Distributed line fault detection method

Abstract

The invention discloses a distributed line fault detection method, particularly relates to the technical field of line fault detection, and aims at the identified fault position by identifying the occurrence position and the fault type of each historical fault of a line. The method comprises the following steps: extracting influence parameters of distributed nodes corresponding to a fault occurrence position before a fault occurs as analysis objects, focusing parameter changes of the nodes before the fault occurs, extracting corresponding parameters according to differentiation characteristics of three faults of short circuit, insulation aging and mechanical damage, and processing by utilizing pre-constructed analysis logic, so as to obtain a fault analysis result; according to the invention, the fault state values of the nodes corresponding to different fault types can more accurately reflect the parameter change trend before the different fault types occur, and the problem that the early warning threshold cannot be dynamically optimized according to historical fault data because a fixed threshold method is mostly adopted to trigger fault early warning in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of line fault detection technology, and more specifically, to a distributed line fault detection method. Background Technology

[0002] Power lines are a crucial component of the power system, and their safe and stable operation directly impacts the system's reliability and security. However, because power lines are constantly exposed to the natural environment, they are susceptible to damage from natural factors such as lightning strikes, strong winds, heavy rains, and snow, as well as human-caused disturbances, leading to faults such as short circuits, open circuits, and grounding faults. If these faults are not detected and resolved promptly, they can cause widespread power outages, severely impacting social production and people's lives.

[0003] With the development of distributed technologies and intelligent algorithms, distributed line fault detection methods have gradually become a research hotspot. However, existing distributed line fault detection methods still have the following shortcomings in practical applications:

[0004] The method of triggering fault warnings often uses fixed thresholds, lacks in-depth analysis of the correlation between fault location and node parameter changes, and cannot dynamically optimize the warning thresholds based on historical fault data, which limits the adaptability and accuracy of fault detection.

[0005] To address this, a distributed line fault detection method is proposed. Summary of the Invention

[0006] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a distributed line fault detection method.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A distributed line fault detection method, comprising:

[0009] Fault data processing: Identify the location and type of each historical fault on the line, including short-circuit faults, insulation aging faults, and mechanical damage faults; extract the influence parameters of the corresponding nodes at the fault location before the fault occurred, including RMS current, RMS voltage, insulation resistance, joint temperature, and vibration frequency.

[0010] Early warning threshold analysis: For each fault location and corresponding fault type, analyze the changes in the influence parameters of the corresponding deployed nodes before the fault occurs, generate fault status values ​​for each node corresponding to different fault types, and integrate the fault status values ​​obtained from the analysis of each node corresponding to different fault types into a set of fault early warning thresholds.

[0011] Real-time node monitoring: Monitor the changes in the impact parameters of each node in real time, compare the real-time monitoring results with the fault warning thresholds for different fault types of each node, and selectively trigger the fault warnings corresponding to different fault types based on the comparison results. After triggering the fault warning, mark the predicted fault location and prediction confidence rate and send them to the maintenance personnel.

[0012] Specifically, for short-circuit faults, the analysis of the changes in the influence parameters of the corresponding deployed nodes before the fault occurs includes:

[0013] If the fault type is a short circuit fault, then extract the current, voltage and harmonic content from the influence parameters of the corresponding deployment node before the fault occurs;

[0014] Using the actual time of the fault as a reference point, the current, voltage, and harmonic content within a set time window before the fault occurs are extracted.

[0015] For the effective current value at each time point within the set time window before the fault occurs, the effective current value at each time point is used as the numerator and the rated current is used as the denominator to calculate the current ratio at each time point.

[0016] For the effective voltage values ​​at each time point within the set time window before the fault occurs, the voltage ratio at each time point is calculated using the effective voltage value at each time point as the denominator and the rated voltage as the numerator.

[0017] Specifically, generating the fault status value corresponding to the short-circuit fault type for each node is as follows:

[0018] The average value of each group's current ratio is taken as the current state value within the set time window before the fault occurs, and the average value of each group's voltage ratio is taken as the voltage state value within the set time window before the fault occurs.

[0019] The current state value and voltage state value within the time window set before the fault occurs are multiplied by the corresponding preset current weight and voltage weight, and then summed to obtain the fault state value of the node corresponding to the short circuit fault.

