Intelligent fault recording and traveling wave ranging integrated control method and device

Abstract

The invention discloses an intelligent fault recording and traveling wave distance measurement integrated control method and device, and belongs to the technical field of fault positioning, and the method comprises the steps: collecting the power frequency electric quantity data and high-frequency traveling wave data of monitoring points at two sides of a fault line; calculating a first fault distance estimation value based on the power frequency electrical quantity data, and identifying an initial traveling wave head based on the high-frequency traveling wave data to calculate a second fault distance estimation value; defining an evaluation time period, and calculating a signal-to-noise ratio value in the evaluation time period; and according to the signal-to-noise ratio value, determining the contribution degree of the first fault distance estimation value and the second fault distance estimation value in generating final fault positioning distance calculation, and performing comprehensive processing on the first fault distance estimation value and the second fault distance estimation value in combination with the contribution degree to obtain a final fault positioning distance. According to the invention, the accuracy and reliability of fault positioning are improved, and positioning requirements under different fault conditions and signal environments are met.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and in particular to an intelligent fault recording and traveling wave ranging integrated control method and device. Background Art

[0002] Traditional power system fault location methods primarily fall into two categories: the power frequency impedance method and the traveling wave ranging method. The power frequency impedance method calculates the fault distance by constructing a line impedance model based on the post-fault power frequency electrical quantity changes. This method has the advantages of a relatively simple algorithm and low equipment requirements, but its accuracy is limited under complex fault conditions. The traveling wave ranging method locates faults based on the propagation characteristics of high-frequency transient traveling wave signals generated by the fault. It offers the advantages of high ranging accuracy and independence from system operating mode, but it requires high signal quality and significantly degrades performance in the presence of severe noise interference.

[0003] In existing technologies, the power frequency impedance method and traveling wave ranging method are typically used independently as fault location methods, lacking an effective fusion mechanism to leverage the strengths of both methods. When the signal environment is complex and noise interference is severe, a single method cannot guarantee accurate and reliable positioning. Furthermore, existing technologies lack an adaptive mechanism to dynamically adjust the weights of different positioning methods based on actual signal quality. This makes it impossible to achieve optimal positioning results under varying fault conditions and signal environments, impacting the overall performance of the fault location system. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to adaptively adjust the weight contribution of different positioning methods according to signal quality through the intelligent fusion mechanism of the power frequency impedance method and the traveling wave ranging method, so as to improve the fault location accuracy and enhance the reliability, while overcoming the limitations of a single method in a complex signal environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an integrated control method for intelligent fault recording and traveling wave ranging, comprising: synchronously collecting power frequency electrical quantity data and high-frequency traveling wave data from monitoring points on both sides of the fault line through an integrated control device; calculating a first fault distance estimate based on the power frequency electrical quantity data, and identifying an initial traveling wave head based on the high-frequency traveling wave data to calculate a second fault distance estimate; defining an evaluation time period for the initial traveling wave head identified in the high-frequency traveling wave data, and calculating a signal-to-noise ratio value within the evaluation time period; determining, based on the signal-to-noise ratio value, the contribution of the first fault distance estimate and the second fault distance estimate in generating a final fault location distance calculation, and performing comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain a final fault location distance.

[0007] As a preferred solution of the integrated control method of intelligent fault recording and traveling wave ranging described in the present invention, wherein: the synchronous collection of industrial frequency electrical quantity data and high-frequency traveling wave data of the monitoring points on both sides of the fault line by the integrated control device includes: performing high-precision clock synchronization and channel status self-test; collecting the industrial frequency electrical quantity data and the high-frequency traveling wave data in parallel; monitoring the energy change of the high-frequency traveling wave data; and when the energy change of the high-frequency traveling wave data exceeds the energy fluctuation benchmark determined based on the historical statistical characteristics of the high-frequency traveling wave data, adding an association identifier to the collected industrial frequency electrical quantity data corresponding to the energy change moment of the high-frequency traveling wave data; the association identifier is used to indicate that the industrial frequency electrical quantity data with the association identifier is preferentially used to calculate the first fault distance estimation value.

[0008] As a preferred solution of the integrated control method of intelligent fault recording and traveling wave ranging described in the present invention, wherein: the calculation based on the power frequency electrical quantity data to obtain the first fault distance estimate includes: preferentially selecting the power frequency electrical quantity data with the associated identifier; constructing a fault circuit voltage balance equation based on the preferentially selected power frequency electrical quantity data; and using an iterative numerical analysis method to solve the fault circuit voltage balance equation to obtain the first fault distance estimate.

[0009] As a preferred solution of the integrated control method of intelligent fault recording and traveling wave ranging described in the present invention, the method includes: identifying the initial traveling wave head based on the high-frequency traveling wave data to calculate the second fault distance estimate, including: applying wavelet transform to perform multi-scale decomposition on the high-frequency traveling wave data; extracting the modulus maximum point on the high-frequency component after the multi-scale decomposition; determining the arrival time of the initial traveling wave head based on the time series characteristics of the modulus maximum point; and calculating the second fault distance estimate based on the arrival time of the initial traveling wave head and in combination with the principle of double-end traveling wave ranging.

[0010] As a preferred solution of the integrated control method of intelligent fault recording and traveling wave ranging described in the present invention, wherein: the defining of the evaluation time period includes defining the noise evaluation window before the arrival moment of the initial traveling wave head and the signal evaluation window after the arrival moment of the initial traveling wave head; the calculating of the signal-to-noise ratio value within the evaluation time period includes calculating the noise level based on the high-frequency traveling wave data in the noise evaluation window, and calculating the signal plus noise level based on the high-frequency traveling wave data in the signal evaluation window, and then obtaining the signal-to-noise ratio value through the noise level and the signal plus noise level.

[0011] As a preferred embodiment of the integrated control method for intelligent fault recording and traveling wave ranging described in the present invention, a first signal-to-noise ratio threshold and a second signal-to-noise ratio threshold are set, wherein the first signal-to-noise ratio threshold is lower than the second signal-to-noise ratio threshold; based on the comparison result of the signal-to-noise ratio value with the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, a first contribution value of the first fault distance estimation value and a second contribution value of the second fault distance estimation value are calculated using a preset mapping rule; wherein the preset mapping rule is as follows: when the signal-to-noise ratio value is lower than the first signal-to-noise ratio threshold, the first contribution value and the second contribution value are determined according to a preset first-level configuration rule, such that the first contribution value is greater than the second contribution value; when the signal-to-noise ratio value is higher than the second signal-to-noise ratio threshold, the first contribution value and the second contribution value are determined according to a preset second-level configuration rule, such that the second contribution value is greater than the first contribution value; and when the signal-to-noise ratio value is between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, the first contribution value and the second contribution value are calculated using a preset function mapping relationship based on the relative position of the signal-to-noise ratio value between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold.

[0012] As a preferred solution of the integrated control method of intelligent fault recording and traveling wave ranging described in the present invention, it further includes: performing consistency verification on the first fault distance estimation value and the second fault distance estimation value; the comprehensive processing includes: determining the corresponding first weight coefficient and second weight coefficient respectively through a preset mapping relationship based on the first contribution value and the second contribution value; and performing weighted fusion on the first fault distance estimation value and the second fault distance estimation value based on the first weight coefficient and the second weight coefficient to obtain the final fault location distance.

[0013] Another object of the present invention is to provide an intelligent fault recording and traveling wave ranging integrated control device.

[0014] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent fault recording and traveling wave ranging integrated control device, comprising: a data acquisition module, used to synchronously collect power frequency electrical quantity data and high-frequency traveling wave data of monitoring points on both sides of the fault line through an integrated control device; a preliminary positioning module, used to calculate a first fault distance estimate based on the power frequency electrical quantity data, and identify an initial traveling wave head based on the high-frequency traveling wave data to calculate a second fault distance estimate; a signal quality evaluation module, used to define an evaluation time period for the initial traveling wave head identified in the high-frequency traveling wave data, and calculate a signal-to-noise ratio value within the evaluation time period; a fusion positioning module, used to determine the contribution of the first fault distance estimate and the second fault distance estimate in generating a final fault location distance calculation based on the signal-to-noise ratio value, and perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain a final fault location distance.

[0015] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the intelligent fault recording and traveling wave ranging integrated control method are implemented.

