Substation grounding grid health state assessment and early warning method based on multi-source data fusion

Abstract

The invention relates to the technical field of power equipment detection, and particularly discloses a substation grounding grid health state evaluation and early warning method based on multi-source data fusion, which comprises the following steps: gridding a grounding grid region, measuring grounding resistance and environmental parameters of each measurement region, and calculating a corrected resistance growth rate time sequence; the soil resistivity, the PH value and the chloride ion content are detected, and the soil corrosivity score is evaluated; according to the resistance growth rate time sequence and the soil corrosivity score, identifying a resistance suddenly-increased and strongly-corroded region, and marking a corrosion suspicious region; testing current is injected into nodes below the suspicious area, voltage response is measured, branch resistance parameters are inversely calculated through an optimization algorithm, the corrosion anomaly degree is calculated, and a branch troubleshooting priority sequence is generated. According to the method, the grounding resistance dynamic change and the soil corrosion static evaluation are fused, preliminary identification and dynamic early warning of the corrosion risk area are realized, the troubleshooting range is narrowed, and the inversion precision and the diagnosis efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of power equipment testing technology, and specifically to a method for assessing and warning the health status of substation grounding grids based on multi-source data fusion. Background Technology

[0002] As a key component for the safe operation of substations, the health status of the grounding grid directly affects the stability of the power system and the safety of personnel and equipment. However, because the grounding grid is buried deep underground, has a complex structure, and cannot be directly observed, it is a typical "black box system," making the assessment and monitoring of its health status a challenging technical problem.

[0003] Currently, non-destructive testing methods for grounding grids mainly rely on mathematical inversion of ground measurement data to achieve assessments under non-excavation, online, or energized conditions.

[0004] In the prior art, such as the corrosion diagnosis method for substation grounding grid disclosed in CN105445615A, although a corrosion diagnosis method based on a simplified network circuit model and Tellegen's theorem is proposed, there are still obvious shortcomings: First, the method does not perform preliminary identification and range reduction of the corrosion area before mathematical inversion, resulting in high computational complexity and strong non-uniqueness of the solution in the inversion process, which affects the accuracy and efficiency of diagnosis.

[0005] Secondly, its assessment dimension is singular, relying solely on electrical measurement data for inversion, without comprehensively considering the changing trend of grounding resistance and multi-source information such as soil corrosivity. It cannot fully and dynamically reflect the true corrosion risk of the grounding grid, especially in areas with strong soil corrosivity and rapid resistance growth, where it is prone to missed or misjudgment.

[0006] Therefore, there is an urgent need for a method that can integrate multi-source data, dynamically assess the health status of the grounding grid, and achieve accurate early warning, so as to improve the reliability, efficiency, and adaptability of grounding grid corrosion diagnosis. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a substation grounding grid health status assessment and early warning method based on multi-source data fusion. The specific technical solution is as follows: The substation grounding grid health status assessment and early warning method based on multi-source data fusion includes the following steps: Step S1: Divide the grounding grid area into multiple measurement zones, measure the grounding resistance and environmental parameters of each measurement zone in the current cycle, retrieve historical cycle measurement data, calculate the resistance growth rate based on the difference in grounding resistance between adjacent cycles, and correct it in conjunction with environmental parameters to obtain the time sequence of the resistance growth rate of each measurement zone.

[0008] Step S2: Set up soil sampling points in the test area, test the soil resistivity, pH value and chloride ion content of the soil samples at the sampling points, and evaluate the soil corrosivity score of each test area.

[0009] Step S3: Based on the time series of resistance growth rate and soil corrosivity score of each test area, identify whether there are areas with sudden increase in resistance and strong corrosion. If so, determine that the ground grid is suspected of corrosion and mark the suspected corrosion area. If not, continue to the next cycle of health monitoring.

[0010] Step S4: Inject test current into multiple accessible nodes of the grounding network below the suspected corrosion area and measure the node voltage response. Use an optimization algorithm to inversely calculate the resistance parameters of the grounding network branches, and calculate the corrosion anomaly degree of each branch accordingly to generate a branch investigation priority sequence.