[0020] Specifically, regarding insulation aging faults, the analysis of the changes in the influence parameters of the corresponding deployment nodes before the fault occurs includes:

[0021] If the fault type is insulation aging fault, then the insulation resistance and joint temperature are extracted from the influence parameters of the corresponding deployment node before the fault occurred.

[0022] Using the actual time of the fault as a reference point, the insulation resistance and joint temperature within a set time window before the fault occurred are extracted.

[0023] Extract the insulation resistance values ​​at each time point within the set time window before the fault occurs, and calculate the resistance retention rate at each time point using the insulation resistance value at each time point as the numerator and the initial reference value as the denominator.

[0024] The normal reference rate of the preset resistance retention rate is used to compare the resistance retention rate at each time point with the normal reference rate in chronological order until a set of resistance retention rates is found to be lower than the preset normal reference rate. The time point corresponding to the set of resistance retention rates is marked as the starting point, and the duration of the rate being lower than the normal reference rate is counted from the starting point as the attenuation duration.

[0025] Extract the insulation resistance value at the end of the set time window, subtract the insulation resistance value at the end of the set time window from the initial reference value, and divide the result by the set time window duration to obtain the attenuation rate.

[0026] Extract the joint temperature at each time point within the set time window before the fault occurs. Construct a Cartesian coordinate system with the set time window as the horizontal axis and the joint temperature as the vertical axis. Plot the numerical points of the joint temperature at each time point in the Cartesian coordinate system. Preset the temperature threshold of the joint temperature. Identify the position corresponding to the temperature threshold on the vertical axis as the starting position. Extend a line segment horizontally to the right from the starting position as the threshold line.

[0027] Value points above the threshold line are identified as joint anomaly points. Vertical line segments between the joint anomaly points and the threshold line are constructed from each group of joint anomaly points. The average length of the vertical line segments constructed from each group of joint anomaly points is calculated to obtain the joint temperature anomaly value. The proportion of the number of joint anomaly points to the total number of value points is calculated to obtain the joint temperature anomaly ratio.

[0028] Specifically, generating the fault state value corresponding to the insulation aging fault type for each node is as follows:

[0029] The allowed duration and allowed rate of attenuation decrease are preset. The ratio of the attenuation duration and attenuation decrease rate within the time window before the fault occurs is calculated with the corresponding preset allowed duration and allowed rate of attenuation decrease, respectively, to obtain the duration state ratio and rate state ratio.

[0030] The allowable abnormal value and allowable abnormal ratio of the preset temperature abnormality value and the temperature abnormality ratio are calculated by comparing the temperature abnormality value and the temperature abnormality ratio within the set time window before the fault occurs with the corresponding preset allowable abnormality value and allowable abnormality ratio to obtain the abnormality state ratio and the abnormality state ratio.

[0031] Extract the duration state ratio, rate state ratio, abnormal length state ratio, and abnormal number state ratio within the set time window before the fault occurs, and multiply them by the preset duration weight, rate weight, abnormal length weight, and abnormal number weight respectively. Then sum them to obtain the fault state value of the insulation aging fault corresponding to the node.

[0032] Specifically, regarding mechanical damage failures, the analysis of the changes in the influence parameters of the corresponding deployment nodes before the failure occurs includes:

[0033] If the fault type is mechanical damage fault, then extract the joint temperature and vibration frequency from the influence parameters of the corresponding deployment node before the fault occurs.

[0034] Using the actual time of the fault as a reference point, the joint temperature and vibration frequency within a set time window before the fault occurred are captured.

[0035] Extract the vibration frequency values ​​at each time point within the set time window before the fault occurs, and compare the vibration frequency values ​​at each time point with the reference vibration frequency; identify the number of time points within the set time window that are higher than the reference vibration frequency as the number of abnormal vibrations, and calculate the ratio between the number of abnormal vibrations and the total number of time points as the proportion of abnormal vibrations within the set time window before the fault occurs.

[0036] Extract the temperature difference between the end and start time points within the set time window before the fault occurs, divide it by the duration of the time window, and obtain the temperature rise rate.

