Long-distance power transmission line grounding line selection system based on optical fiber sensing network
By generating mutation, similarity, and error indices through multi-dimensional data acquisition and intelligent evaluation, and combining them with the threshold of the positioning management module, the problem of inaccurate positioning in traditional fiber optic sensor networks is solved, enabling accurate identification and rapid response to faults in long-distance power transmission lines.
Patent Information
- Application Number
- CN202512052235.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-03-27
AI Technical Summary
Traditional long-distance transmission line grounding location systems based on fiber optic sensor networks lack unified quantitative standards, resulting in inaccurate positioning, delayed operation and maintenance response, and a lack of closed-loop management.
A multi-dimensional acquisition module is used to acquire transmission line data, and an intelligent evaluation module generates mutation index, similarity index, and error index. Combined with the preset thresholds of the positioning management module, accurate fault identification and rapid location are achieved.
It improves the accuracy of fault identification and operational efficiency, shortens the fault handling cycle, and achieves efficient management and control of the entire process.
Smart Images
Figure CN121741383A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power transmission line fault diagnosis, in particular to a long-distance power transmission line grounding line selection system based on an optical fiber sensing network. BACKGROUND
[0002] The optical fiber sensing network is an intelligent monitoring system constructed based on optical fiber transmission and sensing technology, which has the advantages of anti-electromagnetic interference, low transmission loss, fast response speed, and distributed monitoring, and can accurately capture multi-dimensional physical and electrical signals to provide reliable data support for complex scenarios. As the core backbone of the power system, long-distance power transmission lines often span vast areas and are in complex and variable environments, and are easily affected by lightning, icing, insulation aging and other factors to cause grounding faults, which not only threatens the safe and stable operation of the power grid, but also may cause large-scale power outages and cause significant economic losses. Grounding line selection is a key link after a fault occurs, and its core goal is to quickly and accurately locate the fault branch, while the optical fiber sensing network can realize real-time acquisition and accurate analysis of fault signals, greatly improving the efficiency and accuracy of grounding line selection, providing strong protection for rapid fault repair and preventing the spread of faults. It is an important support for ensuring the safe and stable operation of long-distance power transmission lines, reducing operation and maintenance costs, and improving the intelligent management level of the power grid.
[0003] At present, the traditional long-distance power transmission line grounding line selection system based on the optical fiber sensing network relies on manual experience or simple waveform comparison, lacks a unified quantitative standard, and is prone to inaccurate positioning due to data level differences or experience deviations. In addition, it does not pay attention to the response error in the signal transmission process of long-distance lines, resulting in a lag in operation and maintenance response and a lack of closed-loop management. SUMMARY
[0004] In view of the shortcomings of the prior art, the present application provides a long-distance power transmission line grounding line selection system based on an optical fiber sensing network, which has the advantages of high fault identification accuracy and high efficiency in the whole process of management and control, and solves the problems of inaccurate fault positioning and lack of closed-loop management of the traditional long-distance power transmission line grounding line selection system based on the optical fiber sensing network.
[0005] To achieve the above purpose, the present application provides the following technical scheme: a long-distance power transmission line grounding line selection system based on an optical fiber sensing network, comprising a multi-dimensional acquisition module, an intelligent evaluation module and a positioning management module; The multi-dimensional acquisition module acquires management data of all monitoring sections of the power transmission line, management data of power transmission signals and management data of synchronous response timestamps through a connection optical fiber link and a distributed sensing device, and classifies and forms segmented data sets, signal data sets and synchronous data sets; The intelligent evaluation module includes a fault evaluation unit, a line selection analysis unit and an error evaluation unit, and the fault evaluation unit is provided with a fixed monitoring period , and the mutation index of each monitoring section of the power transmission line is generated , the similar index of each branch line in the monitoring section is generated according to the segmented data set and the signal data set , the error index of each monitoring section of the power transmission line is generated according to the segmented data set and the synchronization data set ; The positioning management module is provided with a fixed value mutation threshold , a similar threshold , and an error threshold , in combination with the mutation index , the similar index , and the error index , the corresponding operation and maintenance measures are triggered.
[0006] Preferably, the segmented data set includes the reference zero sequence current value, the reference current phase angle, the reference line temperature, the reference line humidity, the reference arc sound frequency, the number of branch lines, the vibration amplitude of the fault reference waveform, the number of waveform sampling points, the line length and the total number of each monitoring section of the power transmission line.
