A method and system for fault location in hybrid circuits

Abstract

The application provides a fault positioning method and system for a hybrid line, and the fault positioning method comprises the following steps: acquiring environmental data around each section in real time; controlling a standard traveling wave generating device to trigger at a first fixed frequency or when the environmental data exceeds a set threshold, so as to update the traveling wave speed in real time; establishing a speed prediction model according to the environmental data and the traveling wave speed; when a first traveling wave detecting device detects a fault traveling wave, calling the time nodes at which all the first traveling wave detecting devices detect the fault traveling wave in a current period, so as to determine the detection interval of the fault traveling wave; and obtaining the fault position by calculation. The technical problem solved by the application is that the wave speed is significantly affected by environmental factors in actual operation, the existing fault positioning method does not consider dynamic environmental interference, lacks a real-time wave speed calibration mechanism, is still prone to misjudgment when facing sudden weather changes, and finally leads to a positioning error of more than one kilometer, which cannot meet the high-precision operation and maintenance requirements of the power grid.

Description

Technical Field

[0001] This invention relates to the field of technology, and more specifically, to a fault location method and system for hybrid circuits. Background Technology

[0002] With the increasing complexity of power systems, the large-scale application of hybrid lines has become an inevitable trend. These lines present significant challenges in fault location due to their combination of characteristics, such as the susceptibility to environmental interference found in overhead lines and the complex impedance distribution of cable lines. Traditional traveling wave fault location methods primarily rely on fixed wave velocity models to pinpoint fault locations.

[0003] However, the relevant technologies have at least one of the following problems: in actual operation, wave speed is significantly affected by environmental factors. The fault location methods in the existing technologies do not consider dynamic environmental interference and lack a real-time wave speed calibration mechanism. They are still prone to misjudgment when faced with sudden weather changes, which ultimately leads to positioning errors exceeding the kilometer level and cannot meet the high-precision operation and maintenance requirements of the power grid. Summary of the Invention

[0004] The technical problem solved by this invention is that wave velocity is significantly affected by environmental factors during actual operation. Existing fault location methods do not consider dynamic environmental interference and lack a real-time wave velocity calibration mechanism. They are still prone to misjudgment when faced with sudden weather changes, ultimately leading to location errors exceeding the kilometer level, which cannot meet the high-precision operation and maintenance requirements of the power grid.

[0005] To address the aforementioned problems, this invention provides a fault location method for hybrid lines, which include both overhead and cable lines. The hybrid lines are divided into multiple detection zones based on their different types, and each detection zone is further divided into multiple equidistant segments. Standard traveling wave generators are installed at both ends of each segment, and first traveling wave detection devices are installed at both ends of each detection zone. The fault location method includes: acquiring environmental data around each segment in real time; controlling the standard traveling wave generators to trigger at a first fixed frequency or when the environmental data exceeds a set threshold to update the traveling wave rate in real time; establishing a rate prediction model based on the environmental data and the traveling wave rate; when the first traveling wave detection device detects a fault traveling wave, retrieving all time points in the current time period where the first traveling wave detection device detected the fault traveling wave to determine the detection zone where the fault traveling wave was generated; predicting the rate of the fault traveling wave based on the rate prediction model, and calculating the fault location.

[0006] Compared with existing technologies, the technical effects achieved by this solution are as follows: by dividing the hybrid line into multiple detection zones and collecting environmental data in real time, and by combining a standard traveling wave device to update the traveling wave rate under different triggering conditions, a rate prediction model that adapts to environmental changes is dynamically established. This solves the problem of unstable wave velocity in overhead and cable lines due to differences in temperature, humidity or weather, ensures real-time calibration of wave velocity parameters during fault location, and significantly improves the location accuracy and reliability in complex environments.

[0007] In one embodiment of the present invention, retrieving the time nodes when all first traveling wave detection devices detected fault traveling waves within the current time period to determine the detection interval where the fault traveling wave occurred includes: arranging the acquired time nodes in chronological order, and marking the first traveling wave detection device corresponding to the first sequential time node as the first node; marking the first traveling wave detection devices adjacent to the first node as the nth node in chronological order, where n = {2, 3, 4, ..., n}; determining whether the location of the fault traveling wave is at the endpoint of the first node; if yes, determining that a fault has occurred on the line at the location of the first node; if no, calculating the predicted passage time of the fault traveling wave through the nth node and the first node using a rate prediction model, and comparing it with the difference between the time nodes of the corresponding lines to determine the line where the fault is located.

[0008] Compared with existing technologies, the technical effects achieved by this solution are as follows: by sorting the time nodes of the fault traveling wave and marking the logical relationship between adjacent detection devices, it can distinguish whether the fault occurs at the node endpoint or on the line. By combining the comparison between the predicted passage time and the actual difference, it can effectively suppress the misjudgment of the interval caused by sudden changes in local environment, such as a sudden drop in wave velocity due to a local lightning strike, and optimize the positioning efficiency of the fault detection interval.

[0009] In one embodiment of the present invention, the predicted passage time of the fault traveling wave between the nth node and the first node is calculated using a rate prediction model, and compared with the difference between the time nodes of the corresponding lines to determine the line where the fault is located. This includes: defining the difference between the time node of the nth node and the time node of the first node in the current time period as the k-th difference, where k=n; obtaining the line length Ln between the nth node and the first node; obtaining the rate Vn of the fault traveling wave between the nth node and the first node according to the rate prediction model, and calculating the passage time Tn using the formula, where the formula is Tn=Ln / Vn; establishing an error model; comparing the magnitude Δt of the k-th difference with Tn, and correcting the location result using the error model.

