Current collection line operation analysis method based on transient traveling wave signal analysis technology

Abstract

The invention relates to the technical field of station operation management, and discloses a transient traveling wave signal analysis technology-based current collection line operation analysis method, which comprises a signal acquisition step, a signal processing step, a time synchronization step, a fault determination step and a decision output step. According to the invention, transient traveling wave signals are collected in real time through the traveling wave sensing module, online acquisition of fault signals is realized, then, multi-scale decomposition and denoising, fault feature extraction and noise suppression are carried out on the signals by adopting a wavelet transform technology to avoid misjudgment, and meanwhile, nanosecond timestamp alignment of arrival time of the transient traveling wave signals is realized based on a high-precision synchronous clock. According to the method, time reference consistency is ensured, multi-dimensional resolving is carried out through transmission equation parameter estimation and a line topology constraint model by taking an adjacent physical interval unit as a minimum resolving unit so as to accurately determine an actual fault interval, in addition, decision-making information is generated and pushed in real time, rapid first-aid repair is assisted, shutdown loss is reduced, and the operation reliability of a current collection line is improved.

Description

Technical Field

[0001] This application relates to the field of station operation and management technology, specifically a method for analyzing the operation of power collection lines based on transient traveling wave signal analysis technology. Background Technology

[0002] As a critical link in power transmission, the power collection lines of new energy power plants operate in a complex environment and are susceptible to short-circuit faults caused by factors such as lightning and partial discharge. Among these faults, single-phase grounding faults account for as much as 80%. If such faults are not handled in a timely manner, wind farms that are not effectively grounded may continue to operate with faults, causing more wind turbines to disconnect from the grid, resulting in significant economic losses and social impact.

[0003] In existing fault location technologies for power collection lines in new energy power plants, noise is easily introduced during fault signal acquisition due to the high acquisition frequency. Useful signals may be submerged by noise, leading to errors in fault feature extraction or even misjudgment. Furthermore, the time synchronization accuracy of existing location technologies is insufficient, making it difficult to meet the requirement of accurate comparison of the arrival time of fault traveling waves. Secondly, when the fault location is determined, it is impossible to accurately locate specific line segments with small distances (such as between two specific nodes), resulting in low efficiency in fault finding and repair, and making it difficult to meet the industry's high requirements for power generation reliability.

[0004] Therefore, there is an urgent need for a method for analyzing the operation of power collection lines that can quickly and accurately locate fault sections. Summary of the Invention

[0005] The purpose of this application is to provide a method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology, so as to solve the technical problems mentioned in the background art.

[0006] To achieve the above objectives, this application discloses the following technical solution: a method for analyzing the operation of a power collection line based on transient traveling wave signal analysis technology, the method comprising:

[0007] The signal acquisition steps include: deploying traveling wave sensing modules at the beginning and end of the main line of the collector line to acquire transient traveling wave signals generated by the fault in real time, wherein the transient traveling wave signals include transient traveling wave voltage and transient traveling wave current signals;

[0008] The signal processing steps include: performing multi-scale decomposition and denoising on the acquired transient traveling wave signal using wavelet transform technology, and extracting fault features, including wavefront arrival time, amplitude, and spectral characteristics.

[0009] The time synchronization steps include: aligning the arrival time of the transient traveling wave signal with a timestamp using a high-precision synchronous clock based on the BeiDou system;

[0010] The fault determination steps include: obtaining the preliminary fault location through transmission equation parameter estimation, and, based on the preliminary fault location, using adjacent physical interval units as the smallest solution unit, performing multi-dimensional calculations on the preliminary fault location based on a pre-built line topology constraint model to determine the actual fault range. The adjacent physical interval units are the interval areas between adjacent landmark facilities or landmark nodes with clear physical boundaries in the collector line. The line topology constraint model is set with the physical coordinates, lengths, node relationships, and fault feature database of the main line and branch lines of the collector line.

[0011] The decision output steps include: generating decision information based on the determined actual fault range, and pushing the decision information to the background in real time, wherein the decision information includes fault characteristics, location details and operation and maintenance strategies.

[0012] Preferably, in the signal acquisition step, the distributed traveling wave sensing module includes a traveling wave electromagnetic sensor and a traveling wave photoelectric sensor, and the distributed traveling wave sensing module acquires transient signals across the entire frequency band in the following manner:

[0013] In the low-frequency range of 1kHz–1MHz, the low-frequency transient characteristics of weak faults are captured by traveling wave electromagnetic sensors.