[0037] Specifically, generating the fault state value corresponding to the mechanical damage fault type for each node is as follows:

[0038] The permissible vibration ratio and temperature rise warning rate of the preset vibration abnormality ratio and temperature rise rate are calculated by comparing the vibration abnormality ratio and temperature rise rate within the set time window before the fault occurs with the corresponding preset permissible vibration ratio and temperature rise warning rate to obtain the vibration state ratio and temperature rise state ratio.

[0039] Extract the vibration state ratio and temperature rise state ratio within a set time window before the fault occurs, multiply them by the preset vibration weight and temperature rise weight respectively, and then sum them to obtain the fault state value of the mechanical damage fault corresponding to the node.

[0040] Specifically, the selective triggering of fault warnings corresponding to different fault types based on the comparison results includes:

[0041] The system monitors the fault status values ​​of each node corresponding to different fault types in real time as real-time status values. It extracts each set of fault status values ​​of the same fault type from the fault warning threshold set of each node. It compares the real-time status value of each node with each set of fault status values ​​extracted from the fault warning threshold set. If the real-time status value of a node is higher than any fault status value extracted from the fault warning threshold set, a fault warning for the corresponding fault type is triggered.

[0042] Specifically, after triggering the fault warning, the predicted fault location and prediction confidence rate are marked as follows:

[0043] Identify the fault type that triggers the fault warning. If it is a short circuit fault, the real-time status value is parsed into current status value and voltage status value, represented by x1 and x2. Similarly, each set of fault status values ​​extracted from the fault warning threshold set is parsed into current status value and voltage status value, represented by y1 and y2.

[0044] Calculate the credibility value using the formula ,in The confidence influence weights for the current state value and the voltage state value are respectively.

[0045] Select fault state values ​​with lower confidence level R and identify the historical fault numbers corresponding to these fault state values. Based on the historical fault numbers, retrieve the fault occurrence time and location in the database and use the retrieved fault occurrence location as the predicted fault location to trigger the current fault warning.

[0046] Each set of confidence level values ​​corresponds to a set of confidence level intervals, and each set of confidence level intervals corresponds to a confidence rate. The lower confidence level value R is matched with each set of confidence level intervals, and the matching confidence rate is used as the predicted confidence rate for triggering the fault warning.

[0047] The technical effects and advantages of this invention are as follows:

[0048] (1) By identifying the location and type of each historical fault in the line, the influence parameters of the corresponding nodes before the fault occurred are extracted for the identified fault location as the analysis object. Focusing on the parameter changes of these nodes before the fault occurred, the corresponding parameters are extracted according to the differentiated characteristics of the three types of faults: short circuit, insulation aging, and mechanical damage. The parameters are processed using the pre-constructed analysis logic and used as the fault status values ​​of the nodes corresponding to different fault types. This more accurately reflects the parameter change trend before the occurrence of different fault types and solves the problem that existing technologies mostly use fixed thresholds to trigger fault warnings and cannot dynamically optimize the warning threshold based on historical fault data.

[0049] (2) By calculating the confidence level of real-time status values ​​and historical fault status values, the location of similar historical faults is matched as the predicted location, which solves the problem that traditional early warning can only judge the type but cannot locate the fault. The confidence level value is mapped to the prediction confidence rate, providing quantitative decision-making basis for operation and maintenance personnel.

[0050] (3) By constructing a set of fault warning thresholds, when a new fault occurs, the threshold of the corresponding node is dynamically updated according to the location of the new fault and the parameter analysis results. It can continuously learn from historical data and adapt to changes in line status. Attached Figure Description

[0051] Figure 1 This is a flowchart of a distributed line fault detection method according to the present invention. Detailed Implementation

[0052] 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.

[0053] Example

[0054] like Figure 1 As shown, a distributed line fault detection method includes:

[0055] Fault data processing: Identify the location and type of each historical fault on the line, including short-circuit faults, insulation aging faults, and mechanical damage faults; extract the influence parameters of the corresponding nodes at the fault location before the fault occurred, including RMS current, RMS voltage, insulation resistance, joint temperature, and vibration frequency.