[0010] Compared with existing technologies, the technical effects achieved by this solution are as follows: by introducing line length and dynamic wave speed calculation prediction time based on environmental data, and combining error model to correct the difference results, such as adjusting wave speed error rate and deviation tolerance coefficient, the cumulative deviation caused by environmental fluctuations is specifically compensated, thereby improving the consistency of fault location across sections.

[0011] In one embodiment of the present invention, the magnitude of the k-th difference Δt is compared with Tn, and the location result is corrected by an error model, including: wave velocity error rate ε, which is dynamically adjusted according to the line type and real-time environmental data; deviation tolerance coefficient λ, which is used to construct an error buffer; the error model performs hierarchical judgment: first layer: when Δt>Tn×(1+ε×λ), the fault is directly determined to be located between the first node and the nth node; second layer: when Tn<Δt≤Tn×(1+ε×λ), the standard traveling wave generator is triggered to calibrate the wave velocity; third layer: when Δt≤Tn, the fault location is recalculated after correcting the equivalent length of the line in combination with environmental data.

[0012] Compared with existing technologies, the technical effects achieved by this solution are as follows: Through a hierarchical error model, wave velocity calibration or equivalent line length correction is automatically triggered when the environment changes abruptly. For example, in scenarios where the wave velocity of overhead lines is significantly reduced in rainy weather, the error buffer is adaptively expanded to avoid misjudgment caused by a single threshold and enhance the robustness of the algorithm in extreme environments.

[0013] In one embodiment of the present invention, a second traveling wave detection device is provided at both ends of any segment. The fault location method includes: when the second traveling wave detection device detects a fault traveling wave, the time is recorded as the detection time; the detection times of all second traveling wave detection devices detecting fault traveling waves within the current time period are retrieved to determine the segment in which the fault traveling wave is generated; the rate of the fault traveling wave is predicted according to the rate prediction model, and the fault location is obtained by calculation.

[0014] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by adding a second traveling wave detection device at both ends of the section and collecting higher resolution detection time data, and by combining the section-level environmental parameters to optimize the wave velocity prediction model, the impact of environmental noise such as strong winds interfering with the propagation of overhead line traveling waves on short section positioning is reduced, thereby improving the sensitivity and accuracy of local fault point capture.

[0015] In one embodiment of the present invention, retrieving the detection times of all second traveling wave detection devices that detected the fault traveling wave within the current time period to determine the segment where the fault traveling wave occurred includes: arranging the acquired detection times in chronological order, acquiring the first two detection times, and designating the second traveling wave detection device corresponding to the first detection time as the first detection position; designating the second traveling wave detection device corresponding to the second detection time as the second detection position; designating the second traveling wave detection device on the side of the first detection position furthest from the second detection position as the third detection position; determining whether the location where the fault traveling wave occurred is at the endpoint of the first detection position; if so, determining that a line fault has occurred at the location of the first detection position; if not, determining whether each segment within the detection interval within the current time period is under the same weather conditions based on environmental data.

[0016] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: By judging the weather consistency within the same detection range through environmental data, it can distinguish between uniform and non-uniform environmental conditions. For example, when icing in a local section of a mixed line causes abnormal wave velocity, it can dynamically switch the positioning criteria and fuse time data from multiple detection positions to avoid wave velocity model conflicts caused by environmental differences across sections and reduce the ambiguity range of the fault section.

[0017] In one embodiment of the present invention, the determination of whether each segment within the detection interval during the current time period is under the same weather conditions is made based on environmental data, including: if yes, the fault location is determined to be between the first detection position and the second detection position, and the specific fault location is obtained by calculation; if no, the fault location is further determined by calculation based on the detection time of the fault traveling wave measured by the first detection position, the second detection position, and the third detection position and the length of the corresponding segment.

[0018] Compared with existing technologies, the technical effects achieved by this solution are as follows: For inconsistent weather conditions, the wave velocity weights of different sections are dynamically adjusted based on the relationship between the time difference of the first, second, and third detection positions and the section length. For example, historical rainy day wave velocity parameters are used in thunderstorm sections to solve the problem of contradictory traveling wave propagation speeds caused by sudden changes in local environment, such as water accumulation in cable sections and dryness in overhead sections, and to accurately locate cross-environmental fault points.

[0019] In one embodiment of the present invention, the fault location is further determined by calculation based on the detection time of the fault traveling wave detected by the first detection position, the second detection position, and the third detection position, and the length of the corresponding segment. This includes: defining the line within the first detection position and the second detection position as the first detection segment; defining the line within the first detection position and the third detection position as the second detection segment; obtaining the line length of the first detection segment as the second length; obtaining the second wave velocity within the first detection segment in the current time period according to the rate prediction model; obtaining the time for the fault traveling wave to pass through the first detection segment as the second time according to the calculation; determining whether the difference between the detection time of the first detection position and the second detection position is less than the second time; if so, determining that the fault location is within the first detection segment, and obtaining the specific fault location through calculation; if not, determining that the fault location is within the second detection segment, and obtaining the specific fault location through calculation.