[0007] Preferably, the signal data set includes the zero sequence current value, the current phase angle, the line temperature, the line humidity, the arc sound frequency and the vibration amplitude of the branch line waveform of each monitoring section of the power transmission line.
[0008] Preferably, the synchronization data set contains the response timestamp and the fiber signal propagation speed of each monitoring section of the power transmission line.
[0009] Preferably, the mutation index The calculation process is as follows: S11, according to the segmented data set, the management data of the first monitoring section of the power transmission line is extracted, and the reference zero sequence current value of the first monitoring section is recorded as , the reference current phase angle of the first monitoring section is recorded as , the reference line temperature of the first monitoring section is recorded as , the reference line humidity of the first monitoring section is recorded as , and the reference arc sound frequency of the first monitoring section is recorded as ; S12, according to the signal data set, the management data of the first monitoring section in the monitoring period The power transmission signal management data of the first monitoring section, and the first The zero-sequence current value of each monitoring segment is denoted as: , Indicates the monitoring period The total number of timestamps recorded within the record, will be the first The current phase angle of each monitoring segment is denoted as... , will the The line temperature of each monitoring section is recorded as follows: , will the The line humidity of each monitoring section is recorded as follows: , will the The arc sound frequency of each monitoring segment is recorded as follows: ; S13, Calculate the monitoring cycle within, no. The cumulative absolute deviation of the current phase angle of each monitoring segment Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Current phase angle at each timestamp ; S14. Calculate the monitoring cycle within, no. Cumulative absolute deviation of line temperature in each monitoring section Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Line temperature at each timestamp ; S15. Calculate the monitoring cycle based on S11-S14. within, no. mutation index of each monitoring segment Its expression is as follows: In the formula, Indicates the first The peak value of zero-sequence current in each monitoring segment Indicates the first The maximum line humidity of each monitoring section, Indicates the first The peak value of the electric arc sound frequency in each monitoring segment , , , and All are weighting coefficients, and .
[0010] Preferably, the similarity index The calculation process is as follows: S21. Based on the segmented dataset, divide the first... The branch lines of each monitoring segment are denoted as , Indicates the first The total number of branches in each monitoring segment, and the monitoring cycle within, no. The vibration amplitude of the fault reference waveform for each monitoring segment is denoted as: , Indicates the number of waveform sampling points; S22, Calculate the monitoring cycle within, no. Average vibration amplitude of the fault reference waveform in each monitoring segment Its expression is as follows: S23. Based on the signal dataset, the monitoring period... within, no. The first monitoring segment The vibration amplitude of the branch waveform is denoted as , ; S24. Calculate the monitoring cycle within, no. mean amplitude of the branch waveform Its expression is as follows: S25. Calculate the monitoring cycle based on S21-S24. within, no. Similarity index between branch waveform and fault reference waveform Its expression is as follows: In the formula, Indicates the monitoring period within, no. The covariance between the branch waveform and the fault reference waveform. Indicates the monitoring period within, no. The standard deviation of the waveform of each branch line, Indicates the monitoring period Within, the standard deviation of the fault reference waveform.
[0011] Preferably, the error index The calculation process is as follows: S31. Based on the segmented dataset, the line length of each monitoring segment of the transmission line is recorded as follows: , This represents the total number of monitoring segments. Then, based on the synchronized dataset, the response timestamp for each monitoring segment of the transmission line is recorded as... The speed of optical fiber signal propagation is denoted as ; S32. Calculate the average value of the length difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The length of the line in each monitoring section Indicates the transmission line number The length of the line in the monitoring segment, and the length of the monitoring segment. The monitoring segment and the first The monitoring segments are adjacent; S33. Calculate the average response time difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The response timestamp of each monitoring segment Indicates the transmission line number The response timestamp of each monitoring segment; S34. Based on S31-S33, calculate the error index of the transmission line. Its expression is as follows: In the formula, This represents the response time difference between adjacent monitoring segments under ideal conditions.
[0012] Preferably, the mutation index ≥ Mutation threshold When this occurs, it indicates that the sudden change in the ground fault of the corresponding monitoring section has exceeded the standard. Triggering measures include activating the line distance protection and conducting an emergency inspection of the transmission line of the corresponding monitoring section.
