Early warning method and system for deterioration trend of conducting state of grounding device
By measuring the conduction impedance of the grounding down conductor in the grounding grid, a topology connection model is established and a degradation trend model is constructed. This solves the problems of one-sidedness and lack of early warning in the existing technology for grounding device condition assessment, realizes accurate assessment and early warning of grounding devices, and improves the operation safety and efficiency of the power grid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-24
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies cannot accurately assess the health status of individual grounding devices and lack early warning of dynamic deterioration trends in grounding device performance, resulting in difficulties and inefficiency in identifying potential hazards and the inability to achieve early warning.
By measuring the conduction impedance of multiple grounding down conductors in the grounding grid, a topology connection model is established, the grounding impedance value is calculated, a degradation trend model is constructed, and future resistance values are predicted. Based on the comprehensive evaluation model, a risk level warning is output.
It enables early warning of latent faults in grounding devices, improves the proactive safety and economy of power grid operation, and reduces operation and maintenance costs and efficiency.
Smart Images
Figure CN121831607A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power system equipment status monitoring and fault diagnosis, and specifically relates to a method and system for early warning of the deterioration trend of the conduction state of a grounding device. Background Art
[0002] The grounding device is a key facility to ensure the safe and stable operation of the power system and protect the safety of personnel and equipment. Whether its conduction state is good or not directly affects whether lightning current and short-circuit current can be smoothly discharged. At present, in engineering, a portable grounding conduction tester is generally used to regularly measure the conduction impedance between adjacent grounding down conductors, and judge whether the measured value is qualified according to relevant regulations. However, the existing technical solutions have the following significant defects: (1)片面化状态评估与定位困难:现有方法仅能获取两个测试点之间回路阻抗的宏观结果。当测得接地引下线1(参考点)与2(测试点)之间的导通阻抗值异常增大时,现有技术无法判断是接地点1的接地性能劣化、接地点2的接地性能劣化,还是两者之间的连接导体出现问题。在下一轮测试时,若参考点更换为3点,则无法定量比较接地阻抗变化趋势,若不以接地点的评估方式,则可能导致在发现隐患后仍需投入大量人力物力进行排查定位,效率低下,且无法对单个接地点的健康状态进行精准管理。 One-sided state assessment and difficulty in positioning: The existing method can only obtain the macroscopic result of the loop impedance between two test points. When the conduction impedance value between the grounding down conductor 1 (reference point) and 2 (test point) is measured to increase abnormally, the existing technology cannot determine whether it is the deterioration of the grounding performance of grounding point 1, the deterioration of the grounding performance of grounding point 2, or a problem with the connecting conductor between the two. In the next round of testing, if the reference point is changed to point 3, the change trend of the grounding impedance cannot be quantitatively compared. If the evaluation method does not use the grounding point, it may lead to a large amount of manpower and material resources being invested in troubleshooting and positioning after potential hazards are found, with low efficiency, and the health status of a single grounding point cannot be accurately managed.
[0003] (2)数据分析浅层化与预警缺失:传统的状态评估仅依赖于单次测量结果与固定阈值的比较,缺乏对同一测点历史数据的纵向深度挖掘。这种模式是一种“事后判断”,而非“事前预警”。它无法反映接地装置性能的动态劣化趋势,无法在设备彻底失效前提前发现隐患。例如,一个接地点的接地电阻可能每年以5%的速率缓慢增长,虽然每次测量值都未超标,但其劣化趋势表明它将在几年后失效,而现有技术无法识别这种潜在风险。 Shallow data analysis and lack of early warning: Traditional state assessment only relies on the comparison of single measurement results with fixed thresholds, lacking in-depth longitudinal mining of historical data at the same measurement point. This mode is a "post-mortem judgment" rather than "pre-warning". It cannot reflect the dynamic deterioration trend of the grounding device performance and cannot detect potential hazards in advance before the equipment completely fails. For example, the grounding resistance of a grounding point may slowly increase at a rate of 5% per year. Although each measured value does not exceed the standard, its deterioration trend indicates that it will fail in a few years, and the existing technology cannot identify this potential risk.
[0004] Therefore, there is an urgent need for an innovative method that can accurately track and evaluate the states of each independent grounding point in the grounding grid, predict its deterioration trend based on historical data, and achieve early warning. Summary of the Invention
[0005] The technical problem to be solved by the present invention: Aiming at the above problems of the existing technology, a method and system for early warning of the deterioration trend of the conduction state of a grounding device are provided to achieve early and advanced warning of potential faults of the grounding device, and improve the proactive safety and economy of power grid operation.
