Hard insulator pollution flashover risk prediction method and system considering microclimate conditions

Abstract

The present application relates to the technical field of insulator operation and maintenance management, and particularly relates to a hard insulator pollution flashover risk prediction method and system considering micro-meteorological conditions, comprising: obtaining micro-meteorological monitoring data, regional pollution source information and first, second and third parameters respectively representing hydrophobicity, surface hardness and effective creepage distance of each insulator in the line of the target power transmission corridor; identifying a first time window meeting the condensation condition and a second time window meeting the pollutant deposition condition based on the micro-meteorological data; correcting the first time window in combination with the first parameter of the insulator, and evaluating the irreversible contamination probability level according to the second parameter and the regional pollution source information; taking the third parameter as the benchmark, combining the actual window and the probability level, determining the dynamic pollution flashover risk level and giving an early warning through traversing the risk mapping table; the present application combines the material structure characteristics of the insulator with the micro-meteorological and regional pollution conditions, realizes accurate dynamic prediction and graded early warning of the pollution flashover risk, and improves the pertinence of mixed line operation and maintenance decision.

Description

Technical Field

[0001] This invention relates to the technical field of insulator operation and maintenance management, and in particular to a method and system for predicting the risk of flashover of rigid insulators considering micrometeorological conditions. Background Technology

[0002] Insulator flashover is one of the major faults threatening the safe and stable operation of the power grid. With the development of monitoring technology, dynamic risk assessment methods based on micrometeorology and regional pollution have become the mainstream research direction. These methods typically predict pollution accumulation trends and insulator surface wetting conditions by monitoring environmental data, thereby assessing flashover risk.

[0003] However, in actual power grids, insulators are not homogeneous devices. To adapt to different operating environments, a variety of rigid composite insulators with special materials and structural designs have been developed. These include insulators that use reinforced hydrophobic materials to delay surface moisture absorption, insulators that use high-hardness sheaths to prevent bird peck damage, and insulators that use extra-large umbrella diameter devices to change local creepage paths and prevent bird damage.

[0004] Most existing methods for predicting pollution flashover risk treat insulators as standard components with uniform response characteristics. Their prediction models are mainly based on general environmental data and fail to fully consider and quantify the different effects of different material and structural characteristics on pollution adhesion, wetting process, and final flashover voltage. As a result, when dealing with mixed lines equipped with insulators of different characteristics, the prediction accuracy of existing methods may be insufficient, and they cannot accurately reflect the actual risk mitigation or aggravation effect brought about by the material and structure of a specific insulator, thus affecting the pertinence of operation and maintenance decisions. Summary of the Invention

[0005] This invention provides a method and system for predicting the risk of flashover of rigid insulators that takes into account micrometeorological conditions, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Methods for predicting the risk of flashover of rigid insulators considering micrometeorological conditions include: Acquire micro-meteorological monitoring data, regional pollution source information, and material structure parameters of each insulator in the target transmission corridor; the material structure parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance; Based on micrometeorological monitoring data, the first time window that meets the preset condensation formation conditions and the second time window that meets the preset pollutant deposition conditions are identified and output. For each insulator, the first time window is corrected according to the first parameter to obtain the actual effective wetting risk window corresponding to that insulator; Based on the second parameter and regional pollution source information, determine the probability of irreversible surface fouling occurring within the second time window and output the probability level; Using the third parameter as a benchmark, combined with the actual effective wetting risk window and probability level, the dynamic pollution flashover risk level of the insulator is determined and an early warning is issued by traversing the preset risk mapping table.

[0007] Furthermore, the first parameter is the saturated moisture absorption rate or moisture diffusion coefficient of the insulator sheath material; The second parameter is the surface hardness value measured based on the Barcol hardness or Shore D scale. The third parameter is the equivalent creepage distance calculated based on the geometry of the insulator skirt.

[0008] Furthermore, when the insulator is equipped with an independent bird protection device, the calculation of the equivalent creepage distance includes the increase in the air gap between the insulators caused by the independent bird protection device.

[0009] Furthermore, during the correction process, the first parameter is used to delay the formation rate of surface conductive channels; the first time window is corrected based on the first parameter, including: The start time of the first time window is delayed by a first duration, and the first duration is positively correlated with the value of the first parameter.

[0010] Furthermore, based on the second parameter and regional pollution source information, the probability of irreversible surface fouling occurring within the second time window is determined, including: When the regional pollution source information indicates a risk of bird damage and the second parameter is below a preset threshold, the probability level of bird pecking injury or highly attached bird droppings pollution is determined to be high.

[0011] Furthermore, the risk mapping table is calibrated based on multiphysics simulation. The calibration process includes: Establish a finite element model of an insulator that includes different material structural parameters; Fluid dynamics simulation was coupled into the finite element model of the insulator to simulate the pollution settling process under different wind speeds and wind directions; Electrothermal simulation was coupled into the finite element model of the insulator to simulate the surface leakage current distribution under different humidity and temperature conditions. Based on the simulation results, a database of the correspondence between material structural parameter combinations, environmental time windows and flashover voltage margins was established, and a risk mapping table was generated accordingly.