[0013] Preferably, the similarity index ≥ Similarity threshold When this occurs, it indicates that the corresponding branch line is faulty. Triggering measures include suspending the load transmission of the corresponding branch line and promptly notifying management personnel to conduct key investigations.
[0014] Preferably, the error index ≥ Error Threshold When this occurs, it indicates that the response error of the transmission line has exceeded the standard. Triggering measures include notifying management personnel to correct the signal response timestamp in a timely manner and initiating a self-test procedure.
[0015] Compared with existing technologies, this invention provides a grounding fault location system for long-distance transmission lines based on fiber optic sensor networks, which has the following advantages: 1. This invention acquires management data of all monitoring sections of the transmission line, management data of transmission signals, and management data of synchronization response timestamps through a multi-dimensional acquisition module. These data are then categorized into segmented datasets, signal datasets, and synchronization datasets, providing comprehensive data support for subsequent fault assessment, line selection analysis, and error judgment. The intelligent assessment module is set with a fixed monitoring cycle. Assess the degree of abrupt change in ground faults in each monitoring section of the transmission line and generate the corresponding abrupt change index. To avoid misjudgments caused by a single signal and improve the accuracy of fault identification, the fault severity of each branch within the monitoring section is analyzed to generate a corresponding similarity index. It has high accuracy in fault identification.
[0016] 2. This invention uses an intelligent evaluation module to assess the response error level of each monitoring section of the transmission line and generates a corresponding error index. This provides a basis for system calibration, ensuring the reliability of overall monitoring and judgment. The positioning management module is based on preset thresholds and linked with three major indices, triggering precise operation and maintenance measures for different exceedance situations, realizing rapid identification and accurate line selection of grounding faults in long-distance transmission lines, effectively shortening the fault handling cycle, and achieving high efficiency in the whole process control. Attached Figure Description
[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Example Please see Figure 1 Table 1 shows the experimental data of the mutation index, and Table 2 shows the experimental data of the similarity index. This invention provides a grounding selection system for long-distance transmission lines based on optical fiber sensor networks, including a multi-dimensional acquisition module, an intelligent evaluation module, and a positioning management module. The multi-dimensional acquisition module acquires management data of all monitoring sections of the transmission line, management data of transmission signals, and management data of synchronization response timestamps by connecting fiber optic links and distributed sensing devices, and classifies them into segmented datasets, signal datasets, and synchronization datasets. The segmented dataset includes the reference zero-sequence current value, reference current phase angle, reference line temperature, reference line humidity, reference arc sound frequency, number of branches, vibration amplitude of fault reference waveform, number of waveform sampling points, line length, and total number for each monitoring segment of the transmission line. The signal dataset includes the zero-sequence current value, current phase angle, line temperature, line humidity, arc sound frequency, and vibration amplitude of branch waveforms for each monitoring section of the transmission line. The synchronous dataset contains the response timestamps and fiber optic signal propagation speeds for each monitoring segment of the transmission line; The intelligent assessment module includes a fault assessment unit, a route selection analysis unit, and an error assessment unit. The fault assessment unit is set with a fixed monitoring period. By combining segmented datasets and signal datasets, the abrupt change in ground faults of each monitoring segment of the transmission line is evaluated, and a corresponding abrupt change index is generated. The calculation process is as follows: S11. Based on the segmented dataset, extract the first segment of the transmission line. The management data of the first monitoring segment, and the first The reference zero-sequence current value of each monitoring segment is denoted as: , will the The reference current phase angle of each monitoring segment is denoted as... , will the The reference line temperature for each monitoring section is recorded as follows: , will the The reference line humidity for each monitoring section is recorded as follows: , will the The reference arc frequency of each monitoring segment is denoted as ; S12. Based on the signal dataset, extract the monitoring period. within, no. The power transmission signal management data of the first monitoring section, and the first The zero-sequence current value of each monitoring segment is denoted as: , Indicates the monitoring period The total number of timestamps recorded within the record, will be the first The current phase angle of each monitoring segment is denoted as... , will the The line temperature of each monitoring section is recorded as follows: , will the The line humidity of each monitoring section is recorded as follows: , will the The arc sound frequency of each monitoring segment is recorded as follows: ; S13, Calculate the monitoring cycle within, no. The cumulative absolute deviation of the current phase angle of each monitoring segment Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Current phase angle at each timestamp ; S14. Calculate the monitoring cycle within, no. Cumulative absolute deviation of line temperature in each monitoring section Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Line temperature at each timestamp ; S15. Calculate the monitoring cycle based on S11-S14. within, no. mutation