[0020] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by defining segmented detection segments and matching segment lengths with dynamic wave velocity parameters, and combining environmental data to compensate for long-term environmental changes, the accuracy of the calculation model when the fault point is located in different detection segments is ensured, and the positioning offset of long-distance lines is avoided.

[0021] In one embodiment of the present invention, the fault location method includes establishing an attenuation characteristic model of a traveling wave signal, including amplitude attenuation and frequency variation; and correcting the distance between the fault point and the branch node according to the attenuation characteristic model to improve the accuracy of fault location.

[0022] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by establishing a model of the attenuation characteristics of traveling wave signals, such as the attenuation of high-frequency components caused by increased humidity, and by combining environmental data to correct the waveform characteristics of traveling wave propagation, the equivalent wave velocity measurement error caused by environmental interference, such as signal distortion in rain and fog, is compensated, thereby improving the positioning reliability of branch nodes and complex terrain areas.

[0023] On the other hand, the present invention also provides a fault location system, which can apply the fault location method in any of the above examples. The fault location system includes: a weather monitoring device for real-time acquisition of environmental data of any segment; a time series system for retrieving and arranging the times of detected fault traveling waves; a historical database for collecting weather conditions and corresponding traveling wave velocities in different historical periods; a control system for controlling a standard traveling wave generator to emit standard traveling waves; a fault location system for obtaining the fault location through judgment and calculation, and visually displaying and recording the fault location results; and a communication module for transmitting data to the fault location system.

[0024] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: it can achieve the technical effects corresponding to any of the above examples, which will not be elaborated here.

[0025] By adopting the technical solution of the present invention, the following technical effects can be achieved:

[0026] (1) By dividing the mixed line into multiple detection intervals, environmental data is collected in real time and the standard traveling wave device is triggered to update the wave velocity. Combined with the rate prediction model, the traveling wave velocity changes caused by environmental fluctuations such as temperature, humidity, and weather are dynamically corrected, solving the problem of unstable wave velocity of overhead lines and cable lines in complex environments, and significantly improving the real-time performance and accuracy of fault location.

[0027] (2) Based on the multi-level data collaboration of the first and second traveling wave detection devices, through time series analysis, hierarchical error model and non-uniform weather condition criteria, the cumulative error caused by local environmental changes (such as lightning strikes and icing) or long-term gradual changes (such as day and night temperature difference) is compensated in different scenarios. Combined with section-level fine detection to suppress noise interference, the positioning robustness across line types and weather conditions is achieved.

[0028] (3) Integrate traveling wave attenuation characteristic model, historical database and real-time environmental monitoring data, and solve the signal distortion problem caused by branch nodes, terrain differences and multi-segment environmental coupling (such as cable ground temperature and overhead wind speed) in mixed lines by signal waveform analysis (such as amplitude and frequency attenuation) and dynamic correction of equivalent line length, and build a high-precision positioning system with full-dimensional environmental perception. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating the process of establishing a rate prediction model, provided as an embodiment of the present invention;

[0031] Figure 2 A flowchart for locating the detection range where a fault is located, provided as an embodiment of the present invention;

[0032] Figure 3 A flowchart for locating the section where a fault is located, provided as an embodiment of the present invention. Detailed Implementation

[0033] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0034] Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application. It should be noted that in the description of this application, terms such as "first" and "second" are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0035] like Figures 1 to 3 As shown in the provided flowchart, this invention provides a fault location method for hybrid lines, which include both overhead and cable lines. The hybrid lines are divided into multiple detection zones based on their different types, and each detection zone is further divided into multiple equidistant segments. Standard traveling wave generators are installed at both ends of each segment, and first traveling wave detection devices are installed at both ends of each detection zone. The fault location method includes: acquiring environmental data around each segment in real time; controlling the standard traveling wave generators to trigger at a first fixed frequency or when the environmental data exceeds a set threshold to update the traveling wave rate in real time; establishing a rate prediction model based on the environmental data and the traveling wave rate; when the first traveling wave detection device detects a fault traveling wave, retrieving all time points in the current time period where the first traveling wave detection device detected the fault traveling wave to determine the detection zone where the fault traveling wave was generated; predicting the rate of the fault traveling wave based on the rate prediction model, and calculating the fault location.

[0036] Specifically, because overhead lines and cable lines in mixed circuits have different characteristics of traveling waves and are affected by the environment, mixed circuits are divided into different detection zones according to different types.

[0037] Furthermore, real-time acquisition of environmental data around each section is accomplished through a network of environmental sensors distributed along the route. These sensors continuously monitor parameters such as temperature, humidity, and air pressure, and transmit the data to the central processing system in real time.

[0038] Furthermore, the velocity prediction model refers to a prediction algorithm based on environmental data and historical traveling wave velocities, which can be implemented using machine learning or statistical regression methods. This model can predict the traveling wave velocity based on current environmental data. Model training and updating is an ongoing process; as new data accumulates, the prediction accuracy will continuously improve.

[0039] Furthermore, a standard traveling wave generator refers to a device capable of generating a standard traveling wave signal, which can be implemented using a pulse generator or a signal injection device.