[0016] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the intelligent fault recording and traveling wave ranging integrated control method are implemented.

[0017] The present invention has the following beneficial effects: The present invention uses an integrated control device to synchronously collect power-frequency electrical quantity data and high-frequency traveling wave data, calculates two independent fault distance estimates, and determines the contribution weight of each estimate based on the signal-to-noise ratio of the traveling wave signal, thus achieving an intelligent fusion of the power-frequency impedance method and the traveling wave ranging method. The present invention can dynamically adjust the weights of different positioning methods based on actual signal quality, relying more on the stability of the power-frequency impedance method when the signal-to-noise ratio is low, and fully utilizing the high-precision characteristics of the traveling wave ranging method when the signal-to-noise ratio is high. This effectively overcomes the limitations of a single method, improves the accuracy and reliability of fault location, and adapts to positioning requirements under different fault conditions and signal environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1The present invention provides an overall flow chart of an integrated control method for intelligent fault recording and traveling wave ranging according to an embodiment of the present invention.

[0020] Figure 2 This is a diagram of the application environment of an integrated control method for intelligent fault recording and traveling wave ranging provided by one embodiment of the present invention.

[0021] Figure 3 A schematic diagram of the overall structure of an intelligent fault recording and traveling wave ranging integrated control device provided by one embodiment of the present invention.

[0022] Figure 4 A diagram of a computer device for an integrated control method of intelligent fault recording and traveling wave ranging provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, with reference to Figure 1 , is an embodiment of the present invention, which provides an integrated control method for intelligent fault recording and traveling wave ranging, including:

[0025] S100: Synchronously collects power frequency electrical quantity data and high frequency traveling wave data from monitoring points on both sides of the fault line through an integrated control device;

[0026] S200: Calculating a first fault distance estimate based on power frequency electrical quantity data, and identifying an initial traveling wave head based on high frequency traveling wave data to calculate a second fault distance estimate;

[0027] S300: defining an evaluation time period for an identified initial traveling wave head in the high-frequency traveling wave data, and calculating a signal-to-noise ratio value within the evaluation time period;

[0028] S400: Determine, based on the signal-to-noise ratio, the contribution of the first fault distance estimate and the second fault distance estimate in generating a final fault location distance calculation, and perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain a final fault location distance.

[0029] It should be noted that fault location in power system transmission lines is one of the key technologies to ensure the safe and stable operation of the power grid. Traditional fault location methods mainly include the impedance method and the traveling wave method. The impedance method is based on the calculation of power frequency electrical quantities. It has the advantages of relatively simple algorithms and low communication requirements, but its accuracy is easily affected by factors such as system operation mode, transition resistance, and load current. The traveling wave method locates based on the propagation characteristics of the fault traveling wave. It has high theoretical accuracy and is not affected by the system operation mode, but it requires a high data sampling rate, and its performance degrades significantly when the traveling wave signal is weak or the noise is large. In existing technologies, only one method is often used alone, which makes it difficult to maintain high positioning accuracy under various fault conditions.

[0030] Therefore, in order to address the problem of insufficient adaptability of the above-mentioned single positioning method, the intelligent fusion of the two positioning methods is realized through steps S100-S400: first, the high-precision synchronous acquisition of different frequency characteristic data is ensured through an integrated control device, providing a reliable data basis for subsequent analysis; then, the technical advantages of the impedance method and the traveling wave method are used to calculate the fault distance estimate, giving full play to their respective strengths; then, through the quantitative evaluation of the traveling wave signal quality, the reliability of each method under the current fault conditions is dynamically judged; finally, based on the signal quality evaluation results, the fusion weight is intelligently determined to achieve complementary advantages and improve the accuracy of the two methods, so that more accurate and reliable fault location results can be obtained under different fault conditions and signal environments.

[0031] Example 2, reference Figure 1 and Figure 2 , which is an embodiment of the present invention, provides an integrated control method for intelligent fault recording and traveling wave ranging.

[0032] Aiming at the fact that related technologies cannot output reliable and accurate fault location data for different fault environments, an intelligent fault recording and traveling wave ranging integrated control method is proposed. Two fault distance estimation values ​​are calculated based on the collected industrial frequency electrical quantity data and high-frequency traveling wave data respectively. The signal-to-noise ratio value is determined by evaluating the quality of the traveling wave signal. The contribution of the two estimation values ​​is dynamically determined based on the signal-to-noise ratio value. The two methods are intelligently fused to obtain the final fault location distance.

[0033] The intelligent fault recording and traveling wave ranging integrated control method provided by the embodiment of the present invention can be applied to Figure 2 The power system fault location application environment shown in the figure. The integrated control device uses the power communication network to transmit data and synchronize clocks with monitoring points on both sides of the fault line. The data storage system is used to store collected power frequency electrical quantity data, high-frequency traveling wave data, related system operating parameters, and historical fault data. The data storage system can be integrated into the integrated control device or deployed on a remote server via a network connection.

[0034] When a fault occurs on a transmission line, the integrated control device synchronously collects power frequency electrical quantity data and high-frequency traveling wave data from monitoring points on both sides of the fault line; a first fault distance estimate is calculated based on the power frequency electrical quantity data, and an initial traveling wave head is identified based on the high-frequency traveling wave data to calculate a second fault distance estimate; an evaluation time period is defined for the initial traveling wave head identified in the high-frequency traveling wave data, and a signal-to-noise ratio value within the evaluation time period is calculated; based on the signal-to-noise ratio value, the contribution of the first fault distance estimate and the second fault distance estimate in generating the final fault location distance calculation is determined, and the two fault distance estimates are comprehensively processed based on the contribution to obtain the final fault location distance.

[0035] Monitoring points can include, but are not limited to, digital protection devices, fault recorders, and intelligent electronic devices installed within substations. These devices possess high-precision data acquisition and clock synchronization capabilities. Integrated control devices can be dedicated fault location devices, components of substation automation systems, or fault analysis systems within power grid dispatch centers. Communication networks can utilize fiber optic communications, power carrier communications, or wireless communications to ensure real-time and reliable data transmission.

[0036] Specifically, the integrated control device includes a data acquisition module, a signal processing module, a communication interface module, and a power management module. The data acquisition module is equipped with a high-precision analog-to-digital converter and a multi-channel input interface, which is used to synchronously receive the industrial frequency electrical quantity data and high-frequency traveling wave data transmitted by the monitoring points on both sides. The industrial frequency sampling frequency is not less than 2kHz to meet the requirements of baseband analysis, and the high-frequency sampling frequency is not less than 1MHz to meet the requirements of high-frequency characteristic analysis of traveling wave signals. The signal processing module adopts a high-performance digital signal processor or field programmable gate array, integrating core algorithm modules such as wavelet transform algorithm library, modulus maximum detection algorithm, iterative numerical analysis algorithm, etc., to realize real-time analysis and processing of collected data. The communication interface module supports multiple communication protocols to ensure that the data transmission delay between the monitoring points is less than 100ms and the communication reliability is greater than 99.9%. The power management module is equipped with a main and standby power switching function to ensure the stable operation of the equipment under various operating conditions.

[0037] The integrated control device also incorporates a clock synchronization module. Using the high-precision time reference provided by GPS or Beidou satellites, it achieves microsecond-level clock synchronization for data collected from monitoring points on both sides. Synchronization accuracy is better than 1 microsecond, ensuring the accuracy of subsequent traveling wave ranging calculations based on time differences. The device's software system includes a signal quality assessment program, a contribution calculation program, and a weighted fusion processing program. This program adjusts the fusion weights of the impedance and traveling wave positioning results based on the signal-to-noise ratio of the current signal environment, enabling intelligent fault location.

[0038] Further, step S100: synchronously collecting the power frequency electrical quantity data and the high frequency traveling wave data of the monitoring points on both sides of the fault line through the integrated control device.

[0039] Specifically, the power frequency electrical quantity data and the high frequency traveling wave data of the monitoring points on both sides of the fault line are synchronously collected through the integrated control device, including:

[0040] Performing high-precision clock synchronization and channel state self-checking;

[0041] Collecting the power frequency electrical quantity data and the high frequency traveling wave data in parallel;

[0042] Monitoring the energy change of the high frequency traveling wave data;

[0043] When the energy change of the high frequency traveling wave data exceeds the energy fluctuation reference determined according to the historical statistical characteristics of the high frequency traveling wave data, adding an association identifier to the collected power frequency electrical quantity data corresponding to the time of the energy change of the high frequency traveling wave data;

[0044] The association identifier is used to indicate that the power frequency electrical quantity data with the association identifier is preferentially used for calculation of the first fault distance estimation value.