index of each monitoring segment Its expression is as follows: In the formula, Indicates the first The peak value of zero-sequence current in each monitoring segment Indicates the first The maximum line humidity of each monitoring section, Indicates the first The peak value of the electric arc sound frequency in each monitoring segment , , , and All are weighting coefficients, and ; Specifically, when a transmission line experiences a sudden ground fault, the fault phase current will form a loop through the earth, thereby generating a zero-sequence current. This is the most direct electrical characteristic reflecting a ground fault. At the same time, a ground fault will disrupt the balance of the three-phase current, causing an abnormal shift in the phase angle of the fault phase current. Ground faults are often accompanied by discharge heating, causing the line temperature at the fault point to rise abnormally. This is the core physical thermal characteristic of the fault. In addition, damp insulation can easily induce ground faults, and the ionization effect of the fault arc will also change the local humidity. Furthermore, the arc discharge generated by a ground fault will release characteristic sound waves of 100-1000Hz, such as the crackling sound during a fault. Through multi-dimensional signals, the misjudgment problem caused by a single signal can be effectively avoided, improving the accuracy of fault identification. The following are the experimental data for the mutation index, as shown in Table 1: Table 1: Experimental Data for the Mutation Index Table 1 shows the experimental data for the mutation index. Transmission line monitoring section A was selected as the experimental target, with a monitoring period of... Set to 10 seconds, data is collected every second, weighting coefficients. , , , , ; The location management module has a fixed threshold value for mutation. This is used to quickly determine whether the abrupt change in the grounding fault of each monitoring section of a transmission line exceeds the standard. The value directly affects the system's accuracy in identifying fault abrupt change signals and its response timeliness. An excessively high abrupt change threshold... This can lead to a sluggish system response to minor ground fault abrupt changes, making it unable to promptly detect weak anomalies in the early stages of a fault, delaying fault location and handling, and potentially causing the fault area to expand and equipment damage to worsen. Conversely, an overly sensitive system to fluctuations in line parameters during normal operation can frequently misinterpret these as fault abrupt changes, triggering unnecessary troubleshooting processes, increasing maintenance costs and the risk of line outages. Therefore, the optimal value of this parameter needs to be determined through calibration experiments of the following system: abrupt change threshold. The calibration method is as follows: A simulation environment for different monitoring sections of a transmission line was built using a fault simulation platform. This simulated single-phase grounding and two-phase grounding scenarios, setting different fault locations, fault resistance values, and fault development rates. Multiple sets of experiments were conducted, recording fault mutation characteristic parameters for each monitoring section, such as the amplitude of current mutation, the slope of voltage mutation, the time series curve of grounding resistance change, and the system's fault determination results. Then, composite fault types were simulated, such as complex scenarios involving single-phase grounding accompanied by line insulation aging and two-phase grounding superimposed with external force damage. The fault combination form and impact were adjusted. To study the impact range and evolution rhythm, multiple experiments were conducted to record the fluctuation patterns of fault mutation characteristic parameters and whether the system accurately triggered the location mechanism. For each candidate threshold, the collected fault mutation characteristic parameter time series curve was used as input. The number of times a true ground fault mutation was not identified in time due to improper threshold setting was counted as a missed detection, and the number of times normal line fluctuations were misjudged as fault mutations was counted as a false detection. Finally, the value that minimizes both the missed detection rate and the false detection rate, and meets the fault location response time standard, was selected as the mutation threshold. The preferred value; In Table 1, the mutation threshold is shown in the experimental data of the mutation index. The preferred value is 10. Based on the analysis, the mutation index of transmission line monitoring section A is... >Mutation threshold This indicates that the sudden change in the ground fault of monitoring section A has exceeded the standard. The triggering measures include activating the line distance protection to prevent the fault from spreading and to conduct an emergency inspection of the transmission line of monitoring section A. The route selection analysis unit analyzes the fault degree of each branch line within the monitoring segment based on the segmented dataset and signal dataset, and generates a corresponding similarity index. The calculation process is as follows: S21. Based on the segmented dataset, divide the first... The branch lines of each monitoring segment are denoted as , Indicates the first The total number of branches in each monitoring segment, and the monitoring cycle within, no. The vibration amplitude of the fault reference waveform for each monitoring segment is denoted as: , Indicates the number of waveform sampling points; S22, Calculate the monitoring cycle within, no. Average vibration amplitude of the fault reference waveform in each monitoring segment Its expression is as follows: S23. Based on the signal dataset, the monitoring period... within, no. The first monitoring segment The vibration amplitude of the branch waveform is denoted as , ; S24. Calculate the monitoring cycle within, no. mean amplitude of the branch waveform Its expression is as follows: S25. Calculate the monitoring cycle based on S21-S24. within, no. Similarity index between branch