[0040] Furthermore, the standard traveling wave generator has two triggering mechanisms: one is triggering at a fixed frequency once per hour or every half hour, which can be adjusted according to the actual situation; the other is triggering when environmental data exceeds a preset threshold, such as temperature exceeding a set threshold or humidity exceeding a certain threshold. After triggering, the standard traveling wave generator produces a traveling wave signal with known characteristics. After the first traveling wave detection device detects these standard traveling wave signals, it records the precise arrival time of the signal. By calculating the propagation time of the standard traveling wave over a known distance, the system can update the traveling wave rate in the current environment in real time.

[0041] Furthermore, when an actual fault occurs, the first traveling wave detection device will detect the traveling wave signal generated by the fault. The system retrieves the arrival times of the fault traveling waves recorded by all detection devices, and determines the detection interval where the fault occurred by analyzing the time sequence and time difference.

[0042] Furthermore, the system utilizes a velocity prediction model to predict the propagation velocity of the fault traveling wave in the relevant section based on current environmental data. Combining the arrival time of the fault traveling wave and the predicted propagation velocity, the precise location of the fault is calculated. This dynamically adjusted method can adapt to complex and changing environmental conditions, significantly improving the accuracy of fault location. Especially when facing sudden weather changes, traditional fixed wave velocity models may produce large errors, while this method, through real-time updates and predictions of wave velocity, can maintain high-precision location.

[0043] In one embodiment of the present invention, retrieving the time nodes when all first traveling wave detection devices detected fault traveling waves within the current time period to determine the detection interval where the fault traveling wave occurred includes: arranging the acquired time nodes in chronological order, and marking the first traveling wave detection device corresponding to the first sequential time node as the first node; marking the first traveling wave detection devices adjacent to the first node as the nth node in chronological order, where n = {2, 3, 4, ..., n}; determining whether the location of the fault traveling wave is at the endpoint of the first node; if yes, determining that a fault has occurred on the line at the location of the first node; if no, calculating the predicted passage time of the fault traveling wave through the nth node and the first node using a rate prediction model, and comparing it with the difference between the time nodes of the corresponding lines to determine the line where the fault is located.

[0044] Specifically, since the wiring methods of mixed lines include not only single linear sections, but also complex connection methods such as T-type, loop type, and cross type, the segmentation of mixed lines is further refined according to the location where the line branches, so as to distinguish the detection range of each different branch.

[0045] Furthermore, since the fault location cannot be directly determined, the time nodes of the fault traveling waves detected by all the first traveling wave detection devices are retrieved and arranged in chronological order from early to late. The earliest time node is the first node to be determined. Then, the specific analysis and calculation are carried out to determine which detection interval the fault location is located in connected to the first node.

[0046] Furthermore, it is determined whether the location of the fault traveling wave is at the endpoint of the first node. This determination is achieved by comparing the time difference between the first node and its adjacent nodes. If the time difference is less than a preset threshold, the fault can be considered to have occurred at the endpoint of the first node. If the fault occurs at the endpoint of the first node, it is directly determined that a line fault has occurred at that location. This rapid determination mechanism can significantly improve the location efficiency, especially for faults occurring at the endpoints of the line.

[0047] Furthermore, if the fault is not at the first node endpoint, the predicted travel time of the fault-traveling wave through the nth node and the first node is calculated using a rate prediction model. The rate prediction model can be based on machine learning algorithms, such as support vector regression or neural networks, and dynamically adjusts the traveling wave rate by considering real-time environmental data (such as temperature, humidity, and wind speed). This dynamic adjustment mechanism significantly improves the accuracy of fault location, especially in the face of sudden weather changes.

[0048] Furthermore, the predicted transit time is compared with the difference between the actual time points on the corresponding lines to determine the line where the fault is located. This step can be achieved by setting an error tolerance range; if the actual time difference is within the error tolerance range of the predicted time, the fault is considered to have occurred on that line segment.

[0049] In other words, the solution presented in this application, through a real-time updated rate prediction model, can adapt to the impact of environmental changes on the traveling wave rate, overcoming the limitations of fixed wave rate models. Utilizing multi-point time node comparison and prediction models, it significantly improves the accuracy of fault location, keeping errors within a smaller range. The rapid fault identification mechanism for endpoint faults greatly shortens fault location time in certain situations. This solution is applicable to both overhead and cable lines, effectively handling complex situations in mixed lines.

[0050] In one embodiment of the present invention, the predicted passage time of the fault traveling wave between the nth node and the first node is calculated using a rate prediction model, and compared with the difference between the time nodes of the corresponding lines to determine the line where the fault is located. This includes: defining the difference between the time node of the nth node and the time node of the first node in the current time period as the k-th difference, where k=n; obtaining the line length Ln between the nth node and the first node; obtaining the rate Vn of the fault traveling wave between the nth node and the first node according to the rate prediction model, and calculating the passage time Tn using the formula, where the formula is Tn=Ln / Vn; establishing an error model; comparing the magnitude Δt of the k-th difference with Tn, and correcting the location result using the error model.

[0051] Preferably, during the comparison and correction process, a dynamic threshold can be set, which can be adaptively adjusted according to line characteristics and environmental conditions. For example, under severe weather conditions, the error tolerance can be appropriately relaxed. In addition, an iterative optimization algorithm can be introduced to gradually approach the actual fault location through multiple comparisons and corrections.