[0045] In the embodiment, the integrated control device is the core equipment for data collection, and first performs high-precision clock synchronization and channel state self-checking. The high-precision clock synchronization is a key link for ensuring the consistency of the time reference of the data of the monitoring points on both sides, and a GPS or Beidou satellite time system is used as a unified time reference, and the clock synchronization precision is within 1 microsecond. For example, the integrated control device has a high-precision crystal oscillator as a local clock source, periodically calibrates the local clock through satellite time signals, and sends clock synchronization instructions to the monitoring points on both sides to ensure that the data collection time of each monitoring point strictly corresponds.

[0046] In an alternative embodiment, the high-precision clock synchronization can be realized by using the IEEE 1588 Precision Time Protocol (PTP). The PTP protocol uses a master-slave clock architecture, calculates the network transmission delay by exchanging timestamps in the network, and thus achieves sub-microsecond clock synchronization precision. For example, as a master clock source, synchronization messages are sent to the slave clock devices of each monitoring point, and the slave clock devices adjust the local clock according to the received timestamp information, and finally realize the clock unification of the entire monitoring network.

[0047] In another optional embodiment, high-precision clock synchronization can be achieved using the Network Time Protocol (NTP) combined with a local high-precision clock source. Specifically, this involves connecting to a standard time server via the internet or a dedicated network to obtain standard time information. This is then combined with the high-stability frequency reference provided by a local rubidium or cesium atomic clock to achieve long-term clock synchronization stability. For example, if the network connection is interrupted, the local high-precision clock source can maintain clock accuracy for a longer period of time, ensuring continuous operation of the system.

[0048] Channel status self-tests include hardware integrity checks and signal path connectivity verification for data acquisition channels. For example, standard test signals are regularly input into each acquisition channel to test the linearity, sampling accuracy, and frequency response characteristics of the analog-to-digital converters, ensuring that the acquisition channel's technical specifications meet the requirements for collecting power-frequency electrical quantities and high-frequency traveling wave data.

[0049] In an optional embodiment, channel status self-tests can be performed using a built-in signal generator. This built-in standard signal generator can generate test signals of known amplitude, frequency, and phase, and periodically switches to each acquisition channel for calibration testing. For example, by comparing the input standard signal with the acquisition results, the gain error, offset error, and nonlinearity of each channel are calculated to generate channel characteristic correction parameters.

[0050] In another optional embodiment, channel status self-testing can be performed using digital signal injection. By injecting known digital test sequences into the digital signal processing chain, the correctness of the entire signal processing chain can be verified. For example, specific test patterns can be superimposed on the digital signal after analog-to-digital conversion. By testing whether the output terminal can correctly recognize these test patterns, the correctness of the digital signal processing algorithm and the integrity of the data transmission can be verified.

[0051] During the data acquisition phase, power-frequency electrical quantity data and high-frequency traveling wave data are collected in parallel. Power-frequency electrical quantity data primarily includes the amplitude and phase information of three-phase voltage and three-phase current, with a sampling frequency of 2.56 kHz. For example, the collected power-frequency data is preprocessed with an anti-aliasing filter to remove high-frequency interference components, and then digitized using a 16-bit or higher-precision analog-to-digital converter.

[0052] In an optional embodiment, the collection of power-frequency electrical quantity data can be achieved using synchronized phasor measurement units (PMUs). Based on GPS clock synchronization, PMU technology can accurately measure the phasors of voltage and current in power systems, providing amplitude and phase angle information. For example, using a fundamental frequency of 50 Hz or 60 Hz as a reference, the PMU calculates synchronized phasors through digital filtering and discrete Fourier transform, achieving measurement accuracy of 0.1% amplitude error and 0.01 degree phase angle error.

[0053] In another alternative embodiment, power-frequency electrical quantity data can be collected using traditional voltage and current transformers in conjunction with a high-precision sampling device. Specifically, the voltage and current transformers proportionally convert the high-voltage and high-current signals on the primary side into standard secondary-side signals, which are then sampled by a high-precision data acquisition card. For example, using a 0.2-level precision transformer in conjunction with a 24-bit high-precision ADC (analog-to-digital converter) can meet the electrical quantity measurement accuracy requirements for fault location.

[0054] The acquisition frequency of high-frequency traveling wave data is set to 1MHz or higher to capture the traveling wave signal generated by the fault and its propagation process in the line. The energy changes of high-frequency traveling wave data are continuously monitored, and the energy calculation adopts the root mean square calculation method of the sliding window, that is:

[0055]

[0056] Among them, E w is the energy value in the window, N is the number of sampling points in the window, x i is the amplitude of the i-th sampling point.

[0057] In an optional embodiment, the energy change monitoring of high-frequency traveling wave data can adopt an energy operator detection method. The energy operator can simultaneously reflect the amplitude and frequency changes of the signal and has good detection performance for sudden change signals. For example, the Teager-Kaiser energy operator is used, and its calculation expression is:

[0058] Ψ[x(n)]=x 2 (n)-x(n+1)·x(n-1);

[0059] Among them, Ψ[x(n)] is the output of the energy operator, and x(n), x(n+1), and x(n-1) are the signal values ​​of the current and previous and next sampling points, respectively.

[0060] In another optional embodiment, energy change monitoring can be performed using a wavelet transform. Wavelet transform is used to perform multi-scale decomposition on high-frequency traveling wave data, and energy changes are calculated at different frequency scales. For example, a Daubechies wavelet or Morlet wavelet is used to perform a continuous wavelet transform on the signal, and the energy of the wavelet coefficients at each scale is calculated. When the energy at a certain scale exceeds the corresponding threshold, a fault is determined to have occurred.

[0061] The determination of energy fluctuation benchmark is based on the historical statistical characteristics of high-frequency traveling wave data, using T threshold =μ noise +k·σ noise The calculation method is, where T thresholdis the energy fluctuation benchmark, μ noise is the mean value of background noise energy, σ noise is the standard deviation of the background noise energy, and k is an adjustable multiplication factor with a typical value of 3 to 5.

[0062] When the energy change in the monitored high-frequency traveling wave data exceeds a preset energy fluctuation threshold, a correlation flag is added to the power-frequency electrical quantity data collected at that moment. The correlation flag contains information such as the timestamp, energy change amplitude, and confidence level, indicating that the power-frequency electrical quantity data with the correlation flag should be used preferentially for calculating the first fault distance estimate.

[0063] In an optional embodiment, the association identifier can be implemented using database tagging. A flag field is added to the power frequency data record, including information such as a fault flag, a timestamp, and the energy change magnitude. For example, when a sudden energy change is detected, the power frequency data within a certain time window before and after the corresponding moment is marked as "fault-related data," and the specific timestamp and energy change value are recorded.

[0064] In another optional embodiment, association identification can be implemented using a file index. A separate index file is generated for each fault event, recording information such as the fault occurrence time, the location of the associated data file, and data quality assessment. Exemplarily, the index file uses XML or JSON format and contains fields such as the fault ID, occurrence time, data file path, and signal quality score, facilitating subsequent data retrieval and processing algorithm invocation.

[0065] The detailed data collection process described above ensures high-quality, synchronized acquisition of both power-frequency electrical data and high-frequency traveling wave data, providing a reliable foundation for subsequent fault distance calculations. Furthermore, intelligent energy change monitoring and associated identification mechanisms automatically identify and tag key data related to fault events, improving the relevance and efficiency of subsequent analysis and processing.

[0066] Furthermore, step S200: a first fault distance estimation value is calculated based on the power frequency electrical quantity data, and an initial traveling wave head is identified based on the high frequency traveling wave data to calculate a second fault distance estimation value.

[0067] Specifically, obtaining a first fault distance estimation value by calculation based on power frequency electrical quantity data includes:

[0068] Prioritize the selection of power frequency electrical quantity data with associated identification;

[0069] Construct the fault circuit voltage balance equation based on the preferred power frequency electrical quantity data;

[0070] An iterative numerical analysis method is used to solve the fault circuit voltage balance equation to obtain the first fault distance estimate.

