving the standard time, each terminal starts to collect three-phase signals (voltage or current) at a location at a very high frequency (e.g., 20,000 times per second), and saves the original data.

[0100] After the collection is completed, to avoid interference from "noise", the system uses filtering technology (e.g., wavelet transform or Gaussian filtering) to remove the noise. At the same time, if there are problems such as amplitude abnormalities or frequency mutations in the data, the system will automatically eliminate them, to ensure that only effective and clean signal data is retained.

[0101] (3) Wave head extraction module

[0102] configured to perform sliding window processing on the sampling signals under a synchronous time base, and determine a wave head arrival time through a dynamic threshold, to generate wave head information with a time stamp;

[0103] The wave head extraction module includes:

[0104] a sliding window processing unit configured to perform sliding window segmentation processing on the sampling signals under the Beidou time base, to extract transient amplitude variation observation values, the window size being dynamically adjusted according to signal main frequency characteristics and system running states;

[0105] a wave head detection unit configured to compare the current amplitude variation observation values with a dynamic threshold, and determine a wave head arrival time according to the comparison, to generate a time stamp with a Beidou time stamp;

[0106] The threshold determination and optimization unit is used for constructing a signal feature correlation radius related to a Beidou timing error, generating a fault feature convergence threshold dynamically based on the correlation radius and a historical amplitude variation reference sequence by using a mean shift algorithm, and performing dynamic smoothing processing on the threshold by using a Beidou-noise joint kernel function through the interface with the intelligent filtering module, so as to improve the sensitivity and noise resistance of the wave head detection.

[0107] Specifically, the threshold determination and optimization unit is constructed as follows:

[0108] The signal feature correlation radius is calculated, the Beidou timing error and the signal main frequency characteristics are combined, the threshold adjustment is matched with the time synchronization accuracy and the signal frequency domain characteristics, and the expression is as follows:

[0109]

[0110] Wherein, R BNC is the signal feature correlation radius, used for restricting the action range of the kernel function; f signal is the signal main frequency, analyzed in real time by the sliding window processing unit; sigma BNC is the Beidou timing error, dynamically corrected according to the clock jitter;

[0111] Based on the Beidou synchronization historical data and the joint kernel function, the fault feature convergence threshold is iteratively calculated, the non-fault amplitude variation interference is suppressed, and the sensitivity of the wave head detection is improved, and the formula is as follows:

[0112]

[0113] Wherein, T conv (t) is the fault feature convergence threshold, reflecting the statistical consistency of the current signal and the historical wave head feature; delta A obs (t) is the transient amplitude variation observation value, extracted in real time by the sliding window processing unit; delta Aref (k) is the historical amplitude variation reference sequence, stored according to the Beidou timestamp alignment; K BNC is the Beidou-noise joint kernel function, which fuses the timing error and the noise distribution characteristics; N sync is the Beidou synchronization reference point number, dynamically configured by the timing interval and the sampling rate;

[0114] The threshold is dynamically smoothed through the intelligent filtering module, the instantaneous noise disturbance is suppressed, and the fault feature continuity is maintained, and the expression is as follows:

[0115] T final (t) = beta * T conv (t) + (1-beta) * T final (t-1);

[0116] Wherein, T final(t) is a dynamic threshold value for real-time wave head detection determination; β is a fault feature attenuation factor, which is self-adaptively optimized by the intelligent filtering module according to historical fault data.

[0117] In the embodiment, the so-called "wave head" is a small section of signal with obvious change generated when the power failure just occurs, and the target of the system is to accurately find the time of the moment.

[0118] The system continuously scans whether there is a "mutation" in the signal through a sliding window. If the energy of a certain section of signal suddenly rises and exceeds the dynamically calculated threshold value, it is determined that this is a "wave head", and the accurate time (time stamp) when it occurs is recorded.

[0119] The threshold value is not fixed, but is automatically adjusted according to historical waveforms, time error and noise conditions, so that the system can accurately identify the wave head in different environments.