[0011] Compared with existing technologies, the substation grounding grid health status assessment and early warning method based on multi-source data fusion described in this invention has the following beneficial effects: 1. By gridding the grounding grid area, comprehensively measuring grounding resistance and environmental parameters, calculating the corrected resistance growth rate time series, and combining it with soil corrosivity scoring, this invention identifies areas with sudden increases in resistance and strong corrosion. Thus, before entering the complex mathematical inversion, it can initially delineate areas of suspected corrosion, significantly narrowing the scope of subsequent refined detection, reducing the difficulty and non-uniqueness of inversion calculation, and improving the overall diagnostic efficiency.

[0012] 2. This invention not only considers the static evaluation of soil corrosivity but also incorporates the dynamic trend of grounding resistance growth, and integrates the two through a benchmark score reduction mechanism. When the resistance growth rate is abnormally high, the system can dynamically lower the strong corrosion score threshold, making the early warning mechanism more flexible and adaptable. This avoids missing high-risk, rapidly developing areas due to soil corrosion scores not reaching the conventional threshold, achieving more timely and accurate corrosion early warning.

[0013] 3. After identifying suspected corrosion areas, this invention further injects test current into accessible nodes, uses an optimized algorithm to inversely calculate the resistance parameters of each branch, and calculates its corrosion anomaly degree, generating a branch inspection priority sequence. This method can not only locate corrosion areas but also assess the degree of corrosion and provide targeted maintenance guidance, helping maintenance personnel to prioritize high-risk branches and improve maintenance efficiency and system security. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0016] Figure 2 This is a flowchart of the overall method of the present invention.

[0017] Figure 3 This is a flowchart for generating the resistance growth rate timing of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the substation grounding grid health status assessment and early warning method based on multi-source data fusion proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The specific scheme of the substation grounding grid health status assessment and early warning method based on multi-source data fusion provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0021] This invention integrates dynamic changes in grounding resistance with static evaluation of soil corrosion to achieve preliminary identification and dynamic early warning of corrosion risk areas, narrowing the scope of investigation and improving inversion accuracy and diagnostic efficiency. Specifically, the method first integrates the environmentally corrected periodic grounding resistance growth trend with soil corrosivity information to initially locate suspected corrosion areas. Then, within the suspected corrosion areas, refined diagnosis is performed through electrical testing and model inversion. By assessing the degree of branch corrosion anomaly, an investigation priority sequence is generated, achieving early warning and accurate location of potential grounding grid corrosion hazards.

[0022] Please see Figure 1 and Figure 2 As shown, the substation grounding grid health status assessment and early warning method based on multi-source data fusion provided by this invention includes the following steps: In one embodiment of this invention, to achieve accurate assessment and early warning of the substation grounding grid health status, the grounding grid area first needs to be systematically divided into grids, dividing the grounding grid area into multiple measurement zones, thereby achieving refined measurement of grounding resistance and environmental parameters in the spatial dimension, laying the foundation for subsequent health status analysis based on multi-source data fusion. During each monitoring cycle, grounding resistance and environmental parameter data of each measurement zone are synchronously collected, and combined with historical cycle measurement data, a time series reflecting the changing trend of grounding resistance is constructed, thereby achieving dynamic tracking of the evolution of the grounding grid status.

[0023] S1. Grid-based monitoring area division and resistance growth rate time series acquisition, specifically including the following steps: S1-1. Divide the ground grid area into multiple monitoring areas.

[0024] S1-2. Measure the grounding resistance and environmental parameters of each measurement area in the current cycle. The environmental parameters include soil surface temperature and soil moisture.

[0025] It should be noted that the same grid division method for the grounding grid area is used in each monitoring cycle to maintain spatial consistency of the monitoring data. In addition, the same type of measuring instrument, such as a grounding resistance tester, is used each time the grounding resistance of the grounding grid area is measured periodically, and the wiring direction, wiring distance, and burial depth of the measuring electrodes are kept completely consistent in each monitoring cycle, thereby minimizing errors introduced by differences in measurement conditions.