[0071] Identifying an initial traveling wave head based on high-frequency traveling wave data to calculate a second fault distance estimation value includes:

[0072] Apply wavelet transform to perform multi-scale decomposition on high-frequency traveling wave data;

[0073] Extract the modulus maximum point on the high-frequency component after multi-scale decomposition;

[0074] According to the time series characteristics of the modulus maximum point, the arrival time of the initial traveling wave head is determined;

[0075] The second fault distance estimate is calculated based on the arrival time of the initial traveling wave head and combined with the principle of double-terminal traveling wave ranging.

[0076] In this embodiment, the process of calculating the first fault distance estimate based on the power frequency electrical quantity data first requires prioritizing the power frequency electrical quantity data with an associated identifier. The data segments to which the associated identifier is added in step S100 are filtered out from the collected power frequency electrical quantity data. These data segments correspond to the moments when the high-frequency traveling wave energy changes significantly, and are therefore more likely to reflect the electrical state at the time of the fault. For example, when a single-phase grounding fault occurs on a 110kV transmission line, the phase A voltage of the line is 63.5kV before the fault occurs, and the phase A voltage drops to 15.2kV after the fault, while the zero-sequence current increases from 0 to 120A. At this time, the timestamp recorded by the associated identifier is the moment of fault occurrence, the energy change amplitude is 8.5 times the background noise level, and the confidence level is 0.95. The power frequency data in the 10ms time window before and after this time period are preferentially selected for analysis.

[0077] In an optional embodiment, preferentially selecting power frequency electrical quantity data with an associated identifier can be achieved through time window matching. Based on the timestamp information in the associated identifier, power frequency data within a certain time range before and after the fault occurs is extracted. For example, when the associated identifier timestamp is detected as 15:32:45.123, power frequency data within a time window of 20ms before and after this time (i.e., 15:32:45.103 to 15:32:45.143) is extracted. This window includes a complete power frequency cycle before the fault and the transient process after the fault, ensuring data integrity and analysis accuracy.

[0078] In another optional embodiment, priority selection can be performed using data quality assessment. Signal-to-noise ratio analysis and waveform integrity checks are performed on each data segment with an associated identifier, and the data segment with the best signal quality is selected as input for fault location analysis. For example, for a 220kV line fault, when there are three associated identifier data segments, their total harmonic distortion (THD) is calculated to be 2.3%, 4.1%, and 1.8%, respectively, and their signal amplitude stability deviations are calculated to be 0.5%, 1.2%, and 0.3%. The third data segment with the lowest THD and the most stable amplitude is selected for subsequent analysis.

[0079] Based on the prioritized power-frequency electrical quantity data, a fault loop voltage balance equation is constructed. This fault loop voltage balance equation is based on Kirchhoff's voltage law and considers the relationship between the voltage drop from both ends of the line to the fault point and the voltage at the fault point. The fault loop includes the line segments from both ends of the fault line to the fault point, as well as the transition impedance at the fault point. For example, for a BC phase-to-phase short circuit fault on a 220kV double-circuit line, with the fault point x kilometers away from side A, the constructed fault loop voltage balance equation is:

[0080] U AM -U BM =(I AM -I BM )×(R1+jX1)×x+I f ×R f ;

[0081] Among them, U AM 、U BM is the B-phase voltage and C-phase voltage at the M terminal on the A side; I AM , I BM are the corresponding B-phase current and C-phase current; R1 is the positive-sequence resistance per unit length of the line (subscript 1 indicates the positive-sequence component); X1 is the positive-sequence reactance per unit length of the line; j is the imaginary unit; I f is the fault current (subscript f indicates fault), R f is the transition resistance, and x is the fault distance to be determined.

[0082] In an optional embodiment, the fault circuit voltage balance equation can be constructed using a distributed parameter model. Considering the distributed capacitance and distributed inductance characteristics of the transmission line, a hyperbolic function is used to represent the transmission characteristics of the line. For example, the voltage transmission relationship from the transmitting end to the receiving end of the line is:

[0083] U r =U s cosh(γl)-I s Z c sinh(γl);

[0084] Among them, Ur is the receiving end voltage (subscript r indicates receiving); U s is the voltage at the sending end (subscript s indicates sending); cosh(γl) indicates the hyperbolic cosine function; sinh(γl) indicates the hyperbolic sine function; I s is the current at the sending end; γ is the propagation constant; l is the line length; Z c is the characteristic impedance (the subscript c indicates characteristic).

[0085] Based on this voltage transmission relationship, the fault circuit voltage balance equation is expressed as the balance relationship of the voltages on both sides of the fault point:

[0086] U s1 cosh(γx)-I s1 Z c sinh(γx)=U s2 cosh(γ(Lx))-I s2 Z c sinh(γ(Lx))+I f R f ;

[0087] Among them, U s1 is the voltage on side A (subscript s1 indicates the sending end on side 1); I s1 is the current on side A; U s2 is the voltage on the B side (subscript s2 indicates the second side sending end); I s2 is the current on side B; L is the total length of the line; (Lx) represents the distance from the fault point to side B; I f is the fault current; R f is the transition resistor.

[0088] In another optional embodiment, a lumped parameter model can be used to simplify the construction of the fault loop equation. This lumped representation of the line's resistance, inductance, and capacitance is suitable for fault location on short and medium-distance lines. For example, for a 35kV distribution line, using a lumped parameter π-type equivalent circuit model, the fault loop voltage balance equation is simplified to:

[0089] U A -U B =I A ×(R×x+jX×x)+I f ×R f ;

[0090] Among them, U A 、U B is the voltage at both ends of the line, i.e., terminal A and terminal B; I Ais the current at terminal A; j is the imaginary unit, x is the fault distance; R and X are the resistance and reactance per unit length of the line. When the total length of the line is 20 km, the unit resistance is 1.2 Ω / km, and the unit reactance is 1.8 Ω / km, the fault distance can be obtained by solving this equation.

[0091] Solving the fault circuit voltage balance equation using an iterative numerical analysis method is a key step in obtaining the first fault distance estimate. Because the fault circuit equations are typically nonlinear, a numerical iterative method is required. The fault distance is set as the unknown variable, and the residual error is minimized through iterative calculations.

[0092] In an optional embodiment, iterative numerical analysis can be implemented using the Newton-Raphson method. This method constructs a linearized system of equations by calculating the first-order derivative of the objective function, gradually approaching the true solution. For example, for a single-phase grounding fault on a 35kV distribution line, the initial fault distance estimate is set to 12.5km from the line midpoint. Substituting this into the fault loop equation yields a residual of 2.3kV in the voltage balance equation. The Jacobian matrix elements are calculated by numerical differentiation:

[0093]

[0094] in, represents the partial derivative of the function F with respect to the variable x, where F is the residual function of the fault circuit voltage balance equation and x is the fault distance variable.

[0095] Get the distance correction:

[0096]

[0097] Where Δx is the distance correction (Δ represents the increment), F(x) represents the residual function value at the current fault distance x, and F′(x) represents the first-order derivative of the residual function F at x. The updated fault distance is:

[0098] x new =12.5-3.2=9.3km;

[0099] Among them, x new is the updated fault distance estimate.

[0100] After 5 iterations, the residual converged to below 0.1 kV, and the fault distance was determined to be 8.7 km.

[0101] In another optional embodiment, a genetic algorithm can be used for global optimization. This type of intelligent optimization algorithm can avoid falling into local optimal solutions. For example, the fault distance search range is set to 0 to 50 km of line length. A population of 20 individuals is initialized, each representing a candidate fault distance. The equation residuals corresponding to each individual are evaluated using a fitness function. After 100 generations of evolution, the individual with the best fitness corresponds to a fault distance of 23.6 km, and the minimum residual of the fault loop equation is 0.08 kV.