[0014] In the high-frequency band of 1MHz–100MHz, the system switches to a traveling wave photoelectric sensor and captures high-frequency transient characteristics through optical fiber transmission to overcome interference.

[0015] Preferably, in the fault determination step, the multi-dimensional calculation of the preliminary fault location based on the pre-built line topology constraint model to determine the actual fault range includes the following steps:

[0016] Based on the pre-built line topology constraint model, the preliminary fault location obtained by estimating the transmission equation parameters is mapped to a specific line segment, and the actual fault interval is limited to the range between adjacent physical interval units within the line segment.

[0017] If the spatial coordinates of the initial fault location match the topology range of the branch line, check whether there is at least one T-junction between the branch line and the main line. If there is no T-junction, define the initial fault location as a pseudo-interval without electrical connection and remove the initial fault location. Otherwise, match the fault features extracted in the signal processing step with the fault feature library. When the fault feature matching degree exceeds the preset matching degree threshold, lock the adjacent physical interval unit corresponding to the fault feature as the actual fault interval.

[0018] As a preferred embodiment, when there is a T-junction between the branch line and the main line, when the fault feature matching degree is lower than the preset matching degree threshold, the historical fault records of the specific line segment within the past N time periods are called, and the adjacent physical interval unit where the initial location of the fault is located is defined as the candidate fault interval.

[0019] If the historical failure frequency of a candidate fault interval exceeds a preset frequency threshold, the failure probability weight of the candidate fault interval is increased and it is designated as a high-priority candidate fault interval; otherwise, the failure probability weight of the candidate fault interval is decreased and the candidate fault interval with the highest fault feature matching degree is retained as a low-priority candidate fault interval.

[0020] The high-priority candidate fault interval and the low-priority candidate fault interval are both recorded in the historical fault record after the current fault is determined, and serve as the data source for calling the historical fault record when the next fault is determined. If a candidate fault interval is confirmed as an actual fault interval in any fault determination, its historical fault frequency is automatically incremented by one.

[0021] Preferably, the signal acquisition step further includes:

[0022] Auxiliary traveling wave sensors are installed at topological abrupt change points in the branch lines of the collector line. Several auxiliary traveling wave sensors and traveling wave sensing modules at the beginning and end of the line form a signal complementary network. The transient traveling wave signals collected by the auxiliary sensors are used to analyze the reflection and refraction characteristics of the branch lines and, together with the transient traveling wave signals of the main line collected by the traveling wave sensing modules, to decouple fault sections under multi-branch topology. The topological abrupt change points include: branch boxes, T-junctions, and the transition points between cable and overhead lines.

[0023] Preferably, the method of decoupling fault sections in a multi-branch topology by cooperating with the transient traveling wave signal of the main line acquired by the traveling wave sensing module includes the following steps:

[0024] The amplitude, arrival time, and reflection coefficient of the reflected wave are extracted from the branch line signals acquired by the auxiliary traveling wave sensor.

[0025] The line topology constraint model also sets up a catadioptric template for each branch T-junction node. The reflection coefficient and the time difference between the arrival of the catadioptric wave at the auxiliary sensor and the traveling wave sensing module are compared with the catadioptric template of each branch T-junction node. When the matching degree is greater than the preset catadioptric threshold, the target branch is determined.

[0026] Based on the physical length and traveling wave velocity of the target branch, the distance from the fault point to the T-junction node is calculated. The calculated distance is then mapped to the adjacent physical interval unit of the target branch. When the calculated distance does not exceed the physical length of the target branch, the actual fault interval is determined to be within the adjacent physical interval unit of the target branch.

[0027] Preferably, in the time synchronization step, the time stamp alignment of the arrival time of the transient traveling wave signal using a high-precision synchronization clock based on the BeiDou system includes the following steps:

[0028] The traveling wave sensing module and the auxiliary traveling wave sensor are connected to a high-precision synchronous clock based on the BeiDou system, and receive the standard time signal and second pulse of the BeiDou system in real time to calibrate their respective local clocks to keep them consistent with the BeiDou time reference. When the traveling wave sensing module and the auxiliary traveling wave sensor collect transient traveling wave signals, they generate timestamps based on the calibrated local clocks.

[0029] Preferably, the adjacent physical interval unit includes: the interval area between adjacent poles and towers in the main line, the interval area between adjacent cable joints in the main line, and the interval area between adjacent generator sets, adjacent poles and towers in the branch line section, and adjacent cable joints in the branch line section.