[0006] To solve the above technical problems, the technical solution adopted by the present invention is: A method for early warning of the deterioration trend of the conduction status of a grounding device includes the following steps: S1, measure the continuity impedance between pairs of multiple grounding lead test points in the grounding grid according to the preset test path; S2, calculate the grounding impedance value of each grounding down conductor measurement point based on the multiple measured conduction impedance values obtained; S3. Extract the grounding impedance value of each grounding down conductor measuring point in chronological order to form a historical data sequence of grounding impedance for each measuring point. Fit the sequence to establish a deterioration trend model of grounding impedance over time. Based on the deterioration trend model, predict the resistance value of each grounding down conductor measuring point in the future. S4. Construct a comprehensive evaluation model based on the predicted resistance value, and output the corresponding risk level prompt according to the range of the evaluation results of the comprehensive evaluation model.
[0007] Further, step S2 includes: Establish a topological connection model of the grounding grid, and regard the entire grounding grid as a network consisting of nodes and multiple branches, where nodes represent grounding down conductor measurement points and branches represent connecting conductors and grounding resistance between measurement points; A mathematical model based on the power grid topology is constructed using multiple measured conduction impedance values. The grounding impedance value at each grounding down conductor measurement point is calculated, and the calculation equations are as follows:
[0008] In the above formula, These are the grounding impedance values at the first, second, ..., and Nth grounding lead-down test points, respectively. The grounding continuity impedance of the second, third, ..., Nth measuring points is measured sequentially, with the first grounding lead measuring point as the reference point. The Newton-Raphson algorithm is used to solve the equations. The measured branch-level conduction impedance of each branch is calculated and separated into the node-level grounding impedance represented by each grounding down conductor measurement point.
[0009] Further, step S3 includes: After the substation grounding grid is put into operation, the nodal-level grounding impedance data of each grounding down conductor measuring point is preprocessed, and the grounding impedance data is reduced to the test value of the month in which the commissioning date is used by linear regression method to construct a grounding impedance dataset. The stationarity test is performed on the sequence constructed from the grounding impedance dataset. After the test is passed, the partial autocorrelation function plot of the sequence is plotted and a degradation trend model of grounding impedance over time is constructed. The coefficients of the degradation trend model are estimated using the maximum likelihood estimation method. The predicted resistance value of each grounding down conductor measurement point is calculated using the degradation trend model, and the average predicted resistance value, degradation rate, and maximum predicted resistance value are calculated based on each predicted resistance value.
[0010] Furthermore, the calculation expression for reducing the grounding impedance data to the test value of the month in which the commissioning date falls is as follows:
[0011] In the above formula, , These are the month numbers corresponding to the t-th and t-1-th tests, respectively. , , These are the test values of the i-th grounding down conductor test point after the t-th, t-1-th, and initial tests, respectively, reverting to the month of the commissioning date. , These are the test values and initial test values of the i-th grounding lead test point at the t-th test, respectively.
[0012] Furthermore, the expression for constructing the model of the degradation trend of grounding impedance over time is as follows:
[0013] In the above formula, Let be the white noise from the t-th test. , , ... c represents the test value of the i-th grounding down conductor test point after the t-th, t-1-th, t-2-th, ..., tp-th tests, which is then reverted to the month of the commissioning date. , … The values to be fitted are denoted as .
[0014] Furthermore, the calculation expressions for the average resistance prediction, degradation rate, and maximum resistance prediction based on each resistance prediction value are as follows: The predicted average resistance value is:
[0015] In the above formula, Let N be the predicted resistance value of the i-th grounding down conductor measuring point, and N be the total number of grounding down conductor measuring points. ; The maximum predicted resistance value is:
[0016] In the above formula, These are the predicted resistance values for the first, second, ..., Nth grounding lead measuring points, respectively.
[0017] Furthermore, the expression for the comprehensive evaluation model is as follows:
[0018]
[0019]
[0020] In the above formula, These are the weighting coefficients. These are the overall substation level, risk distribution, and weak links dimensions. This is the predicted value of the average resistance. This is the overall baseline value when the substation grounding grid is put into operation. This is a warning value according to the procedure. This is the predicted value for the maximum resistance.