[0102] The process of identifying the initial traveling wave head based on high-frequency traveling wave data first applies wavelet transform to perform multi-scale decomposition on the high-frequency traveling wave data. Wavelet transform can provide both time and frequency information of the signal and is particularly suitable for analyzing the transient characteristics of traveling wave signals. A suitable mother wavelet function is selected to perform continuous wavelet transform or discrete wavelet transform on the collected high-frequency traveling wave data. For example, for the traveling wave signal generated by a 500kV transmission line fault, the sampling frequency is 1MHz, and the db4 wavelet is selected for 6-layer decomposition. The first layer of decomposition corresponds to the 250-500kHz frequency band, the second layer corresponds to the 125-250kHz frequency band, the third layer corresponds to the 62.5-125kHz frequency band, and so on. When a lightning fault occurs on the line, the initial traveling wave is mainly concentrated in the 10-100kHz frequency band, corresponding to the wavelet decomposition results of the 4th to 6th layers. The traveling wave head characteristics can be clearly identified at these levels.

[0103] In an optional embodiment, the wavelet transform can use the Daubechies wavelet family to achieve multi-scale decomposition. The Daubechies wavelet has good time-frequency localization and orthogonality, and can effectively separate traveling wave signals of different frequency components. For example, the db8 wavelet is used to perform a 7-layer decomposition of the high-frequency traveling wave data of the 110kV line. The 5th layer decomposition result corresponds to the 15.6-31.25kHz frequency band. The amplitude of the wavelet coefficient in this frequency band shows a significant peak at 0.8ms after the fault occurs. The amplitude increases from the background level of 0.02 to 0.45, indicating the arrival of the traveling wave head.

[0104] In another optional embodiment, a Morlet wavelet can be used for continuous wavelet transform. Morlet wavelets have good resolution balance in time-frequency analysis and are particularly suitable for detecting transient characteristics of traveling wave signals. For example, a Morlet wavelet with a center frequency of 50 kHz and a bandwidth parameter of 6 was used to analyze 220 kV line traveling wave data. A significant peak in the wavelet transform modulus was detected 1.2 ms after the fault. The peak value was 0.67, and the corresponding instantaneous frequency was 48 kHz, which is consistent with the typical frequency characteristics of fault traveling waves.

[0105] Extracting the modulus maximum point on the high-frequency component after multi-scale decomposition is the core step in identifying the traveling wave head. The modulus maximum point corresponds to the mutation position of the signal and is an important sign of the arrival of the traveling wave head. Search for local modulus maximum points in the wavelet coefficients of each scale, and the time position of these points corresponds to the possible arrival time of the traveling wave. For example, in the 4th layer wavelet decomposition result, the background noise level is about 0.02, and the modulus maximum detection threshold is set to 0.1 (5 times the noise level). When a modulus maximum point with an amplitude of 0.35 is detected, the corresponding time is 1.2ms after the fault. This point also has corresponding maxima in the 3rd, 4th, and 5th layer decompositions. The time deviation is less than 1 sampling period (1μs), which is confirmed as a valid arrival time of the traveling wave head.

[0106] In an optional embodiment, the extraction of modulus maximum points can be achieved by adaptive threshold comparison. The detection threshold is dynamically adjusted according to the signal characteristics of each decomposition level to improve the accuracy of detection. For example, for the decomposition result of the third level, the background noise standard deviation is 0.025, and the threshold T3 is set as:

[0107] T3=μ3+4σ3=0.01+4×0.025=0.11;

[0108] For the 5th layer decomposition result, the background noise standard deviation is 0.018, and the threshold T5 is set as:

[0109] T5=μ5+4σ5=0.008+4×0.018=0.08;

[0110] Among them, μ3 and μ5 are the noise means of the corresponding levels, and σ3 and σ5 are the noise standard deviations.

[0111] In another optional embodiment, a multi-scale modulus maximum point correlation method can be used to improve detection reliability. A true traveling wave head will produce modulus maximum points at multiple decomposition scales, and these points have a certain correspondence in time. For example, when the modulus maximum point of the fourth layer is detected at time 1.25ms, corresponding maximum points are also detected at 1.24ms of the third layer and 1.26ms of the fifth layer, with time deviations of less than 20μs. This multi-scale correlation verification confirms that the corresponding moment corresponds to the arrival of a true traveling wave head.

[0112] Determining the arrival time of the initial traveling wave head based on the time series characteristics of the modulus maximum point requires comprehensive consideration of the characteristics of multiple candidate peaks. Analyze the amplitude, duration, and frequency characteristics of each modulus maximum point, and combine the physical laws of traveling wave propagation to determine which peak corresponds to the initial fault traveling wave. For example, in a 35kV line fault, three main modulus maximum points were detected: the first one appeared at 0.8ms with an amplitude of 0.23; the second one appeared at 1.4ms with an amplitude of 0.41; and the third one appeared at 2.1ms with an amplitude of 0.18. Based on the line length of 15km and the traveling wave propagation speed of 2.9×10 8 m / s. Theoretically, the one-way propagation time of the traveling wave is about 52μs. The first peak is closest to the fault occurrence moment and has the steepest rising edge, which is determined to be the initial traveling wave head.

[0113] In an optional embodiment, the identification of the initial traveling wave head can be performed based on the energy criterion. The initial traveling wave usually has the largest instantaneous energy and the steepest rising edge. By comparing the energy size and change rate of each candidate peak, the peak that best meets the characteristics of the initial traveling wave is selected as the arrival time of the wave head. For example, the instantaneous energy corresponding to each peak is calculated: the energy of the first peak is 0.053, and the energy change rate is 0.29 / μs; the energy of the second peak is 0.168, and the energy change rate is 0.15 / μs; the energy of the third peak is 0.032, and the energy change rate is 0.08 / μs. Although the second peak has the largest energy, the energy change rate of the first peak is the highest, which is more consistent with the characteristics of the initial traveling wave.

[0114] In another optional embodiment, a pattern recognition method can be used to identify the traveling wave head. By establishing a standard traveling wave head template, the similarity between each candidate peak and the standard template is calculated, and the peak with the highest similarity is selected as the initial traveling wave head. For example, a standard traveling wave head template based on exponentially decaying oscillation is established, and the normalized correlation coefficient between each candidate waveform and the template is calculated. The correlation coefficient of the first peak is 0.87, the second is 0.62, and the third is 0.45. The time corresponding to the first peak with the largest correlation coefficient is selected as the wave head arrival time.

[0115] The second fault distance estimate is calculated based on the arrival time of the initial traveling wave head and the principle of two-terminal traveling wave ranging. Two-terminal traveling wave ranging locates the fault point based on the propagation time difference between the traveling wave and the two ends of the line. The fault distance is calculated using the arrival time of the traveling wave detected by the monitoring points on both sides and the traveling wave propagation speed. The basic calculation formula for two-terminal traveling wave ranging is:

[0116]

[0117] Among them, L fault is the fault distance (from side A), L totalis the total length of the line, v is the speed of traveling wave, t A and t B are the arrival times of the traveling waves detected by the monitoring points on side A and side B, respectively. For example, for a 110 kV line with a length of 80 km, the traveling wave propagation speed is 2.92×10 8 m / s, the monitoring point on side A detects the arrival time of the traveling wave as t A =1.25ms, the time detected by side B is t B =1.42ms, the time difference is 0.17ms. Calculated according to the formula:

[0118] Fault distance = (80 + 2.92 × 10 8 ×0.17×10 -3 ) / 2=(80+49.64) / 2=64.82km.

[0119] In an optional embodiment, the traveling wave propagation velocity can be calculated using a theoretically calculated value. The traveling wave propagation velocity is calculated based on the physical parameters of the line, such as the conductor type, installation height, and phase spacing. For example, for a 220kV overhead line using LGJ-400 / 35 steel-core aluminum stranded wire, the conductor outer diameter is 26.8mm, the phase spacing is 7.5m, and the installation height is 25m. Its positive sequence wave impedance is approximately 355Ω, and the corresponding traveling wave propagation velocity is approximately 2.86×10 8 m / s, which is approximately 95.3% of the speed of light. It should be noted that in actual projects, it is recommended that the impedance parameters of overhead lines be verified using measured values.

[0120] In another optional embodiment, the traveling wave propagation velocity can be obtained through actual measurement and calibration of line parameters. Using artificial fault tests or test data from line commissioning, the traveling wave propagation velocity is verified and calibrated at fault points of known distance to improve ranging accuracy. For example, artificial grounding tests were conducted at 25km, 50km, and 75km of a 110kV line, and the measured traveling wave propagation times were 85.6μs, 171.2μs, and 256.8μs, respectively. The average propagation velocity is calculated as:

[0121] (25×10 3 / 85.6×10 -6 +50×10 3 / 171.2×10 -6 +75×10 3 / 256.8×10 -6 ) / 3=2.91×10 8 m / s,

[0122] Therefore, the calculated average propagation velocity of 2.91×10 8m / s is used as a calibration value for subsequent fault distance calculations.