[0026] S1-3. Retrieve the corresponding measurement data for each historical period from the database.

[0027] S1-4. Calculate the resistance growth rate based on the difference in grounding resistance between adjacent periods, and correct it with environmental parameters to obtain the time series of resistance growth rates for each measurement area. (See reference...) Figure 3 As shown, the method for obtaining the resistance growth rate time series of the test area includes the following steps: S1-4-1, calculate the grounding resistance increment of the test area in the current cycle, wherein the grounding resistance increment is the difference obtained by subtracting the grounding resistance value of the previous cycle from the grounding resistance value of the current cycle.

[0028] It should be noted that the grounding resistance increment is a signed value: if the sign is positive, it means that the grounding resistance in the current cycle is increasing compared to the previous cycle, that is, the grounding resistance is increasing; if the sign is negative, it means that the grounding resistance in the current cycle is decreasing compared to the previous cycle.

[0029] S1-4-2. Based on the environmental parameters of the test area in the current cycle and the previous cycle, calculate the change in environmental parameters and input the change in environmental parameters into the preset environmental parameter-grounding resistance influence model to obtain the interference amount of the grounding resistance increment. The construction method of the environmental parameter-grounding resistance influence model is as follows: S1-4-2-1. Select one or more test areas in the grounding grid area.

[0030] S1-4-2-2. Based on the principle of a single variable, the soil surface temperature of the experimental area was changed in an equal gradient manner, and the grounding resistance of the experimental area was measured at different soil surface temperatures. Multiple sets of data on the changes in soil surface temperature and the corresponding changes in grounding resistance were obtained. All changes are signed values.

[0031] S1-4-2-3. Based on multiple sets of data, construct a test set and use machine learning algorithms to establish a mapping relationship between the change in soil surface temperature and the change in grounding resistance.

[0032] S1-4-2-4. Using the same single variable principle, establish the mapping relationship between the change in soil moisture and the change in grounding resistance.

[0033] S1-4-2-5. Based on the established mapping relationship between the changes in soil surface temperature and soil moisture and the changes in grounding resistance, an environmental parameter-grounding resistance influence model is constructed. The model takes the changes in soil surface temperature and soil moisture as inputs and the cumulative changes in grounding resistance as outputs.

[0034] In one specific embodiment of the present invention, a model can be established based on historical monitoring data using multiple linear regression, with the input being the change in soil surface temperature. and changes in soil moisture The output is the change in grounding resistance. The model formula can be expressed as: ,in The coefficients are obtained by fitting using the least squares method.

[0035] S1-4-3. Subtract the interference amount of the grounding resistance increment from the grounding resistance increment of the test area in the current cycle to obtain the corrected grounding resistance increment.

[0036] S1-4-4 Calculate the ratio of the corrected grounding resistance increment to the grounding resistance value of the previous cycle to obtain the resistance growth rate of the test area in the current cycle.

[0037] S1-4-5. Based on the same calculation method, obtain the resistance growth rate of the test area in each historical period.

[0038] S1-4-6. Arrange the resistance growth rates of all historical cycles and the current cycle in chronological order to generate a time series sequence of the resistance growth rates of the survey area.

[0039] Considering that soil corrosivity is a key environmental factor affecting the corrosion and deterioration of grounding grid conductors, after constructing the time series of grounding resistance growth rates in each survey area, it is necessary to further assess the corrosion risk of the soil environment where the grounding grid is located. By setting up soil sampling points and conducting soil parameter tests in each survey area, key indicators reflecting soil corrosivity are obtained, providing a soil-based basis for subsequent comprehensive identification of potentially corroded areas.

[0040] S2. Soil corrosivity assessment in the test area, specifically including the following steps: S2-1. Soil sampling points are set up in the test area. The specific method is as follows: the center point of the grid in the test area and its four corner points are determined as the soil sampling locations.

[0041] Obtain the burial depth of the grounding grid conductor, set an upward distance based on the burial depth, and determine the corresponding depth value as the soil sampling depth.

[0042] Based on the determined sampling locations and depths, multiple soil sampling points are set up in each survey area.