[0056] Additional information: A monitoring node is set up at a set distance (e.g., 0.5-2 km) along the transmission line. Each node is equipped with multiple types of sensors to collect parameters such as current, voltage, and power in real time, with a sampling frequency of 1-5 kHz.

[0057] For the identified fault locations, select the 3-5 monitoring nodes closest to the fault point (usually 2 nodes before and 2 nodes after the fault point) as the corresponding nodes for the analysis of operating parameters.

[0058] Early warning threshold analysis: For each fault location and corresponding fault type, analyze the changes in the influence parameters of the corresponding deployed nodes before the fault occurs, generate fault status values ​​for each node corresponding to different fault types, and integrate the fault status values ​​obtained from the analysis of each node corresponding to different fault types into a set of fault early warning thresholds.

[0059] Specifically:

[0060] If the fault type is a short circuit fault, then extract the current, voltage and harmonic content from the influence parameters of the corresponding deployment node before the fault occurs;

[0061] Using the actual time of the fault as a reference point, the current, voltage, and harmonic content within a set time window before the fault occurs are captured; the set time window for short-circuit faults can be set to 5 minutes; the precursory characteristics of short-circuit faults usually appear within a short period of time.

[0062] For the effective current value at each time point within the set time window before the fault occurs, the effective current value at each time point is used as the numerator and the rated current is used as the denominator. The current ratio at each time point is calculated separately, and the average of the current ratios is taken as the current status value within the set time window before the fault occurs.

[0063] Before a fault occurs, the current will suddenly increase significantly. It is necessary to analyze the increase in the effective value of the current within the set time window.

[0064] For the effective voltage values ​​at each time point within the set time window before the fault occurs, the voltage ratio at each time point is calculated using the effective voltage value at each time point as the denominator and the rated voltage as the numerator. The average value of each group of voltage ratios is taken as the voltage status value within the set time window before the fault occurs.

[0065] Before a fault occurs, the voltage will drop sharply, and it is necessary to analyze the extent of the voltage drop within a set time window.

[0066] The current state value and voltage state value within the time window set before the fault occurs are multiplied by the corresponding preset current weight and voltage weight, and then summed to obtain the fault state value of the node corresponding to the short circuit fault.

[0067] In addition, in response to the core characteristics of short-circuit faults, namely "sudden increase in current and sudden drop in voltage", the parameters are standardized by "ratio-based" processing (current / rated current, rated voltage / voltage). At the same time, the average value is used to smooth out short-term fluctuations and more accurately reflect the overall trend changes before the fault, thus improving the accuracy of the early warning.

[0068] If the fault type is insulation aging fault, then the insulation resistance and joint temperature are extracted from the influence parameters of the corresponding deployment node before the fault occurred.

[0069] Using the actual time of the fault as the benchmark, the insulation resistance and joint temperature within the set time window before the fault occurred are extracted; the set time window for insulation aging fault is set to 30 days. Insulation aging is a gradual deterioration process, and the precursor characteristics need to be monitored over a long period of time.

[0070] Extract the insulation resistance values ​​at each time point within the set time window before the fault occurs. Use the insulation resistance value at each time point as the numerator and the initial reference value (measured value after new commissioning or maintenance) as the denominator to calculate the resistance retention rate at each time point.

[0071] The normal reference rate of the preset resistance retention rate is used to compare the resistance retention rate at each time point with the normal reference rate in chronological order until a set of resistance retention rates is found to be lower than the preset normal reference rate. The time point corresponding to the set of resistance retention rates is marked as the starting point, and the duration of the rate being lower than the normal reference rate is counted from the starting point as the attenuation duration.

[0072] Extract the insulation resistance value at the end of the set time window, subtract the insulation resistance value at the end of the time window from the initial reference value, and divide the result by the set time window duration to obtain the attenuation rate; the end time point can be set to the value 1 hour before the fault occurs.

[0073] The allowed duration and allowed rate of attenuation decrease are preset. The ratio of the attenuation duration and attenuation decrease rate within the time window before the fault occurs is calculated with the corresponding preset allowed duration and allowed rate of attenuation decrease, respectively, to obtain the duration state ratio and rate state ratio.