[0052] In one embodiment of the present invention, the magnitude of the k-th difference Δt is compared with Tn, and the location result is corrected by an error model. The error model includes: a wave velocity error rate ε, which is dynamically adjusted according to the line type and real-time environmental data; a deviation tolerance coefficient λ, used to construct an error buffer; the error model performs hierarchical judgment: first layer: when Δt>Tn×(1+ε×λ), the fault is directly determined to be located between the first node and the nth node; second layer: when Tn<Δt≤Tn×(1+ε×λ), the standard traveling wave generator is triggered to calibrate the wave velocity; third layer: when Δt≤Tn, the fault location is recalculated after correcting the equivalent length of the line in combination with environmental data.

[0053] Specifically, the error model in this application effectively improves the accuracy of fault location in hybrid lines by introducing multiple key parameters and a hierarchical judgment strategy. Specifically, the wave velocity error rate ε can be dynamically adjusted according to the line type and real-time environmental data; for example, it can be set to 1% for overhead line sections and 0.5% for cable line sections. The deviation tolerance coefficient λ is used to construct the error buffer zone and can be set to a value between 1.2 and 1.5.

[0054] Preferably, the error model also includes a synchronization time deviation threshold, which can be set according to the upper limit of the time synchronization accuracy of adjacent detection devices, for example, it can be set to 10 microseconds.

[0055] The introduction of these parameters allows the error model to adapt more flexibly to different line conditions and environmental factors. For example, under severe weather conditions, the wave velocity error rate ε can be appropriately increased to cope with possible changes in wave velocity. At the same time, the introduction of the deviation tolerance coefficient λ provides a certain buffer space for error judgment, avoiding misjudgments caused by small errors.

[0056] Furthermore, when Tn < Δt ≤ Tn × (1 + ε × λ), a standard traveling wave generator is triggered to perform wave velocity calibration. This step updates the wave velocity value in real time, improving the accuracy of subsequent positioning. The standard traveling wave generator can be triggered every 5 minutes within this interval to obtain the latest wave velocity data.

[0057] Furthermore, when Δt ≤ Tn, the equivalent length of the line needs to be corrected based on environmental data, and then the fault location needs to be recalculated. This step takes into account the impact of environmental factors on line characteristics, further improving positioning accuracy. For example, in hot weather, the equivalent length of the line may need to be increased by 1%-2% to compensate for thermal expansion effects.

[0058] In one embodiment of the present invention, a second traveling wave detection device is provided at both ends of any segment. The fault location method includes: when the second traveling wave detection device detects a fault traveling wave, the time is recorded as the detection time; the detection times of all second traveling wave detection devices detecting fault traveling waves within the current time period are retrieved to determine the segment in which the fault traveling wave is generated; the rate of the fault traveling wave is predicted according to the rate prediction model, and the fault location is obtained by calculation.

[0059] Specifically, retrieve the detection times of all second traveling wave detection devices within the current time period. By comparing the times of multiple detection points, the specific segment where the fault occurred can be determined. This step can be achieved by setting a time window, for example, retrieving all detection times within 10 milliseconds after the fault occurred, to filter out possible interference signals.

[0060] Furthermore, the fault location is obtained through calculation. By combining the traveling wave velocity and the detection time, the fault location can be accurately calculated. The calculation method can employ a dual-end positioning algorithm, which uses the time difference between the second traveling wave detection devices on both sides of the fault point and the predicted traveling wave velocity to determine the distance to the fault point.

[0061] In one embodiment of the present invention, retrieving the detection times of all second traveling wave detection devices that detected the fault traveling wave within the current time period to determine the segment where the fault traveling wave occurred includes: arranging the acquired detection times in chronological order, acquiring the first two detection times, and designating the second traveling wave detection device corresponding to the first detection time as the first detection position; designating the second traveling wave detection device corresponding to the second detection time as the second detection position; designating the second traveling wave detection device on the side of the first detection position furthest from the second detection position as the third detection position; determining whether the location where the fault traveling wave occurred is at the endpoint of the first detection position; if so, determining that a line fault has occurred at the location of the first detection position; if not, determining whether each segment within the detection interval within the current time period is under the same weather conditions based on environmental data.

[0062] Specifically, the acquired detection times are arranged in chronological order. This step can be achieved using a timing system, which can accurately record and sort the time when each second traveling wave detection device detects the fault traveling wave. After sorting, the first two detection times are selected, corresponding to the first and second detection positions, respectively. This selection method ensures that the two detection devices that detected the fault traveling wave earliest are selected, improving the accuracy of subsequent positioning.

[0063] Furthermore, if the two detection times are equal, the fault location is between the first detection position and the second detection position.

[0064] Furthermore, this application introduces the concept of a third detection position, which is a second traveling wave detection device located on the side of the first detection position furthest from the second detection position. The purpose of introducing the third detection position is to provide additional reference points for subsequent, more accurate positioning. For example, when a fault occurs between the first and second detection positions, the data from the third detection position can be used to verify the positioning results or provide additional calculation basis.

[0065] Furthermore, it is determined whether the fault traveling wave occurs at the endpoint of the first detection bit. This determination can be achieved by comparing the detection time difference between the first and second detection bits with a preset threshold. If the time difference is less than the preset threshold, it may indicate that the fault occurs at the endpoint of the first detection bit. This rapid determination method can effectively handle the special case of endpoint faults and improve positioning efficiency.