[0030] Preferably, in the signal processing step, the acquired transient traveling wave signal is decomposed and denoised using wavelet transform technology, and fault features are extracted, including the following steps:

[0031] The db4 wavelet is selected as the basis function to perform multi-scale decomposition on the acquired transient traveling wave signal to obtain low-frequency approximate components and high-frequency detail components at different scales. The noise-containing high-frequency detail components are suppressed by threshold processing method, and the high-frequency components containing fault features and low-frequency approximate components are retained. The denoised transient traveling wave signal is reconstructed by wavelet inverse transform.

[0032] Abrupt change point analysis was performed on the reconstructed transient traveling wave signal to identify and record the wavefront arrival time.

[0033] Extract the peak value of the signal at the wavefront moment as the wavefront amplitude;

[0034] Frequency band analysis was performed on the reconstructed transient traveling wave signal, and the frequency bands with the highest energy proportion were extracted as the main spectral features.

[0035] Preferably, in the decision output step, the operation and maintenance strategy is obtained in the following way:

[0036] Based on the fault characteristics, the actual fault range, and historical operation and maintenance data, the corresponding operation and maintenance strategy is matched from the preset operation and maintenance solution library.

[0037] Beneficial Effects: The power line operation analysis method based on transient traveling wave signal analysis technology proposed in this application achieves online real-time acquisition of fault signals by deploying traveling wave sensing modules at both ends of the main line of the power line to collect transient traveling wave signals in real time, laying the foundation for rapid fault response. Secondly, wavelet transform technology is used to decompose and denoise the transient traveling wave signals at multiple scales and extract fault features, effectively suppressing noise interference, improving signal quality and the accuracy of fault feature identification, and avoiding misjudgments caused by noise. Simultaneously, the high-precision synchronous clock of the BeiDou system enables nanosecond-level timestamps of the arrival time of the transient traveling wave signals. The system ensures the consistency of the time base, providing an accurate time reference for fault location. Furthermore, by estimating transmission equation parameters and combining them with a pre-built line topology constraint model, the system performs multi-dimensional calculations of the initial fault location using adjacent physical interval units as the smallest solution unit. This effectively adapts to the complex multi-branch structure of the power collection line and accurately determines the actual fault range. Additionally, based on the determined actual fault range, decision-making information is generated and pushed to the backend in real time, enabling rapid fault identification and information transmission. This helps maintenance personnel to carry out timely repairs, reduce downtime losses caused by faults, and improve the reliability of power collection line operation. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology, as provided in an embodiment of this application. Detailed Implementation

[0040] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0041] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] This embodiment discloses, as follows: Figure 1 This paper presents a method for analyzing the operation of power collection lines based on transient traveling wave signal analysis technology. The aim is to address core issues in existing technologies, such as low positioning accuracy, slow response, and weak anti-interference capabilities, thereby meeting the needs of rapid fault detection and maintenance in power collection lines at new energy power plants. This method includes signal acquisition, signal processing, time synchronization, fault determination, and decision output steps.

[0043] In detail

[0044] The signal acquisition steps include: deploying traveling wave sensing modules at the beginning and end of the main line of the collector line to acquire transient traveling wave signals generated by the fault in real time. The transient traveling wave signals include transient traveling wave voltage and transient traveling wave current signals.

[0045] In this embodiment, the distributed traveling wave sensing module includes a traveling wave electromagnetic sensor and a traveling wave photoelectric sensor, and the distributed traveling wave sensing module acquires transient signals across the entire frequency band (1kHz–100MHz) in the following manner:

[0046] In the low-frequency band of 1kHz–1MHz, the low-frequency transient characteristics of weak faults (such as high-impedance grounding) are captured by traveling wave electromagnetic sensors.

[0047] In the high-frequency band of 1MHz–100MHz, the system switches to a traveling wave photoelectric sensor and captures high-frequency transient characteristics (such as lightning strikes and partial discharges) through optical fiber transmission with anti-interference characteristics.

[0048] Secondly, the signal acquisition steps also include:

[0049] Auxiliary traveling wave sensors are installed at topological abrupt change points in the branch lines of the collector line. Several auxiliary traveling wave sensors and traveling wave sensing modules at the beginning and end of the line form a signal complementary network. The transient traveling wave signals collected by the auxiliary sensors are used to analyze the reflection and refraction characteristics of the branch lines, and are used in conjunction with the transient traveling wave signals of the main line collected by the traveling wave sensing modules to decouple the fault sections under multi-branch topology, thereby improving the accuracy of weak signal capture and the ability to identify branch faults. Topological abrupt change points include: branch boxes, T-junctions, and the transition points between cable and overhead lines.