[0074] Extract the joint temperature at each time point within the set time window before the fault occurs. Construct a Cartesian coordinate system with the set time window as the horizontal axis and the joint temperature as the vertical axis. Plot the numerical points of the joint temperature at each time point in the Cartesian coordinate system. Preset the temperature threshold of the joint temperature. Identify the position corresponding to the temperature threshold on the vertical axis as the starting position. Extend a line segment horizontally to the right from the starting position as the threshold line.

[0075] Value points above the threshold line are identified as joint anomaly points. Vertical line segments between the joint anomaly points and the threshold line are constructed from each group of joint anomaly points. The average length of the vertical line segments constructed from each group of joint anomaly points is calculated to obtain the joint temperature anomaly value. The proportion of the number of joint anomaly points to the total number of value points is calculated to obtain the joint temperature anomaly ratio.

[0076] The allowable abnormal value and allowable abnormal ratio of the preset temperature abnormality value and the temperature abnormality ratio are calculated by comparing the temperature abnormality value and the temperature abnormality ratio within the set time window before the fault occurs with the corresponding preset allowable abnormality value and allowable abnormality ratio to obtain the abnormality state ratio and the abnormality state ratio.

[0077] Extract the duration state ratio, rate state ratio, abnormal length state ratio, and abnormal number state ratio within the set time window before the fault occurs, and multiply them by the preset duration weight, rate weight, abnormal length weight, and abnormal number weight respectively, and then sum them to obtain the fault state value of the insulation aging fault corresponding to the node.

[0078] In addition, considering the slow evolution characteristics of insulation aging, a long-cycle time window is adopted, combined with continuous monitoring of the decay of resistance retention rate and analysis of the trend increase of joint temperature. This effectively avoids short-term fluctuation interference, solves the problem that traditional fixed thresholds are difficult to capture progressive faults, and accurately adapts to the progressive characteristics of insulation aging faults. This ensures both the advance warning and the reliability of the judgment.

[0079] If the fault type is mechanical damage fault, then extract the joint temperature and vibration frequency from the influence parameters of the corresponding deployment node before the fault occurs.

[0080] Using the actual time of the fault as the benchmark, the joint temperature and vibration frequency within the set time window before the fault occurred are captured; the set time window for mechanical damage faults is set to 7 days, and its precursor characteristics are mostly manifested as gradual mechanical abnormalities such as loosening and displacement of equipment parts, which require mid-term monitoring to capture trend changes.

[0081] Extract the vibration frequency values ​​at each time point within the set time window before the fault occurs, and compare the vibration frequency values ​​at each time point with the reference vibration frequency; where the reference vibration frequency is the stable frequency when there is no mechanical abnormality; identify the number of time points within the set time window that are higher than the reference vibration frequency, as the number of abnormal vibrations, and calculate the ratio between the number of abnormal vibrations and the total number of time points, as the proportion of abnormal vibrations within the set time window before the fault occurs.

[0082] Extract the temperature difference between the end and start time points within the set time window before the fault occurs, divide it by the duration of the time window, and obtain the temperature rise rate.

[0083] The permissible vibration ratio and temperature rise warning rate of the preset vibration abnormality ratio and temperature rise rate are calculated by comparing the vibration abnormality ratio and temperature rise rate within the set time window before the fault occurs with the corresponding preset permissible vibration ratio and temperature rise warning rate to obtain the vibration state ratio and temperature rise state ratio.

[0084] Extract the vibration state ratio and temperature rise state ratio within a set time window before the fault occurs, multiply them by the preset vibration weight and temperature rise weight respectively, and then sum them to obtain the fault state value of the mechanical damage fault corresponding to the node.

[0085] In addition, by analyzing the parameters of the 7 days prior to the occurrence of the fault, it is possible to identify the precursors of the fault based on the abnormal changes in vibration and temperature before the actual occurrence of the mechanical damage fault, and generate early warning thresholds. This gives maintenance personnel enough time to investigate and deal with the fault, which helps to avoid more serious consequences such as power outages and equipment damage caused by the expansion of the fault, and improves the reliability of the transmission line operation.