[0123] Through the detailed analysis and processing described above, the present invention can calculate two independent fault distance estimates based on power-frequency electrical quantity data and high-frequency traveling wave data, providing reliable input data for subsequent fusion processing. The combined use of these two methods fully leverages their respective technical advantages, providing complementary location information under different fault conditions and signal environments.

[0124] Step S300: defining an evaluation time period for the identified initial traveling wave head in the high-frequency traveling wave data, and calculating a signal-to-noise ratio value within the evaluation time period.

[0125] Specifically, defining the evaluation time period includes defining a noise evaluation window before the arrival of the initial traveling wave head and a signal evaluation window after the arrival of the initial traveling wave head;

[0126] Calculating the signal-to-noise ratio value within the evaluation time period includes calculating the noise level based on the high-frequency traveling wave data within the noise evaluation window, and calculating the signal-plus-noise level based on the high-frequency traveling wave data within the signal evaluation window, and then obtaining the signal-to-noise ratio value through the noise level and the signal-plus-noise level.

[0127] In this embodiment, for the initial traveling wave front identified in step S200, a time period for signal-to-noise ratio evaluation needs to be defined. Defining the evaluation time period is critical to ensuring the accuracy of signal-to-noise ratio calculations. By properly setting the noise evaluation window and signal evaluation window, background noise and useful signal components can be effectively separated. For example, for a 110kV transmission line, if the arrival time of the initial traveling wave front is determined to be 1.25ms after the fault in step S200, this time is used as the demarcation reference, with the noise evaluation window set forward and the signal evaluation window set backward.

[0128] The noise evaluation window defined before the arrival of the initial traveling wave head is used to collect pure background noise data. The starting time of the noise evaluation window should be far away from the arrival time of the traveling wave head to ensure that the window does not contain any fault-related signal components. The noise evaluation window is set in the time period of 100-500 microseconds before the arrival of the initial traveling wave head, and the window length is usually 50-200 microseconds. For example, when the arrival time of the initial traveling wave head is 1.25ms, the noise evaluation window is set to 0.75ms to 1.05ms, and the window length is 300 microseconds. The high-frequency traveling wave data in this time period mainly reflects the background noise characteristics of the system, including measurement equipment noise, environmental electromagnetic interference and other non-fault-related signal fluctuations.

[0129] The signal evaluation window defined after the arrival of the initial traveling wave head is used to collect data containing the fault traveling wave signal. The starting time of the signal evaluation window is close to the arrival time of the initial traveling wave head, and the window length should be sufficient to cover the main fault traveling wave signal components. The signal evaluation window is set within the 50-300 microsecond time period after the arrival of the initial traveling wave head. Exemplarily, when the arrival time of the initial traveling wave head is 1.25ms, the signal evaluation window is set to 1.25ms to 1.45ms, and the window length is 200 microseconds. The high-frequency traveling wave data in this time period includes the traveling wave signal generated by the fault and the background noise, wherein the traveling wave signal usually has the largest amplitude and the richest spectral characteristics within the first 100 microseconds after the arrival of the head.

[0130] After the evaluation period is defined, the signal-to-noise ratio (SNR) calculation for that period begins. This calculation is done in two steps: first, the noise level is calculated based on the high-frequency traveling wave data within the noise evaluation window, and then the signal-plus-noise level is calculated based on the high-frequency traveling wave data within the signal evaluation window.

[0131] The noise level is calculated based on the high-frequency traveling wave data within the noise evaluation window using the root mean square value calculation method. All sampling points within the noise evaluation window are extracted and their root mean square values ​​are calculated as a quantitative indicator of the noise level. For example, the noise level calculation formula is:

[0132]

[0133] Among them, P noise is the noise level; N noise is the total number of sampling points in the noise evaluation window, x noise,i is the amplitude of the i-th sampling point (the subscript noise represents noise, and the subscript i represents the i-th sampling point). For example, within the noise evaluation window of 0.75 ms to 1.05 ms, the sampling frequency is 1 MHz, the total number of sampling points is 300, and the RMS value of the amplitude at each sampling point is calculated to be a noise level of 0.035.

[0134] The signal plus noise level is calculated based on the high-frequency traveling wave data within the signal evaluation window using the same root mean square value calculation method. For example, all sampling points within the signal evaluation window are extracted and their root mean square values ​​are calculated as a quantitative indicator of the signal plus noise level. The calculation formula for the signal plus noise level is:

[0135]

[0136] Among them, P signal+noise is the signal plus noise level; N signal is the total number of sampling points within the signal evaluation window; x signal,jis the amplitude of the jth sampling point (the subscript signal represents the signal, and the subscript j represents the jth sampling point). For example, within the signal evaluation window of 1.25ms to 1.45ms, the total number of sampling points is 200, and the calculated signal plus noise level is 0.287.

[0137] The signal-to-noise ratio (SNR) obtained by combining the noise level and the signal-plus-noise level is a key indicator for evaluating the quality of traveling wave signals. For example, the SNR is calculated as:

[0138]

[0139] Wherein, SNR is the signal-to-noise ratio (abbreviation of Signal-to-Noise Ratio), and the unit is decibel (dB);

[0140] is the common logarithmic function with base 10; P signal+noise is the signal plus noise level, P noise is the noise level.

[0141] Based on the calculation results in the previous example, the signal-to-noise ratio is:

[0142]

[0143] The reliability of the initial traveling wave head identification and the accuracy of the subsequent traveling wave ranging results are evaluated by the size of the signal-to-noise ratio. When the signal-to-noise ratio is high, it indicates that the traveling wave signal has an advantage over noise, the traveling wave head identification is accurate, and the ranging results are reliable. When the signal-to-noise ratio is low, it indicates that the traveling wave signal is easily interfered by noise, which may affect the accuracy of the head identification and the ranging results. For example, when the signal-to-noise ratio is greater than 15dB, the traveling wave signal quality is considered to be good and suitable for accurate fault location analysis; when the signal-to-noise ratio is between 10-15dB, the signal quality is medium and needs to be combined with other information for comprehensive judgment; when the signal-to-noise ratio is lower than 10dB, the signal quality is poor and it may be necessary to adopt other fault location methods or wait for better signal conditions.

[0144] In specific application scenarios, different types of faults and different line conditions will result in different signal-to-noise ratio characteristics. For example, for a metallic short-circuit fault on a 220kV transmission line, the fault current is large, the generated traveling wave signal is strong, and a high signal-to-noise ratio of more than 20dB can usually be obtained. For a high-resistance grounding fault on a 35kV distribution line, the fault current is relatively small, the traveling wave signal is weak, and the signal-to-noise ratio may be only 8-12dB. For lightning faults, due to the instantaneous and high-frequency characteristics of the lightning current, a strong traveling wave signal can usually be generated, and the signal-to-noise ratio can reach 15-25dB. Based on the signal-to-noise ratio characteristics in these different scenarios, important signal quality assessment information is provided for the subsequent fusion positioning algorithm.

[0145] The detailed SNR calculation process described above not only quantitatively evaluates the signal quality of high-frequency traveling wave data but also provides an important basis for weighting the first and second fault distance estimates in subsequent steps. As a key indicator of the reliability of traveling wave ranging, the SNR plays a key role in the subsequent fusion algorithm, ensuring the accuracy and reliability of the final fault location results.

[0146] Step S400: Determine the contribution of the first fault distance estimate and the second fault distance estimate in generating the final fault location distance calculation based on the signal-to-noise ratio, and perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain the final fault location distance.