[0050] Based on the aforementioned signal acquisition steps, the signal acquisition stage constructs a complementary signal network across the entire line by deploying traveling wave sensing modules at both ends of the main line and combining them with auxiliary traveling wave sensors placed at topological abrupt change points on branch lines. The distributed traveling wave sensing modules include traveling wave electromagnetic sensors and traveling wave photoelectric sensors, enabling full-band transient signal acquisition: in the low-frequency band, electromagnetic sensors capture low-frequency characteristics of weak faults such as high-impedance grounding; in the high-frequency band, photoelectric sensors are used, leveraging the anti-interference characteristics of fiber optic transmission to capture high-frequency characteristics such as lightning strikes and partial discharges. This architecture not only achieves online real-time acquisition of fault signals, solving the time-consuming and labor-intensive problems of traditional offline location methods, but also improves the accuracy of weak signal capture and the ability to identify branch faults through multi-sensor collaboration and full-band coverage, providing comprehensive signal support for rapid fault response under complex line structures.

[0051] The signal processing steps include: performing multi-scale decomposition and denoising on the acquired transient traveling wave signal using wavelet transform technology, and extracting fault features, including wavefront arrival time, amplitude, and spectral characteristics.

[0052] In this embodiment, the acquired transient traveling wave signal is decomposed and denoised using wavelet transform technology, and fault features are extracted, including the following steps:

[0053] The db4 wavelet is selected as the basis function to perform multi-scale decomposition on the acquired transient traveling wave signal to obtain low-frequency approximate components and high-frequency detail components at different scales. The noise-containing high-frequency detail components are suppressed by threshold processing method, and the high-frequency components containing fault features and low-frequency approximate components are retained. The denoised transient traveling wave signal is reconstructed by wavelet inverse transform.

[0054] Abrupt change point analysis was performed on the reconstructed transient traveling wave signal to identify and record the wavefront arrival time.

[0055] Extract the peak value of the signal at the wavefront moment as the wavefront amplitude;

[0056] Frequency band analysis is performed on the reconstructed transient traveling wave signal, and several frequency bands (e.g., 3) with the highest energy proportion are extracted as the main spectral features.

[0057] Based on the above signal processing steps, the signal processing stage uses wavelet transform technology to process the transient traveling wave signal. At the same time, it accurately identifies fault characteristics through mutation point analysis. This not only effectively suppresses noise interference and avoids misjudgment caused by the drowning out of useful signals, but also achieves accurate quantification of fault characteristics through standardized wavelet decomposition and feature extraction processes, providing high-quality feature input for subsequent fault location.

[0058] The time synchronization steps include: using a high-precision synchronous clock based on the BeiDou system to timestamp the arrival time of the transient traveling wave signal, ensuring that the timestamp alignment error is synchronized at the nanosecond level.

[0059] In this embodiment, the arrival time of the transient traveling wave signal is timestamped using a high-precision synchronous clock based on the BeiDou system, including the following steps:

[0060] The traveling wave sensing module and the auxiliary traveling wave sensor are connected to a high-precision synchronous clock based on the BeiDou system, and receive the standard time signal and second pulse of the BeiDou system in real time to calibrate their respective local clocks to keep them consistent with the BeiDou time reference. When the traveling wave sensing module and the auxiliary traveling wave sensor collect transient traveling wave signals, they generate timestamps based on the calibrated local clocks.

[0061] Based on the aforementioned time synchronization steps, the time synchronization process utilizes the high-precision synchronous clock of the BeiDou system to ensure that the time reference of all sensors is consistent with BeiDou time. When acquiring transient traveling wave signals, each sensor generates a timestamp based on its calibrated local clock, achieving nanosecond-level alignment of the arrival time of the transient traveling wave signal. This not only solves the problem of insufficient time synchronization accuracy in existing technologies but also ensures the consistency of signals between the main line and branch lines in the time dimension through unified time calibration of all sensors along the entire line. This provides a stringent time reference for the accurate calculation of the fault traveling wave propagation time difference in multi-branch structures, further improving the reliability of the positioning results.

[0062] The fault determination steps include: estimating the initial fault location through transmission equation parameters; and, based on the initial fault location, using adjacent physical interval units as the smallest solution unit, performing multi-dimensional calculations on the initial fault location using a pre-built line topology constraint model (including a branch structure database) to determine the actual fault range. Adjacent physical interval units are the interval areas between adjacent landmark facilities or landmark nodes with clearly defined physical boundaries in the collector line. The line topology constraint model includes the physical coordinates, lengths, node relationships, and fault feature database of the main line and branch lines of the collector line. Landmark facilities or nodes are the basic units in the line structure used to divide line segments, carry functions, or identify locations. Their spacing is determined by line design specifications or operation and maintenance requirements and can be clearly defined through physical coordinates, relative distances, or topological relationships. Feasibly, adjacent physical interval units include: the interval areas between adjacent towers and adjacent cable joints in the main line, and the interval areas between adjacent generator sets, adjacent towers, and adjacent cable joints in branch line segments.