waveform and fault reference waveform This is used to measure the similarity between two vibration waveforms, and its expression is as follows: In the formula, Indicates the monitoring period within, no. The covariance between the branch waveform and the fault reference waveform. Indicates the monitoring period within, no. The standard deviation of the waveform of each branch line, Indicates the monitoring period Within, the standard deviation of the fault reference waveform; Specifically, based on the Pearson correlation coefficient formula, the similarity index... Restricted to Within the range of 1 to 1, this provides a unified standard for judging the similarity of different monitoring sections and branches, which can effectively avoid misjudgment caused by differences in data volume, thereby improving the reliability of system fault identification. At the same time, the closer the value of this index is to 1, the more consistent the vibration trend and amplitude change law of the branch waveform with the fault reference waveform is. It can objectively and accurately reflect whether the branch meets the waveform characteristics of grounding fault, avoiding the subjective error of traditional qualitative judgment. The following is the experimental data for the similarity index, as shown in Table 2: Table 2: Experimental Data for the Similarity Index In the similarity index experimental data in Table 2, transmission line monitoring section B was selected as the experimental target. Monitoring section B is equipped with branch line a, branch line b and branch line c. The location management module has a fixed similarity threshold. This is used to quickly determine whether the abrupt change in the grounding fault of each monitoring section of a transmission line exceeds the standard. The numerical value directly affects the system's matching accuracy and discrimination accuracy of branch fault characteristics. An excessively high similarity threshold... This can lead to an overly stringent system for identifying differences in fault characteristics between branches. Even if a branch exhibits a clear fault signal, it may fail to be accurately identified due to insufficient feature matching, resulting in delayed fault branch identification and delayed fault isolation and repair. Conversely, excessively stringent feature matching can reduce the rigor of the feature matching, misjudging the normal operating fluctuations of non-faulty branches as similar to fault characteristics, leading to misidentification of faulty branches, unnecessary maintenance scheduling and line outages, and increased operating costs and safety risks. Therefore, the optimal value of this parameter needs to be determined through calibration experiments of the following system: similarity threshold. The calibration method is as follows: A multi-branch transmission network simulation environment was built using a line simulation testing system to simulate different fault scenarios under single fault types, such as short-circuit faults and ground faults. Different fault severity levels, fault occurrence times, and fault characteristic parameters, such as fault current waveforms and voltage attenuation patterns, were set. Multiple sets of experiments were conducted, recording the fault characteristic sequence data of each branch and the system's preliminary judgment results on the faulty branch in each set of experiments. Next, complex scenarios involving compound fault types, such as short-circuit faults superimposed with equipment insulation damage and ground faults accompanied by line icing, were simulated. The fault combination mode, the influence of branch range, and the coupling degree of characteristic parameters were adjusted, and multiple sets of experiments were conducted to record the variation patterns of fault characteristics in each branch and whether the system accurately located the faulty branch. For each candidate threshold, the collected fault characteristic sequence data of each branch were used as input. The number of times a real faulty branch was not identified due to improper threshold setting was counted as a missed detection, and the number of times a non-faulty branch was misidentified as a faulty line was counted as a misidentification. Finally, the value that minimizes both the missed detection rate and the misidentification rate while meeting the faulty branch identification response efficiency requirements was selected as the similarity threshold. The preferred value; The error assessment unit evaluates the response error level of each monitoring segment of the transmission line based on the segmented dataset and the synchronous dataset, and generates a corresponding error index. The calculation process is as follows: S31. Based on the segmented dataset, the line length of each monitoring segment of the transmission line is recorded as follows: , This represents the total number of monitoring segments. Then, based on the synchronized dataset, the response timestamp for each monitoring segment of the transmission line is recorded as... The speed of optical fiber signal propagation is denoted as ; S32. Calculate the average value of the length difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The length of the line in each monitoring section Indicates the transmission line number The length of the line in the monitoring segment, and the length of the monitoring segment. The monitoring segment and the first The monitoring segments are adjacent; S33. Calculate the average response time difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The response timestamp of each monitoring segment Indicates the transmission line number The response timestamp of each monitoring segment; S34. Based on S31-S33, calculate the error index of the transmission line. Its expression is as follows: In the formula, This represents the response time difference between adjacent monitoring segments under ideal conditions; The location management module has a fixed error threshold. Error index ≥ Error Threshold When this occurs, it indicates that the response error of the transmission line has exceeded the standard. Triggering measures include notifying management personnel to correct the signal response timestamp in a timely manner and initiating a self-test procedure.