[0086] Real-time node monitoring: Monitor the changes in the impact parameters of each node in real time, compare the real-time monitoring results with the fault warning thresholds of each node for different fault types, and selectively trigger fault warnings corresponding to different fault types based on the comparison results. After triggering the fault warning, mark the predicted fault location and prediction confidence rate.

[0087] Specifically:

[0088] The system monitors the fault status values ​​of each node corresponding to different fault types in real time as real-time status values. It extracts each set of fault status values ​​of the same fault type from the fault warning threshold set of each node. It compares the real-time status value of each node with each set of fault status values ​​extracted from the fault warning threshold set. If the real-time status value of a node is higher than any fault status value extracted from the fault warning threshold set, a fault warning for the corresponding fault type is triggered.

[0089] Identify the fault type that triggers the fault warning. If it is a short circuit fault, the real-time status value is parsed into current status value and voltage status value, represented by x1 and x2. Similarly, each set of fault status values ​​extracted from the fault warning threshold set is parsed into current status value and voltage status value, represented by y1 and y2.

[0090] Calculate the credibility value using the formula ,in The confidence influence weights for the current state value and the voltage state value are respectively.

[0091] Select fault state values ​​with lower confidence level R and identify the historical fault numbers corresponding to these fault state values. Based on the historical fault numbers, retrieve the fault occurrence time and location in the database and use the retrieved fault occurrence location as the predicted fault location to trigger the current fault warning.

[0092] The system presets the confidence level values ​​and the corresponding ranges of confidence levels. Each range of confidence levels corresponds to a confidence rate. The confidence rate range is set between 1% and 100%. The lower the confidence level value, the higher the confidence rate. When the confidence level value is 0, the confidence rate is 100%.

[0093] The lower confidence level value R is matched with the range of confidence level values ​​in each group, and the confidence rate obtained from the matching is used as the predicted confidence rate for triggering the fault warning at present.

[0094] By selecting historical fault status values ​​with lower confidence levels (R), matching the corresponding historical fault numbers, and then retrieving the fault location, the logic of "locating the current fault based on similar fault characteristics" is realized by linking historical data with real-time monitoring. This solves the problem that traditional early warning systems can only determine the fault type but cannot accurately locate the fault.

[0095] For example, when the current and voltage states of a real-time short-circuit fault are highly similar to the characteristics of a historical short-circuit fault (R value close to 0), the location of that historical fault can be directly reused as the predicted location, which greatly improves the efficiency and accuracy of the location.

[0096] To further clarify, the determination of predicted fault location and prediction confidence rate for insulation aging faults and mechanical damage faults is similar to that for short circuit faults, as follows:

[0097] Identify the fault type that triggers the fault warning. If it is an insulation aging fault, the real-time status value is parsed into the time-state ratio, rate-state ratio, differential-length state ratio, and differential-number state ratio, denoted by z1, z2, z3, and z4. Similarly, each set of fault status values ​​extracted from the fault warning threshold set is parsed into the time-state ratio, rate-state ratio, differential-length state ratio, and differential-number state ratio, denoted by w1, w2, w3, and w4.

[0098] Using formula The confidence level value was calculated, where The confidence impact weights are respectively for the duration state ratio, rate state ratio, anisotropic state ratio, and anisotropic state ratio;

[0099] Identify the type of fault that triggers the fault warning. If it is a mechanical damage fault, the real-time state value is parsed into vibration state ratio and temperature rise state ratio, denoted by u1 and u2. Similarly, each set of fault state values ​​extracted from the fault warning threshold set is parsed into vibration state ratio and temperature rise state ratio, denoted by k1 and k2.

[0100] Using formula The confidence level value was calculated, where These are the confidence influence weights for the vibration state ratio and the temperature rise state ratio, respectively.

[0101] Node set establishment: When a new fault occurs, the fault warning threshold set of the corresponding node is dynamically updated based on the analysis results of the new fault location and the impact parameters of the corresponding node.