[0066] Furthermore, when the judgment result indicates that the fault is not at the first detection endpoint, this application innovatively introduces the consideration of environmental data. By determining whether each segment within the detection interval is under the same weather conditions during the current time period, a more accurate parameter basis is provided for subsequent location calculations. This step can be achieved through real-time acquired environmental data and pre-established environmental data models. For example, environmental parameters such as temperature, humidity, and wind speed of each segment can be obtained using a distributed weather station network, and data analysis can be used to determine whether they are under the same weather conditions.

[0067] In one embodiment of the present invention, the determination of whether each segment within the detection interval during the current time period is under the same weather conditions is made based on environmental data, including: if yes, the fault location is determined to be between the first detection position and the second detection position, and the specific fault location is obtained by calculation; if no, the fault location is further determined by calculation based on the detection time of the fault traveling wave measured by the first detection position, the second detection position, and the third detection position and the length of the corresponding segment.

[0068] Specifically, environmental data assessment involves determining whether different sections within the detection range are experiencing the same weather conditions based on real-time acquired environmental data. This can be achieved by comparing meteorological parameters such as temperature, humidity, and wind speed across different sections. A threshold range is set, such as a temperature difference not exceeding 5°C, a humidity difference not exceeding 10%, and a wind speed difference not exceeding 3 m / s, to determine whether they are experiencing the same weather conditions.

[0069] In one embodiment of the present invention, the fault location is further determined by calculation based on the detection time of the fault traveling wave detected by the first detection position, the second detection position, and the third detection position, and the length of the corresponding segment. This includes: defining the line within the first detection position and the second detection position as the first detection segment; defining the line within the first detection position and the third detection position as the second detection segment; obtaining the line length of the first detection segment as the second length; obtaining the second wave velocity within the first detection segment in the current time period according to the rate prediction model; obtaining the time for the fault traveling wave to pass through the first detection segment as the second time according to the calculation; determining whether the difference between the detection time of the first detection position and the second detection position is less than the second time; if so, determining that the fault location is within the first detection segment, and obtaining the specific fault location through calculation; if not, determining that the fault location is within the second detection segment, and obtaining the specific fault location through calculation.

[0070] Specifically, the theoretical time for the fault traveling wave to pass through the first detection segment is calculated. This step provides a reference value for subsequent time comparisons. The calculation formula can be: Theoretical time = Length of the first detection segment / Predicted wave velocity.

[0071] Furthermore, the actual time difference is compared with the theoretical time. This step is crucial for the location method; by comparing the two, it can be preliminarily determined which detection segment the fault is located in. The actual time difference refers to the time difference between the first and second detection positions detecting the fault traveling wave.

[0072] Furthermore, the fault location is determined based on the comparison results. If the actual time difference is less than the theoretical time, the fault location is likely within the first detection segment; otherwise, the fault location is likely within the second detection segment. For different situations, appropriate calculation methods are used to determine the specific fault location.

[0073] In one embodiment of the present invention, the fault location method includes establishing an attenuation characteristic model of a traveling wave signal, including amplitude attenuation and frequency variation; and correcting the distance between the fault point and the branch node according to the attenuation characteristic model to improve the accuracy of fault location.

[0074] Specifically, when establishing a model for the attenuation characteristics of a traveling wave signal, two key factors need to be considered: amplitude attenuation and frequency variation. Amplitude attenuation can be obtained through experimental data or theoretical calculations, and it typically manifests as an exponential decrease in signal strength with propagation distance. Frequency variation may involve the dispersion effect of the signal, and the propagation speed of different frequency components may vary slightly. This model can take various forms; for example, it can be described using a combination of attenuation coefficients and frequency dependence terms.

[0075] Furthermore, based on the established attenuation characteristic model, the distance between the fault point and the branch node can be corrected. The correction process may involve iterative calculations or optimization algorithms to find the fault point location that best reflects the actual situation.

[0076] On the other hand, the present invention also provides a fault location system, which can apply the fault location method in any of the above examples. The fault location system includes: a weather monitoring device for real-time acquisition of environmental data of any segment; a time series system for retrieving and arranging the times of detected fault traveling waves; a historical database for collecting weather conditions and corresponding traveling wave velocities in different historical periods; a control system for controlling a standard traveling wave generator to emit standard traveling waves; a fault location system for obtaining the fault location through judgment and calculation, and visually displaying and recording the fault location results; and a communication module for transmitting data to the fault location system.

[0077] Specifically, weather monitoring devices are used to acquire environmental data for each segment in real time. This data provides the foundation for wave velocity prediction and helps to address the problem that traditional methods do not consider dynamic environmental interference. The time series system is responsible for retrieving and arranging the times when fault traveling waves were detected, providing a basis for determining the fault interval and improving location efficiency. The historical database collects weather conditions and corresponding traveling wave velocities for different historical periods, providing training data for the wave velocity prediction model and helping to build a more accurate prediction model.

[0078] Furthermore, the control system controls the standard traveling wave generator to emit standard traveling waves, achieving real-time wave velocity calibration. This function solves the problem of the lack of a real-time wave velocity calibration mechanism in traditional methods, enabling the system to adapt to sudden weather changes. The fault location system is responsible for the core fault location calculation and result display. By integrating data provided by other modules, the system can calculate the fault location more accurately and improve the readability of the results through visualization. The communication module ensures data transmission between various functional modules, guaranteeing the coordinated operation of the entire system.