[0043] As a specific implementation method, the soil resistivity at each soil sampling point can be measured using the four-electrode Wenner method, and soil samples can be collected at a depth of 1-2 meters underground (i.e., near the burial depth of the grounding grid conductor) using a soil sampler. The collected soil samples are then sent to the laboratory for pH value and chloride ion content determination.

[0044] S2-2. Detect the soil resistivity, pH value and chloride ion content of soil samples at the sampling points, and evaluate the soil corrosivity score of each test area. The method for evaluating the soil corrosivity score of the test area includes the following steps: Based on the soil corrosivity scoring rules stored in the database, obtain the corrosivity scores corresponding to different soil resistivity ranges, different pH value ranges and different chloride ion content ranges.

[0045] For each soil sample taken from a sampling point in the survey area, based on the measured soil resistivity, pH value, and chloride ion content, corresponding corrosion scores are matched and accumulated to obtain the soil corrosion score for each sampling point.

[0046] The average soil corrosivity score of the survey area was calculated by taking the average of the soil corrosivity scores at all sampling points within the survey area.

[0047] After obtaining the resistance growth rate time series and soil corrosivity score of each test area, the electrical performance change trend and environmental corrosion intensity can be combined to identify whether there are areas that simultaneously show a sudden increase in resistance and strong corrosion characteristics. This allows for a preliminary determination of whether there are suspected corrosion areas in the grounding network and whether further refined diagnosis needs to be initiated.

[0048] S3. Identification and judgment of suspected corrosion areas, specifically including the following steps: S3-1. Based on the resistance growth rate time series and soil corrosion score of each test area, identify whether there is a sudden increase in resistance and strong corrosion area. The specific methods include: S3-1-1. Based on the resistance growth rate time series and soil corrosion score of each test area, determine whether each test area meets the following conditions at the same time: (1) There is a continuous subsequence with a value greater than zero in the resistance growth rate time series, and the subsequence as a whole shows a monotonically increasing trend, wherein the length of the continuous subsequence is greater than or equal to 2.

[0049] (2) The soil corrosivity score is greater than or equal to the preset score threshold corresponding to the strong corrosion level. The method for determining the score threshold corresponding to the strong corrosion level is to extract the benchmark score corresponding to the strong corrosion level from the database.

[0050] Traverse the continuous subsequences with values ​​greater than zero and filter out the peak value of the resistance growth rate in the subsequence.

[0051] If the peak resistance growth rate is less than or equal to the preset upper limit of the resistance growth rate, the benchmark score will be directly determined as the score threshold corresponding to the strong corrosion level.

[0052] Conversely, the excess amount of the resistance growth rate is calculated, and the reduction amount of the benchmark score is calculated based on the preset reduction amount of the strong corrosion benchmark score corresponding to the unit excess amount. The reduction amount is then subtracted from the benchmark score to obtain the score threshold corresponding to the strong corrosion level.

[0053] It should be noted that, under conditions of extremely high resistance growth rate, even if the soil corrosivity score does not reach the conventional strong corrosion threshold, the rapid deterioration of its electrical performance itself constitutes a sufficient and adequate early warning trigger condition. Strictly adhering to the original soil corrosivity score threshold may cause the system to overlook such high-risk, rapidly developing defect areas, delaying the response window. Therefore, by introducing a benchmark score adjustment mechanism, dynamically linking the score threshold with the actual resistance growth rate, the static evaluation of soil corrosivity is essentially integrated and weighted with the dynamic trend of grounding resistance changes, making the early warning standard more adaptable.

[0054] S3-1-2. If at least one test area meets the above conditions, it is determined that there is a region with a sudden increase in resistance and strong corrosion. The test areas that meet the conditions are spliced ​​together to determine the suspected corrosion area.

[0055] Otherwise, it is determined that there is no area with a sudden increase in resistance and strong corrosion.

[0056] It should be noted that in a continuous subsequence with values ​​greater than zero, each item has a value greater than zero.

[0057] S3-2. If it exists, the grounding network is suspected of corrosion, and the suspected corrosion area is marked. If it does not exist, the next cycle of health monitoring will continue.