[0063] In this embodiment, the preliminary fault location is calculated from multiple dimensions based on a pre-built line topology constraint model to determine the actual fault range, including the following steps:

[0064] Based on the pre-built line topology constraint model, the preliminary fault location (such as the distance W from the head end) obtained by estimating the transmission equation parameters is mapped to a specific line segment, and the actual fault interval is limited to the range between adjacent physical interval units within the line segment (such as when W falls between "tower A (0.5km) - tower B (0.8km)", the spatial constraint interval is 0.5km to 0.8km).

[0065] If the spatial coordinates of the initial fault location match the topological range of the branch line (i.e., the initial fault location (such as the distance from the head end, geographical coordinates) estimated by the transmission equation parameters matches the spatial range of a branch line in the pre-built line topology model (such as the starting and ending coordinates of the branch line, or the adjacent physical interval units contained in the branch line)), check whether there is at least one T-junction between the branch line and the main line. If there is no T-junction, define the initial fault location as a pseudo-interval without electrical association and remove the initial fault location. Otherwise, match the fault features extracted in the signal processing step with the fault feature library (such as the peak frequency of the joint fault spectrum 20±5MHz and the peak frequency of the tower fault spectrum 5±2MHz). When the fault feature matching degree exceeds the preset matching degree threshold (such as 80%), lock the adjacent physical interval unit corresponding to the fault feature as the actual fault interval.

[0066] Based on the aforementioned fault determination steps, the fault determination process first obtains the preliminary fault location through transmission equation parameter estimation. Then, combined with a pre-constructed line topology constraint model, it performs multi-dimensional calculations using adjacent physical interval units as the smallest solution unit to determine the target interval. For multi-branch scenarios, the target branch is also determined by comparing the catadioptric and reflective wave characteristics collected by auxiliary sensors with the catadioptric and reflective templates of the branch T-junction nodes. The distance from the fault point to the T-junction node is calculated by combining the traveling wave velocity and mapped to adjacent physical interval units. This process, through multi-dimensional collaboration of topology constraints, feature matching, and branch catadioptric and reflective analysis, effectively adapts to the complex structure of multi-branch collector lines, solves the problem of low positioning accuracy of traditional algorithms in multi-branch scenarios, and ensures accurate locking of the actual fault interval.

[0067] Therefore, based on the placement of auxiliary traveling wave sensors at topology abrupt change points, and in conjunction with the transient traveling wave signals of the main line collected by the traveling wave sensing module, fault interval decoupling under multi-branch topology is achieved, including the following steps:

[0068] The amplitude, arrival time, and reflection coefficient R of the reflected wave are extracted from the branch line signal collected by the auxiliary traveling wave sensor. R = (Z2-Z1) / (Z2+Z1), where Z1 is the characteristic impedance of the main line and Z2 is the characteristic impedance of the branch line. When |R|>0.3, it is determined to be a significant reflection.

[0069] The line topology constraint model also sets up catadioptric templates for each branch T-junction node. The reflection coefficient and the time difference between the arrival of the catadioptric wave at the auxiliary sensor and the traveling wave sensing module are compared with the catadioptric templates for each branch T-junction node. When the matching degree is greater than the preset catadioptric threshold, the target branch is determined.

[0070] Based on the physical length L of the target branch and the traveling wave velocity v, the distance from the fault point to the T-connection node is calculated (S = v × Δt / 2, where Δt is the round-trip time of the reflected wave). The calculated distance is then mapped to the adjacent physical interval unit of the target branch. When the calculated distance does not exceed the physical length of the target branch, the actual fault interval is determined to be within the adjacent physical interval unit of the target branch (such as between unit A and unit B on the branch line).