[0079] Furthermore, weather monitoring devices can include various sensors, such as temperature sensors, humidity sensors, and wind speed sensors. These sensors can be distributed at key locations along the hybrid line to collect environmental data in real time. For example, the temperature sensor can collect data every 5 minutes, the humidity sensor every 10 minutes, and the wind speed sensor every 1 minute. This data is transmitted via a communication module to a fault location system for real-time updates to the wave velocity prediction model.

[0080] Furthermore, the timing system can employ a high-precision clock source, such as an atomic clock, to ensure time synchronization accuracy at the microsecond level. When a fault traveling wave is detected, the timing system immediately records a timestamp and arranges these timestamps in chronological order. This precise timing information is crucial for determining the fault region.

[0081] Furthermore, the historical database can employ a distributed storage architecture to ensure rapid access to large amounts of historical data. The database can be indexed according to multiple dimensions such as time, geographical location, and line type, facilitating rapid retrieval and analysis. For example, it can store hourly weather conditions and corresponding traveling wave velocities for the past five years, providing rich training data for wave speed prediction models.

[0082] Furthermore, the control system can employ adaptive control algorithms to dynamically adjust the transmission frequency and intensity of the standard traveling wave based on current environmental data and historical data. For example, during periods of drastic weather changes, the transmission frequency of the standard traveling wave can be increased to calibrate the wave velocity more frequently. The control system can also adjust the frequency range of the standard traveling wave according to the characteristics of different line segments to achieve optimal measurement results.

[0083] Furthermore, the fault location system is the core of the entire system, integrating multiple algorithms and models. For example, machine learning algorithms can be used to build a wave velocity prediction model, which can predict the current traveling wave rate based on real-time environmental data and historical data. The fault location system can also employ multiple verification mechanisms, such as simultaneously using the traveling wave method and impedance method for fault location, and then obtaining the final fault location through weighted averaging or other fusion algorithms.

[0084] The communication module can employ 5G or fiber optic communication technology to ensure real-time and reliable data transmission. To address potential communication interruptions, the system can also be configured with local caching and backup communication paths to ensure that critical data is not lost.

[0085] In summary, the specific steps are illustrated in the flowchart below:

[0086] like Figure 1 The flowchart for establishing the rate prediction model is shown below:

[0087] S101. Real-time acquisition of environmental data;

[0088] S102. Determine whether the environmental data exceeds the set threshold.

[0089] S103. If so, proceed to S105;

[0090] S104. If not, proceed to S106.

[0091] S105, Trigger the standard traveling wave generator and proceed to S107;

[0092] S106, The standard traveling wave generator is triggered at a fixed frequency and enters S107;

[0093] S107. Update the real-time traveling wave rate and construct a rate prediction model.

[0094] like Figure 2 The fault location process within the detection range is shown below:

[0095] S201, A fault traveling wave was detected;

[0096] S202. Retrieve the time nodes recorded by the first traveling wave detection device for the current time period;

[0097] S203. Arrange all the acquired time points in chronological order and select the earliest time point.

[0098] S204. Determine if the fault location is at the first node location;

[0099] S205. If so, the fault location is determined to be at the first node.

[0100] S206. If not, retrieve the time nodes recorded by all the first traveling wave detection devices adjacent to the first node, and analyze which detection interval the fault is located in by using the error model and rate prediction model.

[0101] like Figure 3 The fault location segment localization process is shown below:

[0102] S301. Retrieve the current time period and generate the detection time of the second traveling wave detection device within the fault detection interval;

[0103] S302. Determine the first two detection bits according to the order of time, and mark them as the first detection bit and the second detection bit respectively, and obtain the third detection bit;

[0104] S303. Determine whether the detection time recorded by the first detection bit is equal to the detection time recorded by the second detection bit;

[0105] S304. If so, determine that the fault is located within the first detection interval, and obtain the specific location of the fault through calculation;

[0106] S305. If not, further determine whether the fault location occurs at the position of the first detection position;

[0107] S306. If so, then determine that a fault has occurred at the position of the first detection bit;

[0108] S307. If not, check the consistency of weather across all sections.

[0109] S308. If the weather conditions are the same in each section, the fault is located in the first detection section, and the specific location of the fault is obtained by calculation.

[0110] S309. If the weather conditions in different sections are inconsistent, further determine whether the measured time difference is less than the predicted time difference.

[0111] S310. If so, the fault is located in the first detection segment, and the specific location of the fault is obtained by calculation.

[0112] S311. If not, the fault is located in the second detection section, and the specific location of the fault is obtained through calculation.