[0102] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0103] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0104] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0108] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0109] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A distributed line fault detection method, characterized in that, include: Fault data processing: Identify the location and type of each historical fault on the line, including short-circuit faults, insulation aging faults, and mechanical damage faults; extract the influence parameters of the corresponding nodes at the fault location before the fault occurred, including RMS current, RMS voltage, insulation resistance, joint temperature, and vibration frequency. Early warning threshold analysis: For each fault location and corresponding fault type, analyze the changes in the influence parameters of the corresponding deployed nodes before the fault occurs, generate fault status values ​​for each node corresponding to different fault types, and integrate the fault status values ​​obtained from the analysis of each node corresponding to different fault types into a set of fault early warning thresholds. Real-time node monitoring: Monitor the changes in the impact parameters of each node in real time, compare the real-time monitoring results with the fault warning thresholds for different fault types of each node, and selectively trigger the fault warnings corresponding to different fault types based on the comparison results. After triggering the fault warning, mark the predicted fault location and prediction confidence rate and send them to the maintenance personnel.

2. The distributed line fault detection method according to claim 1, characterized in that, The analysis of the changes in the influence parameters of the corresponding deployed nodes before the occurrence of the short-circuit fault is as follows: If the fault type is a short circuit fault, then extract the current, voltage and harmonic content from the influence parameters of the corresponding deployment node before the fault occurs; Using the actual time of the fault as a reference point, the current, voltage, and harmonic content within a set time window before the fault occurs are extracted. For the effective current value at each time point within the set time window before the fault occurs, the effective current value at each time point is used as the numerator and the rated current is used as the denominator to calculate the current ratio at each time point. For the effective voltage values ​​at each time point within the set time window before the fault occurs, the voltage ratio at each time point is calculated using the effective voltage value at each time point as the denominator and the rated voltage as the numerator.

3. The distributed line fault detection method according to claim 2, characterized in that, The generation of fault status values ​​for each node corresponding to the short-circuit fault type is specifically as follows: The average value of each group's current ratio is taken as the current state value within the set time window before the fault occurs, and the average value of each group's voltage ratio is taken as the voltage state value within the set time window before the fault occurs. The current state value and voltage state value within the time window set before the fault occurs are multiplied by the corresponding preset current weight and voltage weight, and then summed to obtain the fault state value of the node corresponding to the short circuit fault.

4. The distributed line fault detection method according to claim 1, characterized in that, The analysis of the changes in influencing parameters of the corresponding deployment nodes before the occurrence of insulation aging faults is as follows: If the fault type is insulation aging fault, then the insulation resistance and joint temperature are extracted from the influence parameters of the corresponding deployment node before the fault occurred. Using the actual time of the fault as a reference point, the insulation resistance and joint temperature within a set time window before the fault occurred are extracted. Extract the insulation resistance values ​​at each time point within the set time window before the fault occurs, and calculate the resistance retention rate at each time point using the insulation resistance value at each time point as the numerator and the initial reference value as the denominator. The normal reference rate of the preset resistance retention rate is used to compare the resistance retention rate at each time point with the normal reference rate in chronological order until a set of resistance retention rates is found to be lower than the preset normal reference rate. The time point corresponding to the set of resistance retention rates is marked as the starting point, and the duration of the rate being lower than the normal reference rate is counted from the starting point as the attenuation duration. Extract the insulation resistance value at the end of the set time window, subtract the insulation resistance value at the end of the set time window from the initial reference value, and divide the result by the set time window duration to obtain the attenuation rate. Extract the joint temperature at each time point within the set time window before the fault occurs. Construct a Cartesian coordinate system with the set time window as the horizontal axis and the joint temperature as the vertical axis. Plot the numerical points of the joint temperature at each time point in the Cartesian coordinate system. Preset the temperature threshold of the joint temperature. Identify the position corresponding to the temperature threshold on the vertical axis as the starting position. Extend a line segment horizontally to the right from the starting position as the threshold line. Value points above the threshold line are identified as joint anomaly points. Vertical line segments between the joint anomaly points and the threshold line are constructed from each group of joint anomaly points. The average length of the vertical line segments constructed from each group of joint anomaly points is calculated to obtain the joint temperature anomaly value. The proportion of the number of joint anomaly points to the total number of value points is calculated to obtain the joint temperature anomaly ratio.