[0120] (4) Time encoding module

[0121] For sorting the wave head arrival time difference between a plurality of sampling terminals according to the power grid topology, constructing a time difference vector and converting it into a power grid position parameter;

[0122] The time encoding module comprises:

[0123] A time difference calculation unit for calculating the wave head arrival time difference between a plurality of sampling terminals, the time difference calculation being based on the time stamp data of the sampling terminals receiving the wave head signal;

[0124] A sorting unit for sorting the time difference according to the power grid topology, the sorting process generating 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 reflecting the spatial distribution of the power grid;

[0125] An encoding generation unit for converting the sorted time difference vector into a position parameter, the position parameter being obtained by combining the time difference and the power grid position mapping relationship based on the linear mapping rule of the power grid topology.

[0126] In the embodiment, each terminal has found the time when it sees the wave head, so now the "time of seeing" can be compared.

[0127] The system calculates the wave head arrival time difference between the terminals, and sorts and encodes according to the structure of the power grid (which terminal is at which position), to finally form a "time difference vector".

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

[0129] (5) Fault location module

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

[0131] The fault location module includes:

[0132] The spatiotemporal propagation modeling unit is used to construct a nonlinear propagation model based on the wavefront arrival time difference and spatial location of multiple sampling terminals. The model takes into account propagation speed, path characteristics and power grid topology factors.

[0133] The initial positioning unit 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] The error correction unit is used to correct the initial position estimate based on factors such as synchronization deviation and power grid structure disturbance.

[0135] The nonlinear optimization unit is used to model the localization problem as a minimization problem of the following objective function, as shown in the formula:

[0136]

[0137] Where: P f P is the fault location vector; i Let Δt be the position of the i-th traveling wave sampling terminal; i v(P) represents the wavefront arrival time difference of the i-th terminal; f ,P i () represents the speed of travel wave propagation; For BeiDou timing error; ω i Φ(P) represents the observation weight of the i-th terminal. f ,T grid ) represents the power grid topology constraint function; T grid For topological description; Ψ(P f H filter H is the filter correction function; filter The historical filtering state is represented by λ and γ, which are the weight systems of the constraint terms, respectively.

[0138] The optimization process combines nonlinear propagation path, sampling noise, timing deviation, and power grid structure information to perform global estimation and dynamic correction of the fault location, and outputs the final accurate location result.

[0139] In this embodiment, the system inputs the time difference vector and the location data of each terminal in the power grid into a positioning model. This model calculates a preliminary fault location based on factors such as radio wave propagation speed, power grid structure, and timing errors.

[0140] This process uses a non-linear mathematical algorithm for optimization, that is, iterative adjustment of the fault point position estimate until the error is minimized.

[0141] (6) Intelligent filtering module

[0142] For predicting the evolution state of the fault location based on a dynamic system model, and fusing observation values, the final accurate fault positioning result is output by a non-linear filtering method.

[0143] The intelligent filtering module comprises:

[0144] A state prediction unit is configured to input the initial positioning coordinates into a state equation, and predict the current fault location according to a dynamic system model, wherein the dynamic system model is based on the physical characteristics of fault propagation, and combines the initial position, propagation time and power grid topology to predict the fault location at the next time;

[0145] A covariance estimation unit is configured to dynamically adjust the covariance matrix of state noise and observation noise according to the current operating environment and signal fluctuation by using a deep learning adaptive mechanism, wherein the deep learning mechanism optimizes the covariance matrix according to real-time feedback.

[0146] A filtering update unit is configured to fuse the state prediction result and the observation value by using an unscented Kalman filter (UKF) method, and update the fault positioning result, wherein the UKF method approximates the state space by a 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, the signal is affected by various interferences, and the result of the preliminary positioning may still have deviations.