[0113] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method of fault location for a hybrid line, characterized in that, The mixed line includes overhead lines and cable lines, is divided into multiple detection intervals according to different line types of the mixed line, and any detection interval is divided into multiple equidistant sections, both ends of any section are provided with a standard traveling wave generating device, and both ends of any detection interval are provided with a first traveling wave detection device; The fault locating method comprises: Real-time acquisition of environmental data around each section; The standard traveling wave generating device is controlled to trigger at a first fixed frequency or when the environmental data exceeds a set threshold, so as to update the traveling wave speed in real time; A speed prediction model is established according to the environmental data and the traveling wave speed; When the first traveling wave detection device detects a fault traveling wave, all time nodes at which the first traveling wave detection device detects the fault traveling wave in a current period are called to determine the detection interval in which the fault traveling wave occurs; The speed of the fault traveling wave is predicted according to the speed prediction model, and the fault location is obtained by calculation. The calling of all time nodes at which the first traveling wave detection device detects the fault traveling wave in a current period to determine the detection interval in which the fault traveling wave occurs comprises: Arranging the acquired time nodes in a chronological order, and recording the first traveling wave detection device corresponding to the first time node in the first order as a first node; The first traveling wave detection device adjacent to the first node is sequentially marked as an n-th node in chronological order, where n={2, 3, 4,..., n}; Judging whether the position at which the fault traveling wave occurs is the endpoint position of the first node; If yes, it is judged that a fault occurs at the position of the first node; If not, the predicted passing time of the fault traveling wave through the n-th node and the first node is calculated by the speed prediction model, and compared with the difference of the corresponding time nodes to determine the detection interval in which the fault occurs. The then comparing the predicted passing time of the fault traveling wave through the n-th node and the first node with the difference of the corresponding time nodes to determine the line in which the fault occurs comprises: Defining the difference between the time node of the n-th node and the time node of the first node in a current period as a k-th difference value, where k=n; Acquiring the line length Ln between the n-th node and the first node; According to the speed prediction model, the speed Vn of the fault traveling wave in the n-th node and the first node is acquired, and the passing time Tn is calculated by a formula, wherein the formula is Tn=Ln / Vn; An error model is established; Comparing the size Δt of the k-th difference value with Tn, and correcting the positioning result by the error model. The comparing the size Δt of the k-th difference value with Tn, and correcting the positioning result by the error model comprises: The error model comprises: A wave speed error rate ε, which is dynamically adjusted according to the line type and real-time environmental data; A deviation tolerance coefficient λ, which is used to construct an error buffer interval; The error model performs hierarchical judgment: The first layer: when Δt>Tn×(1+ε×λ), it is directly determined that the fault is located between the first node and the nth node; The second layer: when Tn<Δt≤Tn×(1+ε×λ), the standard traveling wave generating device is triggered to calibrate the wave speed; The third layer: when Δt≤Tn, the equivalent length of the line is corrected according to the environmental data, and the fault location is recalculated. Both ends of any of the sections are further provided with a second traveling wave detection device, and the fault locating method comprises: When the second traveling wave detection device detects a fault traveling wave, the time is recorded as a detection time; All detection times of the fault traveling wave detected by the second traveling wave detection devices in the current period are called to determine the section where the fault traveling wave is generated; The rate of the fault traveling wave is predicted according to the rate prediction model, and the fault location is obtained by calculation. The calling of all detection times of the fault traveling wave detected by the second traveling wave detection devices in the current period to determine the section where the fault traveling wave is generated comprises: The obtained detection times are arranged in order, the first two detection times are obtained, and the second traveling wave detection device corresponding to the first detection time is recorded as a first detection position; The second traveling wave detection device corresponding to the second detection time is recorded as a second detection position; The second traveling wave detection device on the side away from the second detection position of the first detection position is recorded as a third detection position; It is judged whether the position where the fault traveling wave occurs is the endpoint position of the first detection position; If yes, it is judged that the line at the position of the first detection position is faulty; If not, it is judged whether each section in the detection interval in the current period is in the same weather environment according to the environmental data. The judgment of whether each section in the detection interval in the current period is in the same weather environment according to the environmental data comprises: If yes, it is determined that the fault location is between the first detection position and the second detection position, and the specific fault location is obtained by calculation; If not, the fault location is further determined by calculation according to the detection time of the fault traveling wave detected by the first detection position, the second detection position and the third detection position, and the length of the corresponding section. The further calculation of the fault location according to the detection time of the fault traveling wave detected by the first detection position, the second detection position and the third detection position, and the length of the corresponding section comprises: The line between the first detection position and the second detection position is defined as a first detection section; The line between the first detection position and the third detection position is defined as a second detection section; The length of the first detection section is obtained as a second length; The second wave speed in the first detection section in the current period is obtained according to the rate prediction model; The time of the fault traveling wave passing through the first detection section is obtained as a second time by calculation; It is judged whether the difference between the detection times of the first detection position and the second detection position is less than the second time; If yes, it is judged that the fault position is located in the first detection segment, and the specific fault position is obtained by calculation; If no, it is judged that the fault position is located in the second detection segment, and the specific fault position is obtained by calculation.

2. The fault locating method of claim 1, wherein, The fault positioning method comprises, An attenuation characteristic model of the traveling wave signal is established, including amplitude attenuation and frequency change; According to the attenuation characteristic model, the distance between the fault point and the branch node is corrected to improve the accuracy of fault positioning.

3. A fault location system characterized by, The fault positioning system can apply the fault positioning method as claimed in any one of claims 1-2, and the fault positioning system comprises: A weather monitoring device for obtaining the environmental data of any of the segments in real time; A timing system for arranging the time of detecting the fault traveling wave; A historical database for collecting weather conditions and corresponding traveling wave speeds in different historical periods; A control system for controlling the standard traveling wave generating device to emit a standard traveling wave; A fault positioning system for obtaining the fault position by judgment and calculation, and visualizing and recording the fault positioning result; A communication module for transmitting data to the fault positioning system.

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