[0021] Furthermore, based on the assessment results of the comprehensive assessment model, a corresponding risk level indication is output, specifically including: If the assessment result is less than the first value, it indicates that the overall condition is good, suggesting that regular periodic inspections are sufficient. If the assessment result is between the first and second values, it indicates that preliminary signs of deterioration have been shown, suggesting that the next general testing cycle should be shortened and the tracking of deterioration points should be strengthened. If the assessment result is between the second and third values, it indicates that the overall deterioration trend has been established, suggesting that a special maintenance plan for the grounding grid within the station should be formulated, and the nodes with the highest risk should be addressed first. If the assessment result is greater than the third value, it indicates a serious decline in reliability and a significant safety risk, suggesting that a comprehensive diagnosis and renovation plan must be initiated immediately.
[0022] A grounding device continuity state degradation trend early warning system, the system being applied to a grounding device continuity state degradation trend early warning method, the system comprising: Host module and mobile terminal; The host module includes a power module, a boost module, a 4-channel acquisition unit, a resistance calculation unit, a motherboard, a sensor, an error self-compensation module, a display module, a communication module, and a storage module; The power supply module supplies power to the host computer; the boost module generates the DC or AC voltage required for testing; the 4-channel acquisition unit acquires resistance signals from each channel; the resistance calculation unit calculates the on-resistance based on Ohm's law; the motherboard schedules the various functional modules to work collaboratively according to a preset program or user instructions; the sensor acquires external environmental information; the error self-compensation module dynamically corrects the original resistance data using a locally deployed lightweight neural network model; the communication module enables wireless communication between the host module and the mobile terminal; the storage module stores test data locally; the mobile terminal enables human-computer interaction via an app; and the display module displays test data from test points or allows online viewing of test reports.
[0023] Furthermore, the 4-channel acquisition unit includes an AC conversion circuit, a multi-channel filter, and an MCU, wherein the AC conversion circuit is used to convert analog signals and digital signals; the multi-channel filter is used to filter noise; and the MCU is used to control the on / off state of each acquisition channel.
[0024] Compared with the prior art, the advantages of the present invention are as follows: This invention achieves grounding impedance identification without the need for full excavation or additional grounding test terminals by path-based measurement of the conduction impedance between each down conductor measurement point of the grounding grid, reducing the difficulty of testing and maintenance costs. By calculating the actual grounding impedance at each measurement point through conduction impedance and extracting historical data for fitting and modeling over time, it accurately reflects the deterioration trend of the grounding grid performance over time, avoiding random errors caused by single-point testing and improving the reliability of the evaluation results. Furthermore, by constructing a comprehensive evaluation model based on resistance prediction results and outputting risk levels, it provides early warning of potential deterioration hazards, guiding maintenance personnel to conduct targeted repairs, and significantly improving the operational safety and maintenance efficiency of the grounding grid. Attached Figure Description
[0025] Figure 1 This is a flowchart of a grounding device conduction state degradation trend early warning method according to an embodiment of the present invention.
[0026] Figure 2 This is a schematic diagram for testing the DC resistance of the conduction impedance between pairs of multiple grounding leads in a grounding grid.
[0027] Figure 3 This is a schematic diagram of single-point grounding impedance decoupling based on topological relationships.
[0028] Figure 4 This is a framework diagram of the early warning system for the deterioration trend of the conduction state of the grounding device according to an embodiment of the present invention. Detailed Implementation
[0029] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.
[0030] Example 1 like Figure 1 As shown, the grounding device conduction state degradation trend early warning method of this embodiment includes the following steps: S1, measure the continuity impedance between pairs of multiple grounding lead test points in the grounding grid according to the preset test path; S2, calculate the grounding impedance value of each grounding down conductor measurement point based on the multiple measured conduction impedance values obtained; S3. Extract the grounding impedance value of each grounding down conductor measuring point in chronological order to form a historical data sequence of grounding impedance for each measuring point. Fit the sequence to establish a deterioration trend model of grounding impedance over time. Based on the deterioration trend model, predict the resistance value of each grounding down conductor measuring point in the future. S4. Construct a comprehensive evaluation model based on the predicted resistance value, and output the corresponding risk level prompt according to the range of the evaluation results of the comprehensive evaluation model.