5. A distributed line fault detection method according to claim 4, characterized in that, The generation of fault status values ​​for each node corresponding to the insulation aging fault type is specifically as follows: The allowed duration and allowed rate of attenuation decrease are preset. The ratio of the attenuation duration and attenuation decrease rate within the time window before the fault occurs is calculated with the corresponding preset allowed duration and allowed rate of attenuation decrease, respectively, to obtain the duration state ratio and rate state ratio. The allowable abnormal value and allowable abnormal ratio of the preset temperature abnormality value and the temperature abnormality ratio are calculated by comparing the temperature abnormality value and the temperature abnormality ratio within the set time window before the fault occurs with the corresponding preset allowable abnormality value and allowable abnormality ratio to obtain the abnormality state ratio and the abnormality state ratio. Extract the duration state ratio, rate state ratio, abnormal length state ratio, and abnormal number state ratio within the set time window before the fault occurs, and multiply them by the preset duration weight, rate weight, abnormal length weight, and abnormal number weight respectively. Then sum them to obtain the fault state value of the insulation aging fault corresponding to the node.

6. The distributed line fault detection method according to claim 1, characterized in that, The analysis of the changes in the influence parameters of the corresponding deployment nodes before the occurrence of mechanical damage failures is as follows: If the fault type is mechanical damage fault, then extract the joint temperature and vibration frequency from the influence parameters of the corresponding deployment node before the fault occurs. Using the actual time of the fault as a reference point, the joint temperature and vibration frequency within a set time window before the fault occurred are captured. Extract the vibration frequency values ​​at each time point within the set time window before the fault occurs, and compare the vibration frequency values ​​at each time point with the reference vibration frequency; identify the number of time points within the set time window that are higher than the reference vibration frequency as the number of abnormal vibrations, and calculate the ratio between the number of abnormal vibrations and the total number of time points as the proportion of abnormal vibrations within the set time window before the fault occurs. Extract the temperature difference between the end and start time points within the set time window before the fault occurs, divide it by the duration of the time window, and obtain the temperature rise rate.

7. A distributed line fault detection method according to claim 6, characterized in that, The generation of fault status values ​​for each node corresponding to the mechanical damage fault type is specifically as follows: The permissible vibration ratio and temperature rise warning rate of the preset vibration abnormality ratio and temperature rise rate are calculated by comparing the vibration abnormality ratio and temperature rise rate within the set time window before the fault occurs with the corresponding preset permissible vibration ratio and temperature rise warning rate to obtain the vibration state ratio and temperature rise state ratio. Extract the vibration state ratio and temperature rise state ratio within a set time window before the fault occurs, multiply them by the preset vibration weight and temperature rise weight respectively, and then sum them to obtain the fault state value of the mechanical damage fault corresponding to the node.

8. The distributed line fault detection method according to claim 1, characterized in that, The specific method for selectively triggering fault warnings corresponding to different fault types based on comparison results is as follows: The system monitors the fault status values ​​of each node corresponding to different fault types in real time as real-time status values. It extracts each set of fault status values ​​of the same fault type from the fault warning threshold set of each node. It compares the real-time status value of each node with each set of fault status values ​​extracted from the fault warning threshold set. If the real-time status value of a node is higher than any fault status value extracted from the fault warning threshold set, a fault warning for the corresponding fault type is triggered.

9. A distributed line fault detection method according to claim 1, characterized in that, The process of marking the predicted fault location and prediction confidence rate after triggering the fault warning is as follows: Identify the fault type that triggers the fault warning. If it is a short circuit fault, the real-time status value is parsed into current status value and voltage status value, represented by x1 and x2. Similarly, each set of fault status values ​​extracted from the fault warning threshold set is parsed into current status value and voltage status value, represented by y1 and y2. Calculate the credibility value using the formula ,in The confidence influence weights for the current state value and the voltage state value are respectively. Select fault state values ​​with lower confidence level R and identify the historical fault numbers corresponding to these fault state values. Based on the historical fault numbers, retrieve the fault occurrence time and location in the database and use the retrieved fault occurrence location as the predicted fault location to trigger the current fault warning. Each set of confidence level values ​​corresponds to a set of confidence level intervals, and each set of confidence level intervals corresponds to a confidence rate. The lower confidence level value R is matched with each set of confidence level intervals, and the matching confidence rate is used as the predicted confidence rate for triggering the fault warning.

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