[0147] Specifically, a first signal-to-noise ratio threshold and a second signal-to-noise ratio threshold are set, and the first signal-to-noise ratio threshold is lower than the second signal-to-noise ratio threshold;

[0148] According to a comparison result of the signal-to-noise ratio value and the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, a first contribution value of the first fault distance estimation value and a second contribution value of the second fault distance estimation value are calculated using a preset mapping rule;

[0149] The preset mapping rules are:

[0150] When the signal-to-noise ratio value is lower than the first signal-to-noise ratio threshold, determining the first contribution value and the second contribution value according to the preset first level configuration rule, so that the first contribution value is greater than the second contribution value;

[0151] It should be noted that the first-level configuration rule is implemented using a linear decreasing algorithm. The specific calculation method is as follows: when the signal-to-noise ratio value is lower than the first signal-to-noise ratio threshold, the first contribution value is linearly distributed within the range of 0.7 to 0.8. The lower the signal-to-noise ratio, the larger the first contribution value, and the second contribution value is correspondingly reduced to ensure that the sum of the two is 1. For example, when the signal-to-noise ratio value is 8 decibels and the first signal-to-noise ratio threshold is 10 decibels, the first contribution value is calculated to be 0.75 and the second contribution value is 0.25. When the signal-to-noise ratio value further decreases to 5 decibels, the first contribution value increases to 0.8 and the second contribution value decreases to 0.2, ensuring that the stability of the power frequency impedance method is more relied upon in the presence of severe noise interference.

[0152] When the signal-to-noise ratio value is higher than the second signal-to-noise ratio threshold, determining the first contribution value and the second contribution value according to the preset second level configuration rule, so that the second contribution value is greater than the first contribution value;

[0153] It should be noted that the second-level configuration rule is implemented using a linearly increasing algorithm. Specifically, when the signal-to-noise ratio (SNR) is higher than the second SNR threshold, the second contribution value is linearly distributed within the range of 0.6 to 0.8. Higher SNRs increase the second contribution value, while the first contribution value decreases accordingly. For example, when the SNR is 22 decibels and the second SNR threshold is 18 decibels, the second contribution value is calculated to be 0.7, while the first contribution value is 0.3. When the SNR further increases to 28 decibels, the second contribution value increases to 0.8, while the first contribution value decreases to 0.2, fully leveraging the accuracy advantage of traveling wave ranging under high SNR conditions.

[0154] When the signal-to-noise ratio value is between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, the first contribution value and the second contribution value are calculated through a preset function mapping relationship according to the relative position of the signal-to-noise ratio value between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold.

[0155] It should be noted that the preset function mapping relationship is implemented using an S-type smooth transition function. By calculating the normalized relative position of the signal-to-noise ratio value between the two thresholds, the relative position is substituted into the S-type function as an input parameter to obtain the second contribution value. The first contribution value is equal to 1 minus the second contribution value. For example, when the first signal-to-noise ratio threshold is 10 decibels, the second signal-to-noise ratio threshold is 18 decibels, and the current signal-to-noise ratio value is 14 decibels, the relative position is calculated as (14-10) / (18-10)=0.5, and the second contribution value is 0.45 and the first contribution value is 0.55 when substituted into the S-type function. When the signal-to-noise ratio value is 12 decibels, the relative position is 0.25, the second contribution value is 0.35, and the first contribution value is 0.65, thereby achieving a smooth and gradual transition of the weights of the two ranging methods.

[0156] The method further includes performing a consistency check on the first fault distance estimate and the second fault distance estimate;

[0157] Comprehensive treatment includes:

[0158] According to the first contribution value and the second contribution value, respectively determining the corresponding first weight coefficient and second weight coefficient through a preset mapping relationship;

[0159] Based on the first weight coefficient and the second weight coefficient, the first fault distance estimation value and the second fault distance estimation value are weightedly fused to obtain a final fault location distance.

[0160] In this embodiment, the SNR threshold for determining the contribution is first set. The setting of the first SNR threshold and the second SNR threshold is based on the statistical analysis of a large number of actual fault cases and the performance evaluation of the ranging method under different SNR conditions.

[0161] For example, the first SNR threshold is set at 10 decibels, and the second SNR threshold is set at 18 decibels. The reason for setting these thresholds is that when the SNR is below 10 decibels, high-frequency traveling wave data is severely affected by noise, significantly reducing the accuracy of traveling wave ranging. At this point, the power frequency impedance method has higher reliability. When the SNR is above 18 decibels, the traveling wave signal quality is good, and traveling wave ranging can provide more accurate positioning results. Between 10 and 18 decibels, the performance of the two methods is comparable, and dynamic weighting needs to be assigned based on the specific SNR value.

[0162] The signal-to-noise ratio calculated in step S300 is compared with a set threshold, and a contribution value is determined using a preset mapping rule. The contribution value reflects the credibility and importance of different ranging methods under the current signal conditions. The first contribution value corresponds to the contribution of the first fault distance estimate, namely the power frequency impedance method. The second contribution value corresponds to the contribution of the second fault distance estimate, namely the traveling wave ranging method.

[0163] When the signal-to-noise ratio (SNR) value is lower than the first SNR threshold, the contribution value is determined according to the preset first-level configuration rule. For example, when the SNR value calculated in step S300 is 8 decibels, which is lower than the first SNR threshold of 10 decibels, the reliability of traveling wave ranging is low because the traveling wave signal is severely interfered with by noise. Therefore, the first contribution value is set to 0.75 and the second contribution value is set to 0.25, making the first contribution value greater than the second contribution value, reflecting the advantages of the power frequency impedance method under low SNR conditions.

[0164] When the signal-to-noise ratio (SNR) value is higher than the second SNR threshold, the contribution value is determined according to the preset second-level configuration rules. For example, when the SNR value is 22 decibels, which is higher than the second SNR threshold of 18 decibels, the traveling wave signal quality is excellent, and traveling wave ranging can provide high-precision positioning results. The first contribution value is set to 0.3, and the second contribution value is set to 0.7. This makes the second contribution value greater than the first contribution value, fully utilizing the accuracy advantage of traveling wave ranging under high SNR conditions.

[0165] When the SNR value is between the first SNR threshold and the second SNR threshold, the contribution value is calculated using a preset function mapping relationship based on the relative position of the SNR value. For example, when the SNR value is 14 dB, its relative position in the range of 10 dB to 18 dB is 50%. Using a linear interpolation function mapping relationship, the first contribution value is calculated as 0.525 and the second contribution value is calculated as 0.475, achieving a smooth transition between the contributions of the two ranging methods. This dynamic adjustment mechanism ensures optimal fusion effect under different signal conditions.

[0166] Before determining the contribution value, a consistency check is performed on the first and second fault distance estimates. This consistency check detects any abnormal deviations between the two estimates and ensures the rationality of the fusion process. For example, for a 110 kV transmission line with a length of 80 km, when the first fault distance estimate is 45.2 km and the second fault distance estimate is 47.8 km, the difference between the two is 2.6 km, and the relative error is approximately 3.25%, which is within a reasonable range. However, when the first fault distance estimate is 25.3 km and the second fault distance estimate is 63.7 km, the difference between the two reaches 38.4 km, and the relative error exceeds 48%. This is marked as an abnormal situation, and the data analysis may need to be repeated or the results of a single method may need to be used.

[0167] During the comprehensive processing, the corresponding weight coefficients are determined based on the determined contribution values ​​through a preset mapping relationship. The weight coefficient is a normalized representation of the contribution value, ensuring that the sum of the two weight coefficients is equal to 1. For example, when the first contribution value is 0.6 and the second contribution value is 0.4, the corresponding first weight coefficient is 0.6 and the second weight coefficient is 0.4. This direct mapping relationship simplifies the calculation process while maintaining the accuracy of the contribution assessment.

[0168] Based on the determined weight coefficients, the first fault distance estimation value and the second fault distance estimation value are weighted and fused. The weighted fusion adopts a linear weighted average manner, and the final fault positioning distance is equal to the first fault distance estimation value multiplied by the first weight coefficient plus the second fault distance estimation value multiplied by the second weight coefficient. For example, when the first fault distance estimation value is 42.5 kilometers, the second fault distance estimation value is 39.8 kilometers, the first weight coefficient is 0.4, and the second weight coefficient is 0.6, the final fault positioning distance is calculated as 42.5*0.4 plus 39.8*0.6 equal to 17.0 plus 23.88 equal to 40.88 kilometers.