[0031] In a specific application embodiment, step S1 specifically involves data acquisition and structured storage, and the steps are as follows: Using a portable grounding continuity tester, the continuity impedance between pairs of multiple grounding leads in the grounding grid is measured according to a preset test path (e.g., ...). Figure 2 As shown), using point 1 as a reference point, the grounding continuity impedance of points 2, 3, 4, ..., N is measured sequentially (e.g. Figure 3 As shown in the figure, they are respectively denoted as R21, R31, R41, ..., RN1. The grounding conduction impedance of point 3 is measured with point 2 as the reference point and denoted as R32. Each measurement data includes the test point pair identification, impedance value, test time, ambient temperature and humidity, and is uploaded to the data center to form a structured historical database.
[0032] In this embodiment, step S2 (single-point grounding impedance decoupling calculation based on topology) includes: Establish a topological connection model of the grounding grid, and regard the entire grounding grid as a network consisting of nodes and multiple branches, where nodes represent grounding down conductor measurement points and branches represent connecting conductors and grounding resistance between measurement points; A mathematical model based on the power grid topology is constructed using the multiple conduction impedance measurements (i.e., the total branch impedance) obtained in step S1. The grounding impedance value of each grounding down conductor measurement point is calculated. For example, the impedance value of point 1 is: The impedance value at point i is: ; The system of computational equations expressed in matrix form is as follows:
[0033] In the above formula, These are the grounding impedance values at the first, second, ..., and Nth grounding lead-down test points, respectively. The grounding continuity impedance of the second, third, ..., Nth measuring points is measured sequentially, with the first grounding lead measuring point as the reference point. If there are too many points in the impedance system, the Newton-Raphson algorithm can be used to solve the equation system. The measured branch-level conduction impedance representing each line can be calculated and separated into the node-level grounding impedance represented by each grounding down conductor measurement point, thus realizing the transformation from "loop characteristics" to "point characteristics".
[0034] In this embodiment, step S3 (construction and trend modeling of single-point grounding impedance historical sequence) includes: For each independent grounding point calculated, its grounding impedance value is extracted in chronological order to form a historical data sequence of grounding impedance for that point. For each point's historical data sequence, a time series analysis algorithm (such as linear regression, exponential smoothing, or ARIMA model) is used to fit the data, establishing a model of the grounding impedance's degradation trend over time, and calculating trend indicators such as the annual degradation rate. The decoupled calculated "node-level" grounding impedances are denoted as... , which represents the test value of the i-th node in the t-th test (tested once a year).
[0035] After the substation grounding grid is put into operation, the nodal-level grounding impedance data of each grounding lead measuring point will be collected. , … Preprocessing is performed, in which Given the grounding continuity test value of the i-th node in year T after commissioning, a linear regression method is used to reduce the grounding impedance data to the test value of the month in which the commissioning date occurred. To obtain the test value of the i-th node after the t-th test in production, which is attributed to the month of the production launch date. The data set of the i-th node-level grounding impedance after the substation grounding network is put into operation is [ , … The following is the formula for reducing the grounding impedance data to the test value of the month in which the commissioning date occurred:
[0036] In the above formula, , These are the month numbers corresponding to the t-th and t-1-th tests, respectively. , , These are the test values of the i-th grounding down conductor test point after the t-th, t-1-th, and initial tests, respectively, reverting to the month of the commissioning date. , These are the test values and initial test values of the i-th grounding lead test point at the t-th test, respectively.
[0037] The ADF stationarity test is performed on the sequence constructed from the grounding impedance dataset. If the test value p meets the requirement (p<0.05), the sequence is considered stationary; otherwise, the sequence value is differentially processed, which may require multiple differentials, until the ADF test shows that the test value p meets the requirement (p<0.05). The stationary sequence is then used as the subsequent test object.
[0038] After passing the test, the partial autocorrelation function (PACF, used to initially determine the p-value of the grounding impedance dataset) of the stationary sequence is plotted, and a degradation trend model of grounding impedance over time is constructed. The coefficients of the degradation trend AR(p) model are estimated using the maximum likelihood estimation method. At this point, the model is constructed as follows:
[0039] In the above formula, Let be the white noise from the t-th test. , , ... c represents the test value of the i-th grounding down conductor test point after the t-th, t-1-th, t-2-th, ..., tp-th tests, which is then reverted to the month of the commissioning date. , … The values to be fitted are denoted as .