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

[0149] This filtering process uses 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 shown in Figure 2 Another embodiment of the present application provides a positioning method of a traveling wave fault location system based on Beidou synchronization and intelligent filtering, comprising the following steps:

[0151] Step 1: receiving ephemeris data of multiple Beidou satellites, and constructing a clock correction model based on satellite orbit parameters, clock bias and signal propagation delay to 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 check the collected signals to eliminate noise and abnormal data;

[0153] Step 3: Under the synchronous time base, the sampling signals are processed by sliding window, and the wave head arrival time is determined based on the dynamic threshold to generate wave head information containing time stamp;

[0154] Step 4: According to the power grid topology, the wave head arrival time difference between multiple sampling terminals is sorted to construct a time difference vector, which is converted into a power grid location parameter;

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

[0156] Step 6: Based on the dynamic system model, the evolution state of the fault location is predicted, and the observation value is fused to output the final fault positioning result by nonlinear filtering method.

[0157] Embodiment 2

[0158] In a typical application scenario, the traveling wave fault location system based on Beidou synchronization and intelligent filtering described in the application is deployed in a 110kV double circuit line overhead transmission project in a certain area. The system installs one traveling wave sampling terminal at each end of each line, and configures a Beidou time service module to obtain a B1 frequency band standard time signal, with a time service accuracy better than 500 nanoseconds. The sampling terminal sets the sampling rate to 20kHz, the sampling window length to 1024 points, and the filtering algorithm to wavelet decomposition and Gaussian function joint filtering, and dynamically adjusts the filtering threshold according to the real-time signal quality. In this actual operating environment, a single-phase ground fault is simulated, and the wave head extraction module can detect the effective wave head signal and accurately mark the Beidou time stamp within 10ms after the fault occurs. The time coding module generates a time difference vector based on the field topology relationship, and the fault location module estimates the location in combination with the topology structure and the propagation speed model, and then uses the UKF method for dynamic correction through the intelligent filtering module. The final fault location error is less than 250 meters, the overall response time of the system is less than 30ms, and the synchronization accuracy, wave head recognition accuracy and fault location capability of the application in typical working conditions of high-voltage transmission lines are fully verified, which has good engineering adaptability and practicality.

[0159] Embodiment 3

[0160] In another anti-interference application scenario, to verify the robustness of the system in a complex interference environment, the system is deployed in a 35kV urban ring network line, and various interference factors are simulated by signal injection, including arc oscillation, power frequency harmonic superposition, high frequency transient peak and communication link interruption (simulating a 5ms delay in the sampling terminal). Under the above conditions, the traditional fixed threshold wave head detection method fails or misjudges, while the wave head extraction module of the application constructs a dynamic threshold combining the historical amplitude trend and the Beidou synchronization error, and based on the "Beidou-noise joint kernel function", it can effectively suppress the interference of non-fault transient disturbances. Under the test conditions of high frequency noise signal intensity up to 45dB, the system can still extract the effective wave head stably, and use the time coding module to construct the time difference vector, and the positioning module completes the initial positioning through the nonlinear propagation modeling and error correction mechanism. In the filtering stage, the intelligent filtering module combines the UKF algorithm and the deep learning dynamic covariance adjustment mechanism to automatically identify and weigh abnormal observations, significantly enhancing the robustness of the system to non-ideal factors such as time drift and observation bias. The final experimental results show that under the interference conditions, the fault positioning error of the system is controlled within 300 meters, and the wave head recognition accuracy is more than 92%, which fully shows that the application still has stable operation and high precision positioning ability under the conditions of limited signal quality and complex power waveform disturbance.

[0161] In summary, the application can effectively solve the core problems of existing traveling wave positioning technology in terms of low timing accuracy, unstable wave head recognition, poor anti-interference ability and insufficient positioning accuracy. By constructing a high-precision Beidou clock synchronization mechanism, an adaptive wave head detection algorithm, a nonlinear propagation modeling with fusion topology, and an intelligent filtering and positioning correction module, the application realizes high accuracy, high robustness and high response efficiency of fault point positioning. The system architecture is clear, the module functions are independent and have high synergy, and has good engineering implementation foundation and expansion ability, which is suitable for fault rapid diagnosis and positioning requirements in various complex power systems such as high-voltage transmission network, urban ring network and regional distribution network. It has innovation and effectiveness in system synchronization, data acquisition, fault recognition, modeling and output, and has important technical popularization value and industrial application prospect.