[0169] In actual application, different fault scenarios will lead to different fusion results. For single-phase ground fault occurring in urban power distribution network, due to strong environmental electromagnetic interference, the signal-to-noise ratio is usually low, and the result of the power frequency impedance method is more relied on; for phase-to-phase short-circuit fault occurring in high-voltage transmission line far away from the city, the signal environment is relatively clean, the signal-to-noise ratio is high, and the result of the traveling wave distance measurement method is more adopted; for line fault occurring near industrial area, due to the complexity of signal environment, the weight of the two methods is dynamically adjusted according to the real-time signal-to-noise ratio, so as to ensure that the most reliable positioning result is obtained.

[0170] Through the above detailed contribution degree determination and comprehensive processing flow, the application can intelligently fuse the advantages of the power frequency impedance method and the traveling wave distance measurement method according to the actual signal quality condition, overcome the limitations of single method, and improve the accuracy and reliability of fault positioning. This fusion mechanism makes the fault positioning method of the application adapt to various different power system operating environments and fault conditions.

[0171] Embodiment 3, with reference to Figure 3 As an embodiment of the application, the embodiment provides an intelligent fault recording and traveling wave distance measurement integrated control device, which comprises.

[0172] The data acquisition module is configured to synchronously acquire the power frequency electrical quantity data and the high-frequency traveling wave data of the monitoring points on both sides of the fault line through the integrated control device.

[0173] The preliminary positioning module is configured to calculate a first fault distance estimation value based on the power frequency electrical quantity data, and calculate a second fault distance estimation value based on the high-frequency traveling wave data and the identified initial traveling wave front.

[0174] The signal quality evaluation module is configured to define an evaluation time period for the identified initial traveling wave front in the high-frequency traveling wave data, and calculate a signal-to-noise ratio value in the evaluation time period.

[0175] The fusion positioning module is used to determine the contribution of the first fault distance estimate and the second fault distance estimate in generating the final fault location distance calculation based on the signal-to-noise ratio value, and to perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain the final fault location distance.

[0176] Example 4, with reference to Figure 4 , which is an embodiment of the present invention.

[0177] This embodiment also provides an electronic device, which is suitable for a method for integrating control of intelligent fault recording and traveling wave ranging, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for integrating control of intelligent fault recording and traveling wave ranging as proposed in the above embodiment.

[0178] This embodiment further provides a storage medium storing a computer program, which, when executed by a processor, implements an integrated control method for intelligent fault recording and traveling wave ranging as proposed in the above embodiment.

[0179] The storage medium proposed in this embodiment and the method for realizing an integrated control method of intelligent fault recording and traveling wave ranging proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0180] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0181] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An integrated control method for intelligent fault recording and traveling wave ranging, characterized by: include, The integrated control device synchronously collects power frequency electrical quantity data and high frequency traveling wave data from monitoring points on both sides of the fault line; Calculating a first fault distance estimate based on the power frequency electrical quantity data, and identifying an initial traveling wave head based on the high frequency traveling wave data to calculate a second fault distance estimate; For the initial traveling wave head identified in the high-frequency traveling wave data, defining an evaluation time period, and calculating a signal-to-noise ratio value within the evaluation time period; Determine, based on the signal-to-noise ratio, the contribution of the first fault distance estimate and the second fault distance estimate in generating a final fault location distance calculation, and perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain a final fault location distance.

2. The intelligent fault recording and traveling wave ranging integrated control method according to claim 1, characterized in that: The method of synchronously collecting the power frequency electrical quantity data and high frequency traveling wave data of the monitoring points on both sides of the fault line through the integrated control device includes: Perform high-precision clock synchronization and channel status self-test; Parallel collection of the power frequency electrical quantity data and the high frequency traveling wave data; monitoring energy changes of the high-frequency traveling wave data; and, when the energy change of the high-frequency traveling wave data exceeds an energy fluctuation benchmark determined based on historical statistical characteristics of the high-frequency traveling wave data, adding an association identifier to the collected power-frequency electrical quantity data corresponding to the energy change moment of the high-frequency traveling wave data; The association identifier is used to indicate that the power frequency electrical quantity data with the association identifier is preferentially used to calculate the first fault distance estimation value.

3. The intelligent fault recording and traveling wave ranging integrated control method according to claim 2, characterized in that: The calculating and obtaining a first fault distance estimation value based on the power frequency electrical quantity data includes: Prioritizing the selection of the power frequency electrical quantity data with the associated identifier; Constructing a fault circuit voltage balance equation based on the preferentially selected power frequency electrical quantity data; An iterative numerical analysis method is used to solve the fault circuit voltage balance equation to obtain the first fault distance estimation value.

4. The integrated control method for intelligent fault recording and traveling wave ranging according to claim 3, characterized in that: The identifying an initial traveling wave head based on the high-frequency traveling wave data to calculate and obtain a second fault distance estimation value includes: Applying wavelet transform to perform multi-scale decomposition on the high-frequency traveling wave data; Extracting modulus maximum points on the high-frequency components after the multi-scale decomposition; Determining the arrival time of the initial traveling wave head according to the time series characteristics of the modulus maximum point; The second fault distance estimation value is obtained by calculation according to the arrival time of the initial traveling wave head and in combination with the double-terminal traveling wave ranging principle.

5. The intelligent fault recording and traveling wave ranging integrated control method according to claim 4, characterized in that: Defining the evaluation time period includes defining a noise evaluation window before the arrival of the initial traveling wave head and a signal evaluation window after the arrival of the initial traveling wave head; Calculating the signal-to-noise ratio value within the evaluation time period includes calculating the noise level based on the high-frequency traveling wave data within the noise evaluation window, and calculating the signal-plus-noise level based on the high-frequency traveling wave data within the signal evaluation window, and then obtaining the signal-to-noise ratio value through the noise level and the signal-plus-noise level.

6. The intelligent fault recording and traveling wave ranging integrated control method according to claim 5, characterized in that: Setting a first signal-to-noise ratio threshold and a second signal-to-noise ratio threshold, wherein the first signal-to-noise ratio threshold is lower than the second signal-to-noise ratio threshold; According to a comparison result of the signal-to-noise ratio value with the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, a first contribution value of the first fault distance estimation value and a second contribution value of the second fault distance estimation value are calculated using a preset mapping rule; The preset mapping rule is: When the signal-to-noise ratio value is lower than the first signal-to-noise ratio threshold, determining the first contribution value and the second contribution value according to a preset first level configuration rule, so that the first contribution value is greater than the second contribution value; When the signal-to-noise ratio value is higher than the second signal-to-noise ratio threshold, determining the first contribution value and the second contribution value according to a preset second level configuration rule, so that the second contribution value is greater than the first contribution value; When the signal-to-noise ratio value is between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold, the first contribution value and the second contribution value are calculated using a preset function mapping relationship according to a relative position of the signal-to-noise ratio value between the first signal-to-noise ratio threshold and the second signal-to-noise ratio threshold.

7. The integrated control method for intelligent fault recording and traveling wave ranging according to claim 6, characterized in that: The method further includes performing a consistency check on the first fault distance estimate and the second fault distance estimate; The comprehensive treatment includes: According to the first contribution value and the second contribution value, respectively determine the corresponding first weight coefficient and second weight coefficient through a preset mapping relationship; Based on the first weight coefficient and the second weight coefficient, the first fault distance estimation value and the second fault distance estimation value are weightedly fused to obtain the final fault location distance.

8. An intelligent fault recording and traveling wave ranging integrated control device, using the intelligent fault recording and traveling wave ranging integrated control method according to any one of claims 1 to 7, characterized in that: include: The data acquisition module is used to synchronously collect the power frequency electrical quantity data and high frequency traveling wave data of the monitoring points on both sides of the fault line through the integrated control device; a preliminary positioning module, configured to calculate a first fault distance estimate based on the power frequency electrical quantity data, and identify an initial traveling wave head based on the high frequency traveling wave data to calculate a second fault distance estimate; a signal quality evaluation module, configured to define an evaluation time period for the identified initial traveling wave head in the high-frequency traveling wave data, and calculate a signal-to-noise ratio value within the evaluation time period; a fusion positioning module, configured to determine, based on the signal-to-noise ratio, the contribution of the first fault distance estimate and the second fault distance estimate in generating a final fault location distance calculation, and perform comprehensive processing on the first fault distance estimate and the second fault distance estimate based on the contribution to obtain a final fault location distance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of an intelligent fault recording and traveling wave ranging integrated control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent fault recording and traveling wave ranging integrated control method according to any one of claims 1 to 7 are implemented.

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