[0058] After calculating the corrosion anomaly of each branch of the grounding grid below the suspected corrosion area, the branches can be sorted according to the anomaly value to generate a branch investigation priority sequence, thereby guiding maintenance personnel to carry out grounding grid corrosion investigation and repair work efficiently and in an orderly manner.

[0059] S4. Refined diagnosis of grounding grid branch corrosion, specifically including the following steps: S4-1. Inject test current into multiple accessible nodes of the grounding grid below the suspected corrosion area and measure the node voltage response. Use optimization algorithms to inversely calculate the resistance parameters of the grounding grid branches, and calculate the corrosion anomaly degree of each branch accordingly. Specifically including the following steps: S4-1-1. Establish a resistance network model: Based on the grounding grid design drawings, establish a resistance network model corresponding to the grounding grid structure below the suspected corrosion area, and identify all nodes and branches in the model.

[0060] It should be noted that in the resistor network model, the intersection point between the conductors of the grounding grid is defined as a node, and the continuous conductor segment between two adjacent nodes is defined as a branch.

[0061] S4-1-2. Setting up tests and measurements: Select multiple nodes from the nodes of the resistor network model that are connected to the grounding lead as accessible test ports.

[0062] Test current is injected between selected pairs of test ports, and the voltage drop between each pair of ports is measured simultaneously to obtain multiple sets of port voltage-current measurement data.

[0063] It should be noted that the injected test current can be either DC or low-frequency AC current. To improve the accuracy of the inversion calculation and provide more comprehensive and diverse data constraints, a multi-frequency, multi-excitation measurement strategy can be implemented, that is, using AC excitation currents of multiple frequencies as test currents to increase the quantity and diversity of measurement data.

[0064] S4-1-3, Model Parameter Inversion Iteration: Using multiple sets of port voltage-current measurement data as input, an optimization algorithm is adopted to iteratively adjust the resistance parameters of each branch in the resistor network model with the goal of minimizing the overall error between the predicted voltage drop between each selected port pair calculated by the resistor network model and the corresponding actual measurement value.

[0065] It should be noted that optimization algorithms include, but are not limited to, least squares and genetic algorithms.

[0066] S4-1-4 Convergence Judgment: Repeat step S4-3 until the total error is less than the preset convergence threshold or the number of iterations reaches the preset upper limit.

[0067] S4-1-5. Obtain the inversion results: After the iteration converges, output the optimal estimated values ​​of the resistance parameters of each branch in the resistive network model.

[0068] S4-1-6 Calculate the branch anomaly degree: For each branch, calculate the percentage of the difference between its optimal resistance estimate and the theoretical resistance value relative to the theoretical resistance value, and use this percentage as the corrosion anomaly degree of the branch.

[0069] As a specific implementation method, a genetic algorithm is used to invert the resistance parameters: the population size is set to 100, the crossover probability is 0.8, the mutation probability is 0.05, the upper limit of the number of iterations is 500, the convergence threshold is that the root mean square error between the predicted voltage and the actual voltage is less than 0.01V, and the initial resistance value of the branch is the theoretical resistance value.

[0070] It should be noted that the theoretical resistance value of each branch can be derived from the design theoretical value, historical baseline data, or statistical values ​​of uncorroded branches in the same area.

[0071] S4-2. Generate a branch corrosion investigation priority sequence. The specific method is as follows: Based on the corrosion anomaly value of each branch, sort all branches in descending order to generate a branch corrosion investigation priority sequence.

[0072] In one specific embodiment of the present invention, the location of each branch and its corresponding corrosion anomaly value are marked in the resistive network model corresponding to the suspected corrosion area, and a grounding grid corrosion state distribution map is generated.