[0162] In the description of the application, reference can be made to terms such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. It is intended that there are at least one embodiment or example described in connection with the particular feature, structure, material or characteristic described in connection with the embodiment or example. Moreover, the described particular feature, structure, material or characteristic can be combined in any suitable manner in one or more embodiments or examples. Furthermore, the skilled person can combine and combine features of different embodiments or examples described in the specification and characteristics of different embodiments or examples, without mutual contradiction.

[0163] Any process or method descriptions or blocks in flow charts described herein and elsewhere in this specification can be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps in the process. As well, the preferred embodiments of this application can be implemented in hardware, software, or both hardware and software.

[0164] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or replacements within the technical scope disclosed in the present application, which should 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 system for traveling wave fault location based on Beidou synchronization and intelligent filtering, characterized in that, The application relates to a fault location system based on BeiDou navigation satellite system (BDS) time synchronization, which comprises the following modules: A BeiDou synchronization module is used for receiving ephemeris data of multiple BeiDou satellites, and constructing a clock correction model based on orbit parameters, clock bias and propagation delay information, and generating a standard time signal broadcast to multiple traveling wave sampling terminals; A traveling wave sampling module is used for controlling the synchronous collection of three-phase voltage or current signals of each sampling terminal based on the received standard time signal, and removing noise and abnormal data through filtering and checking; A wave head extraction module is used for performing sliding window processing on the sampling signals under the synchronous time base, and determining the wave head arrival time through a dynamic threshold, and generating wave head information with a time stamp; A time encoding module is used for sorting the wave head arrival time difference between multiple sampling terminals according to the power grid topology structure, constructing a time difference vector and converting the time difference vector into power grid position parameters; A fault location module is used for constructing a propagation model based on the time difference vector and the power grid spatial topology data, estimating the initial position of the fault point, and optimizing the positioning result by using an error correction mechanism; An intelligent filtering module is used for predicting the evolution state of the fault position based on a dynamic system model, fusing observation values, and outputting the final accurate fault location result through a nonlinear filtering method; The fault location module comprises: A space-time propagation modeling unit is used for constructing a nonlinear propagation model based on the wave head arrival time difference of multiple sampling terminals and the spatial positions thereof, wherein the model considers the propagation speed, path characteristics and power grid topology influencing factors; An initial positioning unit is used for inputting the time difference vector and the spatial data into the propagation model, and calculating the initial position of the fault point through numerical iteration; An error correction unit is used for correcting the initial position estimation value according to the synchronization bias and the power grid structure disturbance factors; A nonlinear optimization unit is used for modeling the positioning problem as a minimization problem of the following objective function: Wherein: 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 wave head arrival time difference 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 ) represents the power grid topology constraint function; T grid For topological description; Ψ(P f H filter H is the filter correction function; filter The historical filtering state is represented by λ and γ, which are the weight systems of the constraint terms, respectively. The optimization process combines the nonlinear propagation path, sampling noise, time synchronization bias and power grid structure information to globally estimate and dynamically correct the fault position, and outputs the final accurate positioning result. 2.The system of claim 1, wherein, The BeiDou synchronization module comprises: An ephemeris receiving unit is used for receiving ephemeris data of at least four BeiDou satellites in parallel, including orbit parameters, clock bias and propagation delay information, and calculating a standard time signal based on the ephemeris data; A time synchronization unit is used for broadcasting the standard time signal to multiple traveling wave sampling terminals, and correcting the local clock of each terminal through a synchronization calibration method; A time correction feedback unit is used for collecting the local time synchronization bias of each terminal, and dynamically adjusting the time synchronization bias by using a PID control algorithm to suppress clock drift; An ephemeris consistency correction unit is used for establishing a clock error model based on the ephemeris data of multiple satellites, and calculating error correction values of the satellites by using a least square method optimization algorithm, wherein the error correction values are used for unifying the time synchronization reference of each terminal, and ensuring that the overall synchronization accuracy of the system meets the positioning requirement. 3.The system of claim 2, wherein, The optimization process of the ephemeris consistency correction unit comprises the following steps: The ephemeris data of at least four Beidou satellites are received, including the orbit parameters of the satellites, clock bias, and signal propagation delay information, and a timestamp set is constructed for each satellite based on the ephemeris data, as the basis for subsequent clock synchronization and correction; a clock error model is established according to the ephemeris data of each satellite, the clock error of each satellite at a specific time is calculated based on the factors of satellite orbit change, signal propagation delay and clock drift, an error function is formed, and the expression is: ΔT 卫星i (t) = f (satellite orbit parameters, signal propagation delay, satellite clock drift) ; The clock errors of multiple satellites are corrected using a least squares optimization algorithm, and the optimization objective function is: Wherein: 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; According to the optimized correction value, the clock of each traveling wave sampling terminal is updated, the clock error of each terminal is monitored in real time, and the correction value is dynamically adjusted to ensure the clock synchronization accuracy; if the positioning error exceeds the set range, the system adjusts the time synchronization correction value and optimizes the traveling wave signal according to the feedback signal; the collected traveling wave signal is intelligently filtered, the filtering parameters are adjusted in real time according to the signal quality, and the noise is removed.