[0071] In multi-branch lines, fault traveling waves exhibit complex reflection and refraction phenomena at T-junctions. Traditional single-end or double-end ranging methods are prone to wavefront identification confusion due to the superposition of reflected waves. However, by comparing the reflection and refraction characteristics collected by auxiliary sensors with preset branch T-junction reflection and refraction templates (including parameters such as characteristic impedance differences and reflection delays), the propagation paths of traveling waves in the main line and each branch can be accurately distinguished. This physically isolates the signals of different branches in time and space, effectively eliminating cross-interference of signal reflections in multi-branch structures and avoiding positioning ambiguity caused by crosstalk between branches. Secondly, by mapping the distance from the fault point to the T-junction to the adjacent physical interval units of the branch line, the positioning range of the branch fault is strictly constrained within a clear physical boundary. Compared to the cross-branch misjudgment that traditional algorithms are prone to in multi-branch scenarios, this method, through dual anchoring of topological coordinates and physical interval units, controls the positioning error of the branch fault within a single interval unit, improving the positioning accuracy and boundary certainty of branch line faults. Furthermore, for weak faults such as high-resistance grounding in branch lines, the transient traveling wave signal has low amplitude and a gentle wavefront. The main line sensor may lose the signal due to attenuation, while the auxiliary sensor near the fault point can directly capture the reflection and refraction characteristics of the weak signal. Simultaneously, for complex topologies with multiple nested branches (such as multi-level T-connections), by hierarchically comparing the reflection and refraction templates of each branch with the actual collected reflection coefficients and time differences, non-target branches can be peeled off layer by layer, ultimately pinpointing the specific branch and interval where the fault is located. This solves the pain point of traditional technologies struggling to locate fault branches in multi-nested branches. Moreover, the power collection lines of new energy power plants may undergo dynamic adjustments to their branch structures due to expansion and renovation. The preset branch physical coordinates and node relationships in the topology constraint model can be updated synchronously with line renovations, and the reflection and refraction templates of the auxiliary sensors can also be iteratively optimized using historical data. This collaborative adaptation mechanism of hardware perception and software model ensures that the fault interval decoupling capability is not limited by the number of branches or topology complexity, maintaining stable calculation accuracy even when the line structure changes dynamically, and improving the system's adaptability to complex power grid topologies.

[0072] Furthermore, when there is a T-junction between the branch line and the main line, if the fault feature matching degree is lower than the preset matching degree threshold, the historical fault records within the past N time periods (such as 1-3 years) of the specific line segment are called, and the adjacent physical interval unit where the initial location of the fault is located is defined as the candidate fault interval.

[0073] If the historical failure frequency of a candidate fault interval exceeds a preset frequency threshold (e.g., 3 times), the failure probability weight of the candidate fault interval is increased (weight coefficient ≥ 0.7), and it is used as a high-priority candidate fault interval; otherwise, the failure probability weight of the candidate fault interval is decreased, and the candidate fault interval with the highest fault feature matching degree is retained as a low-priority candidate fault interval.

[0074] Among them, high-priority candidate fault intervals and low-priority candidate fault intervals are recorded in the historical fault record after the current (this) fault is determined (regardless of whether it is confirmed as an actual fault interval). They serve as the data source for calling the historical fault record when the next fault is determined. If a candidate fault interval is confirmed as an actual fault interval in any fault determination, its historical fault frequency is automatically incremented by one; otherwise, its interval identifier and weight record are still retained for subsequent optimization of the calculation accuracy of fault probability weights.

[0075] For scenarios where the fault feature matching degree is lower than a preset threshold, the robustness and accuracy of fault zone location under complex operating conditions are further improved by introducing historical fault records and a fault probability weighting mechanism. This effectively solves the problem of ambiguous location caused by insufficient feature matching degree for weak faults and atypical faults (such as gradual defects like moisture in cold shrink joints). The statistical characteristics of historical fault data provide supplementary evidence for fault zone determination. Secondly, a priority ranking mechanism guides maintenance resources towards high-probability fault zones, reducing ineffective investigations and improving the efficiency of fault location in complex multi-branch lines. Furthermore, through the dynamic accumulation of historical data and weight self-optimization, the system possesses learning capabilities, continuously adapting to actual operating conditions such as line aging and changes in fault modes during long-term operation, continuously improving location accuracy and reliability, and providing data-driven decision support for the full lifecycle fault management of new energy power station collection lines.

[0076] The decision output steps include: generating decision information based on the determined actual fault range and pushing the decision information to the backend in real time. The decision information includes fault characteristics, location details and operation and maintenance strategies.

[0077] In this embodiment, the operation and maintenance strategy is obtained in the following way:

[0078] Based on the fault characteristics, the actual fault range, and historical operation and maintenance data, the corresponding operation and maintenance strategy is matched from the preset operation and maintenance solution library.