[0040] The predicted resistance value of each grounding down conductor measurement point is calculated using the degradation trend model, and the average predicted resistance value, degradation rate, and maximum predicted resistance value are calculated based on each predicted resistance value.
[0041] Specifically, the AR(p) model is used to predict the grounding impedance data at the i-th node level or the incremental grounding impedance data at the i-th node level. Using the AR(p) model prediction results from all key monitoring points (e.g., predicting the resistance value in the nth year), the average predicted resistance value is calculated as follows:
[0042] In the above formula, Let be the predicted resistance value at the i-th grounding down conductor measuring point, and N be the total number of grounding down conductor measuring points. The average predicted resistance value reflects the average level of the substation grounding grid conduction performance, and an upward trend in this value directly indicates a decline in overall performance.
[0043] Calculate the resistance exceedance rate / deterioration rate: ; The degradation rate reflects the proportion of "problem points" in the system. An increase in the degradation rate means that the problem is spreading from local to global.
[0044] The maximum predicted resistance value is:
[0045] In the above formula, These are the predicted resistance values for the first, second, ..., Nth grounding lead measurement points, respectively. The maximum predicted resistance value is used to identify the weakest link in the system, as the overall risk is often determined by the weakest point; this indicator is crucial for risk management.
[0046] In this embodiment, the expression of the comprehensive evaluation model is as follows:
[0047]
[0048]
[0049] In the above formula, These are the weighting coefficients. These are the overall substation level, risk distribution, and weak links dimensions. This is the predicted value of the average resistance. This is the overall baseline value when the substation grounding grid is put into operation. This is a warning value according to the procedure. This represents the predicted maximum resistance. A weighted average of the scores from the three dimensions yields a comprehensive degradation index between 0 and 1, preferably a=0.4, b=0.3, and c=0.3.
[0050] In this embodiment, a corresponding risk level alert (intelligent early warning based on dynamic thresholds and lifespan prediction) is output according to the range of the assessment results of the comprehensive assessment model. Specifically, this includes setting a three-level early warning mechanism: Health (blue): If the assessment result (overall degradation index) is less than the first value (e.g., less than 0.3), the characteristics are: low average resistance, degradation rate of 0, and maximum resistance is much lower than the warning value. This indicates that the overall condition is good and suggests that regular periodic inspections are sufficient. Attention (yellow): If the evaluation result is between the first and second values (e.g., between 0.3 and 0.6), the characteristics are: the average resistance begins to rise slowly, the degradation rate appears but is low (e.g., <10%), and the maximum resistance is close to the attention value. This indicates that preliminary signs of degradation have been shown, suggesting that the next general testing cycle should be shortened and the tracking of degradation points should be strengthened. Warning (orange): If the assessment result is between the second and third values (e.g., between 0.6 and 0.8), the characteristics are: the average resistance has increased significantly, the degradation rate is high (e.g., 10%-30%), and the maximum resistance may have exceeded the warning value. This indicates that the overall degradation trend has been established, and it is suggested that a special maintenance plan for the grounding grid in the station should be formulated, and the nodes with the highest risk should be dealt with first. Severe (Red): If the assessment result is greater than the third value (e.g., greater than 0.8), the characteristics are: high average resistance, large degradation rate (>30%), and multiple nodes close to or exceeding the warning value. This indicates a serious decline in reliability and a major safety risk, suggesting that a comprehensive diagnosis (such as excavation inspection, high current testing) and modification plan must be initiated immediately.
[0051] Compared with the prior art, the beneficial effects of this embodiment are as follows: (1) An innovative decoupling analysis method of “conduction impedance-single-point grounding impedance” was proposed, which breaks through the limitation of existing technology that can only measure loop impedance. By establishing a grounding network topology model and solving the equation system based on multi-source measurement data, the microscopic deconstruction of macroscopic conduction impedance was realized, and the specific grounding point whose performance deteriorated was accurately located. This provides a clear target for condition-based maintenance and upgrades the traditional “surface” assessment to “point” monitoring. (2) It realizes a paradigm shift from "static threshold judgment" to "dynamic trend early warning". It not only focuses on the absolute value of a single measurement result, but also emphasizes the in-depth mining of the historical data sequence of single-point grounding impedance. By establishing a degradation trend model and introducing the rate of change threshold and lifetime prediction, it realizes early and advanced early warning of latent faults of grounding devices. This makes the operation and maintenance strategy change from "post-event maintenance" to "predictive maintenance", which can effectively avoid safety accidents caused by sudden failure of grounding devices and significantly improve the active safety and economy of power grid operation.