4. The system according to claim 1, wherein, The traveling wave sampling module comprises: A sampling control unit is configured to set a sampling period according to the received time synchronization signal of the Beidou synchronization module, and the setting of the sampling period is based on the signal collection 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; A data acquisition unit is configured to collect three-phase current or voltage signals according to the preset sampling accuracy and sampling rate, and store the collected data in a specified format, wherein the sampling accuracy and sampling rate are configured according to the signal collection requirements; A signal verification unit is configured to preliminarily verify the collected data, and remove abnormal data and high-frequency noise signals that do not meet the standard, wherein the standard of the abnormal data includes amplitude and frequency threshold judgment, and the collected data is verified according to the data consistency standard; A signal denoising unit is configured to use a wavelet transform filtering algorithm to smooth the sampling signal and remove high-frequency noise interference, and the filter window size and standard deviation value of the wavelet transform filtering algorithm are adjusted according to the characteristics of the sampling signal.

5. The system according to claim 1, wherein, The wave head extraction module comprises: A sliding window processing unit is configured to perform sliding window segmentation processing on the sampling signal under the Beidou time reference, extract transient amplitude variation observation values, and dynamically adjust the window size according to the signal main frequency characteristics and the system running state; A wave head detection unit is configured to compare the current amplitude variation observation value with a dynamic threshold value, determine the wave head arrival time based on the comparison, and generate a timestamp with a Beidou time stamp; A threshold determination and optimization unit is configured to construct a signal feature correlation radius related to the Beidou time error, dynamically generate a fault feature convergence threshold value based on the correlation radius and a historical amplitude variation reference sequence using a mean shift algorithm, and perform dynamic smoothing processing on the threshold value using a Beidou-noise joint kernel function through the interface with the intelligent filtering module to improve the sensitivity and noise resistance of the wave head detection.