[0073] In summary, this invention, by gridding the grounding network area, measures the grounding resistance and environmental parameters of each test area, and calculates the corrected resistance growth rate time series; it detects soil resistivity, pH value, and chloride ion content to assess soil corrosivity score; based on the resistance growth rate time series and soil corrosivity score, it identifies areas with sudden increases in resistance and strong corrosion, marking areas suspected of corrosion; it injects test current into nodes below the suspected areas, measures the voltage response, and uses an optimized algorithm to inversely deduce branch resistance parameters, calculate corrosion anomaly degree, and generate a branch investigation priority sequence. This invention integrates dynamic changes in grounding resistance with static evaluation of soil corrosion, achieving preliminary identification and dynamic early warning of corrosion risk areas, narrowing the investigation scope, improving inversion accuracy and diagnostic efficiency, and providing a scientific basis for the health status assessment and maintenance of the grounding network.

[0074] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0076] 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, in the form of a computer program product.

[0077] Those skilled in the art will recognize that the modules 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.

[0078] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0079] 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.

[0080] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing and warning the health status of substation grounding grids based on multi-source data fusion, characterized in that, Includes the following steps: The ground grid area is divided into multiple measurement zones. The grounding resistance and environmental parameters of each measurement zone in the current cycle are measured, and historical measurement data are retrieved. The resistance growth rate is calculated based on the difference in grounding resistance between adjacent cycles, and then corrected in combination with environmental parameters to obtain the time series of resistance growth rates for each measurement zone. Soil sampling points were set up in the test area to test the soil resistivity, pH value and chloride ion content of the soil samples at the sampling points, and to evaluate the soil corrosivity score of each test area. Based on the time series of resistivity growth rate and soil corrosivity score of each test area, identify whether there are areas with sudden increase in resistivity and strong corrosion. If so, the ground grid is suspected of corrosion and the suspected corrosion area is marked. If not, continue to carry out the next cycle of health monitoring. Test currents are injected into multiple accessible nodes of the grounding network below the suspected corrosion area, and the node voltage response is measured. The resistance parameters of the grounding network branches are inverted and calculated using an optimization algorithm. Based on this, the corrosion anomaly degree of each branch is calculated, and a branch investigation priority sequence is generated.

2. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 1, characterized in that: The environmental parameters include soil surface temperature and soil moisture.

3. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 2, characterized in that: The method for obtaining the resistance growth rate time series of the test area includes: Calculate the grounding resistance increment of the survey area in the current cycle, where the grounding resistance increment is the difference between the grounding resistance value of the current cycle and the grounding resistance value of the previous cycle. Based on the environmental parameters of the survey area in the current cycle and the previous cycle, calculate the change in environmental parameters, input the change in environmental parameters into the preset environmental parameter-grounding resistance influence model, and obtain the interference amount of the grounding resistance increment. The corrected grounding resistance increment is obtained by subtracting the interference amount of the grounding resistance increment from the grounding resistance increment of the test area in the current period. Calculate the ratio of the corrected grounding resistance increment to the grounding resistance value of the previous cycle to obtain the resistance growth rate of the test area in the current cycle; Based on the same calculation method, the resistance growth rate of the test area in each historical period was obtained; Arrange the resistance growth rates of all historical periods and the current period in chronological order to generate a time series sequence of the resistance growth rates of the survey area.

4. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 3, characterized in that: The method for constructing the environmental parameter-grounding resistance influence model includes: Select one or more experimental areas within the grounding grid region; Based on the principle of a single variable, the soil surface temperature of the experimental area was changed in an equal gradient manner, and the grounding resistance of the experimental area was measured at different soil surface temperatures. Multiple sets of data on the changes in soil surface temperature and the corresponding changes in grounding resistance were obtained. All changes are signed values. A test set was constructed based on multiple sets of data, and a mapping relationship between the change in soil surface temperature and the change in grounding resistance was established using machine learning algorithms. Using the same single variable principle, a mapping relationship between changes in soil moisture and changes in grounding resistance was established; Based on the established mapping relationship between changes in soil surface temperature and soil moisture and changes in grounding resistance, an environmental parameter-grounding resistance influence model is constructed. The model takes changes in soil surface temperature and soil moisture as inputs and the cumulative change in grounding resistance as output.

5. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 1, characterized in that: The method for setting up soil sampling points includes: The center point of the grid in the survey area and its four corner points are determined as the soil sampling locations; Obtain the burial depth of the grounding grid conductor, set the distance upward based on the burial depth, and determine the corresponding depth value as the soil sampling depth; Based on the determined sampling locations and depths, multiple soil sampling points are set up in each survey area.

6. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 1, characterized in that: The methods for evaluating soil corrosivity in the test area include: Based on the pre-stored soil corrosivity scoring rules in the database, the corrosion scores corresponding to different soil resistivity ranges, different pH value ranges, and different chloride ion content ranges are obtained. For each soil sample taken at a sampling point in the test area, based on the measured soil resistivity, pH value and chloride ion content, the corresponding corrosion scores are matched and accumulated to obtain the soil corrosion score at each sampling point. The average soil corrosivity score of the survey area was calculated by taking the average of the soil corrosivity scores at all sampling points within the survey area.

7. The method for assessing and warning the health status of substation grounding grids based on multi-source data fusion according to claim 1, characterized in that: The method for identifying the presence of areas with sudden increases in resistance and severe corrosion includes: Based on the time series of resistivity growth rate and soil corrosivity score of each survey area, determine whether each survey area simultaneously meets the following conditions: (1) There are continuous subsequences with values ​​greater than zero in the time series of resistance growth rate, and the subsequences as a whole show a monotonically increasing trend. The length of the continuous subsequences is greater than or equal to 2. (2) The soil corrosivity score is greater than or equal to the score threshold corresponding to the preset strong corrosion level; If at least one test area meets the above conditions, it is determined that there is a region with a sudden increase in resistance and strong corrosion, and the test areas that meet the conditions are spliced ​​together to determine the suspected corrosion area. Otherwise, it is determined that there is no area with a sudden increase in resistance and strong corrosion.

8. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 7, characterized in that: The method for determining the scoring threshold corresponding to the severe corrosion level is as follows: Extract the baseline score corresponding to the severe corrosion level from the database; Traverse the continuous subsequences with values ​​greater than zero and filter out the peak values ​​of the resistance growth rate in the subsequences; If the peak resistance growth rate is less than or equal to the preset upper limit of resistance growth rate, the benchmark score will be directly determined as the score threshold corresponding to the strong corrosion level. Conversely, the excess amount of the resistance growth rate is calculated, and the reduction amount of the benchmark score is calculated based on the preset reduction amount of the strong corrosion benchmark score corresponding to the unit excess amount. The reduction amount is then subtracted from the benchmark score to obtain the score threshold corresponding to the strong corrosion level.

9. The method for assessing and warning the health status of substation grounding grids based on multi-source data fusion according to claim 1, characterized in that: The method for calculating the corrosion anomaly degree of each branch includes: D1. Based on the design drawings of the grounding grid, establish a resistance network model corresponding to the grounding grid structure below the suspected corrosion area, and identify all nodes and branches in the model. D2. Select multiple nodes connected to the grounding lead from the nodes of the resistor network model as accessible test ports. Test current is injected between selected pairs of test ports, and the voltage drop between each pair of ports is measured simultaneously to obtain multiple sets of port voltage-current measurement data. D3. Using multiple sets of port voltage-current measurement data as input, an optimization algorithm is adopted to iteratively adjust the resistance parameters of each branch in the resistor network model with the goal of minimizing the overall error between the predicted voltage drop between each selected port pair calculated by the resistor network model and the corresponding actual measurement value. D4. Repeat step D3 until the total error is less than the preset convergence threshold or the number of iterations reaches the preset upper limit. D5. When the iteration converges, output the optimal estimated value of the resistance parameters of each branch in the resistor network model. D6. For each branch, calculate the percentage of the difference between its optimal resistance estimate and the theoretical resistance value relative to the theoretical resistance value, and use this percentage as the degree of corrosion anomaly of the branch.

10. The substation grounding grid health status assessment and early warning method based on multi-source data fusion according to claim 1, characterized in that: The method for generating the branch investigation priority sequence is as follows: Based on the corrosion anomaly values ​​of each branch, all branches are sorted in descending order to generate a branch corrosion investigation priority sequence.

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