[0052] Example 2 like Figure 4 As shown, the grounding device conduction status deterioration trend early warning system of this embodiment is applied to the method described in Embodiment 1. The system includes: Host module and mobile terminal; The host module includes a power module, a boost module, a 4-channel acquisition unit, a resistance calculation unit, a motherboard, a sensor, an error self-compensation module, a display module, a communication module, and a storage module; The power module supplies power to the host computer; The boost module includes a charge / discharge management module and a protection module, used to generate the DC or AC voltage required for testing; The 4-channel acquisition unit is used to acquire the resistance signal of each channel; The resistance calculation unit includes a DC current source, a high-precision voltmeter, and an ammeter, and is used to calculate the on-resistance according to Ohm's law. The motherboard is used to schedule the various functional modules to work together according to a preset program or user instructions; The sensor is used to collect external environmental information, such as temperature, humidity, and electromagnetic field strength. The error self-compensation module dynamically corrects the original resistance data through a locally deployed lightweight neural network model. The communication module is used for wireless communication between the host module and the mobile terminal, and supports Bluetooth and WiFi. The storage module has a built-in Flash memory for local storage of test data; The display module is located inside the display screen, which is used to display the resistance value, current value, and test status of four test points, and can also view the test report online. The mobile terminal is used for human-computer interaction via an APP; the display module is used to display test data of test points or view test reports online, and its functions include test parameter setting, test sequence generation, real-time data viewing, anomaly alarm, historical data storage and query, data trend analysis, automatic report generation, and data export; the test parameter setting includes reference point, test point, and injection current level; the test sequence generation supports importing substation test point model templates; the automatic report generation function supports Word / PDF format; and the data export function outputs Excel.
[0053] In this embodiment, one end of the 4-channel acquisition unit includes an AC conversion circuit, a multi-channel filter, and an MCU. The AC conversion circuit is used to convert analog signals to digital signals; the multi-channel filter is used to filter noise; and the MCU is used to control the on / off state of each acquisition channel.
[0054] The system of the present invention corresponds to the method described above and has the same advantages as the method described above.
[0055] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. Computer-readable media include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0056] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A method for early warning of the deterioration trend of the conduction state of a grounding device, characterized in that, Including the following steps: S1, measure the continuity impedance between pairs of multiple grounding lead test points in the grounding grid according to the preset test path; S2, calculate the grounding impedance value of each grounding down conductor measurement point based on the multiple measured conduction impedance values obtained; S3. Extract the grounding impedance value of each grounding down conductor measuring point in chronological order to form a historical data sequence of grounding impedance for each measuring point. Fit the sequence to establish a deterioration trend model of grounding impedance over time. Based on the deterioration trend model, predict the resistance value of each grounding down conductor measuring point in the future. S4. Construct a comprehensive evaluation model based on the predicted resistance value, and output the corresponding risk level prompt according to the range of the evaluation results of the comprehensive evaluation model.
2. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 1, characterized in that, Step S2 includes: Establish a topological connection model of the grounding grid, and regard the entire grounding grid as a network consisting of nodes and multiple branches, where nodes represent grounding down conductor measurement points and branches represent connecting conductors and grounding resistance between measurement points; A mathematical model based on the power grid topology is constructed using multiple measured conduction impedance values. The grounding impedance value at each grounding down conductor measurement point is calculated, and the calculation equations are as follows: In the above formula, These are the grounding impedance values at the first, second, ..., and Nth grounding lead-down test points, respectively. The grounding continuity impedance of the second, third, ..., Nth measuring points is measured sequentially, with the first grounding lead measuring point as the reference point. The Newton-Raphson algorithm is used to solve the equations. The measured branch-level conduction impedance of each branch is calculated and separated into the node-level grounding impedance represented by each grounding down conductor measurement point.
3. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 1, characterized in that, Step S3 includes: After the substation grounding grid is put into operation, the nodal-level grounding impedance data of each grounding down conductor measuring point is preprocessed, and the grounding impedance data is reduced to the test value of the month in which the commissioning date is used by linear regression method to construct a grounding impedance dataset. The stationarity test is performed on the sequence constructed from the grounding impedance dataset. After the test is passed, the partial autocorrelation function plot of the sequence is plotted and a degradation trend model of grounding impedance over time is constructed. The coefficients of the degradation trend model are estimated using the maximum likelihood estimation method. The predicted resistance value of each grounding down conductor measurement point is calculated using the degradation trend model, and the average predicted resistance value, degradation rate, and maximum predicted resistance value are calculated based on each predicted resistance value.
4. The method for early warning of the deterioration trend of the conduction state of the grounding device according to claim 3, characterized in that, The calculation formula for reducing the grounding impedance data to the test value of the month in which the commissioning date falls is as follows: In the above formula, , These are the month numbers corresponding to the t-th and t-1-th tests, respectively. , , These are the test values of the i-th grounding down conductor test point after the t-th, t-1-th, and initial tests, respectively, reverting to the month of the commissioning date. , These are the test values and initial test values of the i-th grounding lead test point at the t-th test, respectively.
5. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 3, characterized in that, The expression for constructing the model of the degradation trend of grounding impedance over time is as follows: In the above formula, Let be the white noise from the t-th test. , , ... c represents the test value of the i-th grounding down conductor test point after the t-th, t-1-th, t-2-th, ..., tp-th tests, which is then reverted to the month of the commissioning date. , … The values to be fitted are denoted as .
6. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 3, characterized in that, The calculation formulas for the average predicted resistance, degradation rate, and maximum predicted resistance based on the predicted resistance values are as follows: The predicted average resistance value is: In the above formula, Let N be the predicted resistance value of the i-th grounding down conductor measuring point, and N be the total number of grounding down conductor measuring points. ； The maximum predicted resistance value is: In the above formula, These are the predicted resistance values for the first, second, ..., Nth grounding lead measuring points, respectively.
7. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 1, characterized in that, The expression for the comprehensive evaluation model is as follows: In the above formula, These are the weighting coefficients. These are the overall substation level, risk distribution, and weak links dimensions. This is the predicted value of the average resistance. This is the overall baseline value when the substation grounding grid is put into operation. This is a warning value according to the procedure. This is the predicted value for the maximum resistance.
8. The method for early warning of the deterioration trend of the conduction state of a grounding device according to claim 1, characterized in that, Based on the assessment results of the comprehensive assessment model, a corresponding risk level indication is output, specifically including: If the assessment result is less than the first value, it indicates that the overall condition is good, suggesting that regular periodic inspections are sufficient. If the assessment result is between the first and second values, it indicates that preliminary signs of deterioration have been shown, suggesting that the next general testing cycle should be shortened and the tracking of deterioration points should be strengthened. If the assessment result is between the second and third values, it indicates that the overall deterioration trend has been established, suggesting that a special maintenance plan for the grounding grid within the station should be formulated, and the nodes with the highest risk should be addressed first. If the assessment result is greater than the third value, it indicates a serious decline in reliability and a significant safety risk, suggesting that a comprehensive diagnosis and renovation plan must be initiated immediately.
9. A grounding device conduction state deterioration trend early warning system, said system being applied to the method described in any one of claims 1-8, characterized in that, The system includes: Host module and mobile terminal; The host module includes a power module, a boost module, a 4-channel acquisition unit, a resistance calculation unit, a motherboard, a sensor, an error self-compensation module, a display module, a communication module, and a storage module; The power supply module supplies power to the host computer; the boost module generates the DC or AC voltage required for testing; the 4-channel acquisition unit acquires resistance signals from each channel; the resistance calculation unit calculates the on-resistance based on Ohm's law; the motherboard schedules the various functional modules to work collaboratively according to a preset program or user instructions; the sensor acquires external environmental information; the error self-compensation module dynamically corrects the original resistance data using a locally deployed lightweight neural network model; the communication module enables wireless communication between the host module and the mobile terminal; the storage module stores test data locally; the mobile terminal enables human-computer interaction via an app; and the display module displays test data from test points or allows online viewing of test reports.
10. The early warning system for the deterioration trend of the conduction state of the grounding device according to claim 9, characterized in that, The 4-channel acquisition unit includes an AC conversion circuit, a multi-channel filter, and an MCU. The AC conversion circuit is used to convert analog signals to digital signals; the multi-channel filter is used to filter noise; and the MCU is used to control the on / off state of each acquisition channel.