6. The system according to claim 5, wherein, The threshold determination and optimization unit is constructed as follows: The signal feature correlation radius is calculated, the Beidou time error is combined with the signal main frequency characteristics, the threshold value adjustment is matched with the time synchronization accuracy and the signal frequency domain characteristics, and the expression is: Wherein: R BNC is the signal feature correlation radius, used to constrain the kernel function range of action; f signal is the signal main frequency, analyzed by the sliding window processing unit in real time; σ BNC is the Beidou timing error, dynamically corrected according to clock jitter; Based on the Beidou synchronization historical data and the joint kernel function, the fault feature convergence threshold is iteratively calculated to suppress the non-fault amplitude variation interference and improve the wave head detection sensitivity, and the formula is: Wherein: T conv (t) is a fault feature convergence threshold, reflecting the statistical consistency of the current signal and the historical wave head feature; ΔA obs (t) is a transient amplitude variation observation value, extracted in real time by a sliding window processing unit; Δ Aref (k) is a historical amplitude variation reference sequence, stored according to the Beidou timestamp; K BNC is a Beidou-noise joint kernel function, which combines the timing error and noise distribution characteristics; N sync The number of Beidou synchronization reference points is dynamically configured by the timing interval and the sampling rate; The threshold is dynamically smoothed by the intelligent filtering module to suppress the instantaneous noise disturbance and maintain the continuity of the fault feature, and the expression is: T final (t) = β x T conv (t) + (1 - β) x T final (t - 1); Wherein: T final (t) is a dynamic threshold value for real-time wave head detection determination; β is a fault feature attenuation factor, which is self-adaptively optimized by the intelligent filtering module according to historical fault data.

7. The system according to claim 1, wherein, The time encoding module comprises: A time difference calculation unit is configured to calculate the wave head arrival time difference between a plurality of sampling terminals, and the time difference calculation is based on the time stamp data of the wave head signals received by the sampling terminals; An ordering unit is configured to order the time difference according to the power grid topology, and the ordering process generates an ordered time difference vector according to the positions of the sampling terminals and the topological connection relationship of the power grid, and the ordered time difference vector accurately reflects the spatial distribution of the power grid; An encoding generation unit is configured to convert the ordered time difference vector into a position parameter, and the position parameter is obtained by combining the time difference and the mapping relationship between the time difference and the power grid position based on the linear mapping rule of the power grid topology. 8.The system of claim 1, wherein, The intelligent filtering module comprises: A state prediction unit is configured to input the initial positioning coordinates into the state equation, and predict the current fault position according to a dynamic system model, and the dynamic system model is based on the physical characteristics of fault propagation, and combines the initial position, the propagation time and the power grid topology to predict the fault position at the next time; A covariance estimation unit is configured to dynamically adjust the covariance matrix of the state noise and the observation noise according to the current operating environment and the signal fluctuation by using a deep learning adaptive mechanism, and the deep learning adaptive mechanism optimizes the covariance matrix according to real-time feedback; a filtering update unit is configured to fuse the state prediction result and the observation value by using an unscented Kalman filter (UKF) method, and update the fault positioning result, and the UKF method approximates the state space by a nonlinear extension, and accurately processes the measurement error and the process noise in the nonlinear dynamic system.

9. The method according to any one of claims 1-8, wherein, The method comprises the following steps: Step 1: receiving ephemeris data of a plurality of Beidou satellites, and constructing a clock correction model based on satellite orbit parameters, clock bias and signal propagation delay to generate a standard time signal and broadcast the standard time signal to a plurality of traveling wave sampling terminals; Step 2: based on the standard time signal, controlling each sampling terminal to synchronously collect three-phase voltage or current signals, and filtering and verifying the collected signals to remove noise and abnormal data; Step 3: under the synchronous time base, performing sliding window processing on the sampling signals, and determining the wave head arrival time based on a dynamic threshold to generate wave head information containing time stamps; Step 4: according to the power grid topology, ordering the wave head arrival time difference between a plurality of sampling terminals, constructing a time difference vector, and converting the time difference vector into a power grid position parameter; Step 5: constructing a propagation model based on the time difference vector and the power grid spatial topology data, estimating the initial position of the fault point, and optimizing the positioning result by using an error correction mechanism; Step 6: predicting the evolution state of the fault position based on a dynamic system model, and fusing the observation value to output the final fault positioning result by using a nonlinear filtering method.

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