[0079] Based on the above decision-making output steps, the decision-making output stage generates decision-making information. The operation and maintenance strategy is obtained by matching fault characteristics, actual fault ranges, and historical operation and maintenance data in a preset operation and maintenance solution library, ensuring the strategy's pertinence and scientific nature. After this decision-making information is pushed to the backend in real time, it not only realizes rapid fault identification and information transmission, but also reduces the blindness of operation and maintenance decisions through standardized, data-driven operation and maintenance strategy recommendations. This helps operation and maintenance personnel to carry out emergency repairs accurately, thereby reducing unplanned downtime and losses caused by faults and improving the reliability and operation and maintenance efficiency of the power collection line.

[0080] In summary, the power line operation analysis method based on transient traveling wave signal analysis technology in this embodiment constructs a power line operation analysis system through multi-stage collaboration: the traveling wave sensing modules at the beginning and end of the main line and the auxiliary sensors at branch topology change points form a complementary signal network for the entire line. Combined with full-band acquisition technology, it realizes online real-time acquisition of fault signals, solves the problem of inefficient offline positioning, and improves the ability to identify weak signals and branch faults; through db4 wavelet multi-scale decomposition denoising and feature quantization extraction, noise interference is effectively suppressed, providing high-quality feature input for positioning; relying on the BeiDou system to achieve nanosecond-level time synchronization, it ensures that the time reference of the main and branch signals is consistent, supporting accurate time difference calculation; through transmission equation parameter estimation combined with a topological constraint model containing branch structures, using adjacent physical interval units as the solution unit, and with the help of reflection and refraction feature comparison and historical fault weighting mechanism, it accurately adapts to complex multi-branch structures and solves the problem of cross-branch misjudgment; finally, it generates decision-making information containing operation and maintenance strategies and pushes it in real time, helping to quickly repair, reduce downtime losses, and comprehensively improve the reliability and operation and maintenance efficiency of power line operation.

[0081] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0082] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology, characterized in that, The method includes: The signal acquisition steps include: deploying traveling wave sensing modules at the beginning and end of the main line of the collector line to acquire transient traveling wave signals generated by the fault in real time, wherein the transient traveling wave signals include transient traveling wave voltage and transient traveling wave current signals; The signal processing steps include: performing multi-scale decomposition and denoising on the acquired transient traveling wave signal using wavelet transform technology, and extracting fault features, including wavefront arrival time, amplitude, and spectral characteristics. The time synchronization steps include: aligning the arrival time of the transient traveling wave signal with a timestamp using a high-precision synchronous clock based on the BeiDou system; The fault determination steps include: obtaining the preliminary fault location through transmission equation parameter estimation, and, based on the preliminary fault location, using adjacent physical interval units as the smallest solution unit, performing multi-dimensional calculations on the preliminary fault location based on a pre-built line topology constraint model to determine the actual fault range. The adjacent physical interval units are the interval areas between adjacent landmark facilities or landmark nodes with clear physical boundaries in the collector line. The line topology constraint model is set with the physical coordinates, lengths, node relationships, and fault feature database of the main line and branch lines of the collector line. The decision output steps include: generating decision information based on the determined actual fault range, and pushing the decision information to the background in real time, wherein the decision information includes fault characteristics, location details and operation and maintenance strategies.

2. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 1, characterized in that, In the signal acquisition step, the distributed traveling wave sensing module includes a traveling wave electromagnetic sensor and a traveling wave photoelectric sensor, and the distributed traveling wave sensing module acquires transient signals across the entire frequency band in the following manner: In the low-frequency range of 1kHz–1MHz, the low-frequency transient characteristics of weak faults are captured by traveling wave electromagnetic sensors. In the high-frequency band of 1MHz–100MHz, the system switches to a traveling wave photoelectric sensor and captures high-frequency transient characteristics through optical fiber transmission to overcome interference.

3. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 1, characterized in that, In the fault determination step, the multi-dimensional solution of the preliminary fault location based on the pre-built line topology constraint model to determine the actual fault range includes the following steps: Based on the pre-built line topology constraint model, the preliminary fault location obtained by estimating the transmission equation parameters is mapped to a specific line segment, and the actual fault interval is limited to the range between adjacent physical interval units within the line segment. If the spatial coordinates of the initial fault location match the topology range of the branch line, check whether there is at least one T-junction between the branch line and the main line. If there is no T-junction, define the initial fault location as a pseudo-interval without electrical connection and remove the initial fault location. Otherwise, match the fault features extracted in the signal processing step with the fault feature library. When the fault feature matching degree exceeds the preset matching degree threshold, lock the adjacent physical interval unit corresponding to the fault feature as the actual fault interval.

4. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 3, characterized in that, When there is a T-junction between the branch line and the main line, if the fault feature matching degree is lower than the preset matching degree threshold, the historical fault records of the specific line segment within the past N time periods are called, and the adjacent physical interval unit where the initial location of the fault is located is defined as the candidate fault interval. If the historical failure frequency of a candidate fault interval exceeds a preset frequency threshold, the failure probability weight of the candidate fault interval is increased and it is designated as a high-priority candidate fault interval; otherwise, the failure probability weight of the candidate fault interval is decreased and the candidate fault interval with the highest fault feature matching degree is retained as a low-priority candidate fault interval. The high-priority candidate fault interval and the low-priority candidate fault interval are both recorded in the historical fault record after the current fault is determined, and serve as the data source for calling the historical fault record when the next fault is determined. If a candidate fault interval is confirmed as an actual fault interval in any fault determination, its historical fault frequency is automatically incremented by one.

5. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to any one of claims 1-3, characterized in that, The signal acquisition step also includes: Auxiliary traveling wave sensors are installed at topological abrupt change points in the branch lines of the collector line. Several auxiliary traveling wave sensors and traveling wave sensing modules at the beginning and end of the line form a signal complementary network. The transient traveling wave signals collected by the auxiliary sensors are used to analyze the reflection and refraction characteristics of the branch lines and, together with the transient traveling wave signals of the main line collected by the traveling wave sensing modules, to decouple fault sections under multi-branch topology. The topological abrupt change points include: branch boxes, T-junctions, and the transition points between cable and overhead lines.

6. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 5, characterized in that, The method of decoupling fault sections in a multi-branch topology by cooperating with the traveling wave sensor module to acquire the transient traveling wave signal of the main line includes the following steps: The amplitude, arrival time, and reflection coefficient of the reflected wave are extracted from the branch line signals acquired by the auxiliary traveling wave sensor. The line topology constraint model also sets up a catadioptric template for each branch T-junction node. The reflection coefficient and the time difference between the arrival of the catadioptric wave at the auxiliary sensor and the traveling wave sensing module are compared with the catadioptric template of each branch T-junction node. When the matching degree is greater than the preset catadioptric threshold, the target branch is determined. Based on the physical length and traveling wave velocity of the target branch, the distance from the fault point to the T-junction node is calculated. The calculated distance is then mapped to the adjacent physical interval unit of the target branch. When the calculated distance does not exceed the physical length of the target branch, the actual fault interval is determined to be within the adjacent physical interval unit of the target branch.

7. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 5, characterized in that, In the time synchronization step, the time stamp alignment of the arrival time of the transient traveling wave signal using a high-precision synchronization clock based on the BeiDou system includes the following steps: The traveling wave sensing module and the auxiliary traveling wave sensor are connected to a high-precision synchronous clock based on the BeiDou system, and receive the standard time signal and second pulse of the BeiDou system in real time to calibrate their respective local clocks to keep them consistent with the BeiDou time reference. When the traveling wave sensing module and the auxiliary traveling wave sensor collect transient traveling wave signals, they generate timestamps based on the calibrated local clocks.

8. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 1, characterized in that, The adjacent physical interval units include: the interval areas between adjacent poles and towers in the main line, and between adjacent cable joints in the main line, as well as the interval areas between adjacent generator sets, adjacent poles and towers in the branch line section, and adjacent cable joints in the branch line section.

9. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 1, characterized in that, In the signal processing step, the acquired transient traveling wave signal is decomposed and denoised using wavelet transform technology, and fault features are extracted, including the following steps: The db4 wavelet is selected as the basis function to perform multi-scale decomposition on the acquired transient traveling wave signal to obtain low-frequency approximate components and high-frequency detail components at different scales. The noise-containing high-frequency detail components are suppressed by threshold processing method, and the high-frequency components containing fault features and low-frequency approximate components are retained. The denoised transient traveling wave signal is reconstructed by wavelet inverse transform. Abrupt change point analysis was performed on the reconstructed transient traveling wave signal to identify and record the wavefront arrival time. Extract the peak value of the signal at the wavefront moment as the wavefront amplitude; Frequency band analysis was performed on the reconstructed transient traveling wave signal, and the frequency bands with the highest energy proportion were extracted as the main spectral features.

10. The method for analyzing the operation of a collector line based on transient traveling wave signal analysis technology according to claim 1, characterized in that, In the decision output step, the operation and maintenance strategy is obtained in the following way: Based on the fault characteristics, the actual fault range, and historical operation and maintenance data, the corresponding operation and maintenance strategy is matched from the preset operation and maintenance solution library.

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