[0020] In this embodiment, the multi-dimensional acquisition module acquires management data of all monitoring sections of the transmission line, management data of transmission signals, and management data of synchronization response timestamps, and classifies them into datasets to provide complete data support for subsequent fault assessment, line selection analysis, and error judgment. The intelligent assessment module is set with a fixed monitoring period. Assess the degree of abrupt change in ground faults in each monitoring section of the transmission line and generate the corresponding abrupt change index. To avoid misjudgments caused by a single signal and improve the accuracy of fault identification, the fault severity of each branch within the monitoring section is analyzed to generate a corresponding similarity index. The fault identification accuracy is high. The intelligent assessment module evaluates the response error of each monitoring section of the transmission line and generates a corresponding error index. This provides a basis for system calibration and ensures the reliability of overall monitoring and judgment. The positioning management module is based on preset thresholds and linked with three major indices to trigger precise operation and maintenance measures for different exceedance situations, so as to realize rapid identification, accurate line selection and full-process control of grounding faults in long-distance transmission lines, and effectively shorten the fault handling cycle.
[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.
[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.
Claims
1. A grounding fault location system for long-distance transmission lines based on fiber optic sensor networks, characterized in that: It includes a multi-dimensional data acquisition module, an intelligent evaluation module, and a location management module; The multi-dimensional acquisition module acquires management data of all monitoring sections of the transmission line, management data of transmission signals, and management data of synchronization response timestamps by connecting fiber optic links and distributed sensing devices, and classifies them into segmented datasets, signal datasets, and synchronization datasets. The intelligent evaluation module includes a fault evaluation unit, a route selection analysis unit, and an error evaluation unit. The fault evaluation unit is set with a fixed monitoring period. By combining segmented datasets and signal datasets, the abrupt change in ground faults of each monitoring segment of the transmission line is evaluated, and a corresponding abrupt change index is generated. The route selection analysis unit analyzes the fault degree of each branch line within the monitoring segment based on the segmented dataset and the signal dataset, and generates a corresponding similarity index. The error assessment unit evaluates the response error level of each monitoring segment of the transmission line based on the segmented dataset and the synchronous dataset, and generates a corresponding error index. ; The location management module is set with a fixed mutation threshold value. Similarity threshold and error threshold Combined with the mutation index Similarity Index Sum of error index This triggers corresponding operation and maintenance measures.
2. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 1, characterized in that: The segmented dataset includes the reference zero-sequence current value, reference current phase angle, reference line temperature, reference line humidity, reference arc sound frequency, number of branches, vibration amplitude of fault reference waveform, number of waveform sampling points, line length, and total number for each monitoring segment of the transmission line.
3. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 2, characterized in that: The signal dataset includes the zero-sequence current value, current phase angle, line temperature, line humidity, arc sound frequency, and vibration amplitude of branch waveforms for each monitoring section of the transmission line.
4. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 3, characterized in that: The synchronization dataset contains the response timestamps for each monitoring segment of the transmission line and the propagation speed of the fiber optic signal.
5. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 4, characterized in that: The mutation index The calculation process is as follows: S11. Based on the segmented dataset, extract the first segment of the transmission line. The management data of the first monitoring segment, and the first The reference zero-sequence current value of each monitoring segment is denoted as: , will the The reference current phase angle of each monitoring segment is denoted as... , will the The reference line temperature for each monitoring section is recorded as follows: , will the The reference line humidity for each monitoring section is recorded as follows: , will the The reference arc frequency of each monitoring segment is denoted as ; S12. Based on the signal dataset, extract the monitoring period. within, no. The power transmission signal management data of the first monitoring section, and the first The zero-sequence current value of each monitoring segment is denoted as: , Indicates the monitoring period The total number of timestamps recorded within the record, will be the first The current phase angle of each monitoring segment is denoted as... , will the The line temperature of each monitoring section is recorded as follows: , will the The line humidity of each monitoring section is recorded as follows: , will the The arc sound frequency of each monitoring segment is recorded as follows: ; S13, Calculate the monitoring cycle within, no. The cumulative absolute deviation of the current phase angle of each monitoring segment Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Current phase angle at each timestamp ; S14. Calculate the monitoring cycle within, no. Cumulative absolute deviation of line temperature in each monitoring section Its expression is as follows: In the formula, Indicates the first The monitoring segment in the first Line temperature at each timestamp ; S15. Calculate the monitoring cycle based on S11-S14. within, no. mutation index of each monitoring segment Its expression is as follows: In the formula, Indicates the first The peak value of zero-sequence current in each monitoring segment Indicates the first The maximum line humidity of each monitoring section, Indicates the first The peak value of the electric arc sound frequency in each monitoring segment , , , and All are weighting coefficients, and .
6. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 5, characterized in that: The similarity index The calculation process is as follows: S21. Based on the segmented dataset, divide the first... The branch lines of each monitoring segment are denoted as , Indicates the first The total number of branches in each monitoring segment, and the monitoring cycle within, no. The vibration amplitude of the fault reference waveform for each monitoring segment is denoted as: , Indicates the number of waveform sampling points; S22, Calculate the monitoring cycle within, no. Average vibration amplitude of fault reference waveform in each monitoring segment Its expression is as follows: S23. Based on the signal dataset, the monitoring period... within, no. The first monitoring segment The vibration amplitude of the branch waveform is denoted as , ; S24. Calculate the monitoring cycle within, no. mean amplitude of the branch waveform Its expression is as follows: S25. Calculate the monitoring cycle based on S21-S24. within, no. Similarity index between branch waveform and fault reference waveform Its expression is as follows: In the formula, Indicates the monitoring period within, no. The covariance between the branch waveform and the fault reference waveform. Indicates the monitoring period within, no. The standard deviation of the waveform of each branch line, Indicates the monitoring period Within, the standard deviation of the fault reference waveform.
7. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 6, characterized in that: The error index The calculation process is as follows: S31. Based on the segmented dataset, the line length of each monitoring segment of the transmission line is recorded as follows: , This represents the total number of monitoring segments. Then, based on the synchronized dataset, the response timestamp for each monitoring segment of the transmission line is recorded as... The speed of optical fiber signal propagation is denoted as ; S32. Calculate the average value of the length difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The length of the line in each monitoring section Indicates the transmission line number The length of the line in the monitoring segment, and the length of the monitoring segment. The monitoring segment and the first The monitoring segments are adjacent; S33. Calculate the average response time difference between adjacent monitoring sections of the transmission line. Its expression is as follows: In the formula, Indicates the transmission line number The response timestamp of each monitoring segment Indicates the transmission line number The response timestamp of each monitoring segment; S34. Based on S31-S33, calculate the error index of the transmission line. Its expression is as follows: In the formula, This represents the response time difference between adjacent monitoring segments under ideal conditions.
8. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 7, characterized in that: The mutation index ≥ Mutation threshold When this occurs, it indicates that the sudden change in the ground fault of the corresponding monitoring section has exceeded the standard. Triggering measures include activating the line distance protection and conducting an emergency inspection of the transmission line of the corresponding monitoring section.
9. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 8, characterized in that: The similarity index ≥ Similarity threshold When this occurs, it indicates that the corresponding branch line is faulty. Triggering measures include suspending the load transmission of the corresponding branch line and promptly notifying management personnel to conduct key investigations.
10. The grounding fault location system for long-distance transmission lines based on fiber optic sensor networks according to claim 9, characterized in that: The error index ≥ Error Threshold When this occurs, it indicates that the response error of the transmission line has exceeded the standard. Triggering measures include notifying management personnel to correct the signal response timestamp in a timely manner and initiating a self-test procedure.