[0012] Furthermore, the risk mapping table is dynamically updated based on historical flashover event data of the target transmission corridor and the material and structural parameters of the corresponding insulators to correct the calibration results of the multiphysics simulation.

[0013] Furthermore, the dynamic flashover risk level is used to generate a heat map of flashover risk for line insulators. The heat map uses tower location and phase sequence as coordinates and renders the color depth according to the risk level. The duration of the actual effective wetting risk window and the probability level together affect the color saturation.

[0014] Furthermore, the early warning operations include: If the third parameter of the insulator is greater than the average value of the line insulator, then a partial discharge re-inspection based on image recognition is initiated. If the first parameter of the insulator shows that its hydrophobicity level is higher than the preset standard, then its routine inspection cycle will be extended accordingly. On the other hand, the present invention also provides a flashover risk prediction system for rigid insulators that takes into account micrometeorological conditions, including: The data acquisition module is used to acquire micro-meteorological monitoring data of the target transmission corridor, regional pollution source information, and material structure parameters of each insulator in the line; the material structure parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance; The time window identification module is used to identify and output the first time window that meets the preset condensation formation conditions and the second time window that meets the preset pollutant deposition conditions based on micrometeorological monitoring data. The wetting risk window correction module is used to correct the first time window for each insulator based on the first parameter to obtain the actual effective wetting risk window corresponding to that insulator. The fouling probability assessment module is used to determine the probability of irreversible surface fouling occurring within a second time window based on the second parameter and regional pollution source information, and output the probability level. The pollution flashover risk warning module is used to determine the dynamic pollution flashover risk level of the insulator and issue a warning by using the third parameter as a benchmark, combined with the actual effective wetting risk window and probability level, and by traversing the preset risk mapping table.

[0015] The technical solution of this invention can achieve the following technical effects: By specifically coupling and linking the three material structural parameters characterizing individual differences of insulators—surface hydrophobicity, surface hardness, and effective creepage distance—with condensation windows and pollution deposition windows identified by micro-meteorological conditions, this method can quantify and distinguish the risk mitigation or aggravation effects of different insulators due to their own design when dealing with mixed lines equipped with hard composite insulators of various characteristics. This transforms risk assessment from a group trend prediction based on general environmental data to an individualized and precise assessment for specific insulators, improving the pertinence of early warning and the effectiveness of operation and maintenance decisions.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the method for predicting the risk of flashover of rigid insulators under micrometeorological conditions, as described in this invention. Figure 2 This is a schematic diagram of the structure of the rigid insulator pollution flashover risk prediction system that takes into account micrometeorological conditions in this invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] 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. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] like Figure 1 As shown, the method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to the present invention specifically includes the following steps: Step S1: Obtain micro-meteorological monitoring data, regional pollution source information, and material structure parameters of each insulator in the target transmission corridor; the material structure parameters shall include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance. Step S2: Based on micrometeorological monitoring data, identify and output the first time window that meets the preset condensation formation conditions and the second time window that meets the preset pollutant deposition conditions; Step S3: For each insulator, the first time window is corrected according to the first parameter to obtain the actual effective wetting risk window corresponding to the insulator; Step S4: Based on the second parameter and regional pollution source information, determine the probability of irreversible surface fouling occurring within the second time window and output the probability level. Step S5: Using the third parameter as a benchmark, and combining it with the actual effective wetting risk window and probability level, determine the dynamic flashover risk level of the insulator and issue an early warning by traversing the preset risk mapping table.

[0022] In this embodiment, the risk of insulator flashover is jointly determined by environmental conditions and the characteristics of the insulator itself. By integrating micrometeorological monitoring data of the target transmission corridor, regional pollution source information, and the differentiated material structure parameters of each insulator, a dynamic risk prediction logic adapted to the characteristics of rigid insulators is constructed. This logic can accurately identify the basic time window for condensation formation and pollutant deposition, and, combined with the characteristics of the insulator itself, complete the characteristic correction and risk probability determination of the corresponding window. By correcting the wetting risk window corresponding to condensation based on the first parameter characterizing surface hydrophobicity, it can conform to the surface moisture absorption patterns of different insulators and establish a precise correlation between wetting conditions and insulator material characteristics. By combining the second parameter characterizing surface hardness with pollution source information to determine the probability level of irreversible surface contamination, the degree of differential impact of insulator structural characteristics on contamination adhesion can be quantified. By using the third parameter characterizing effective creepage distance as a benchmark, and combining the corrected actual effective wetting risk window with the contamination probability level to traverse the risk mapping table to complete the classification and early warning, the differential impact of different material structural characteristics on insulator contamination adhesion, wetting process and final flashover voltage can be fully considered and quantified. This reflects the risk effect of a specific insulator due to its own material and structure, improves the accuracy of pollution flashover risk prediction in mixed line scenarios, and thus improves the accuracy of risk prediction in mixed lines.

[0023] In practical implementation, multiple types of rigid composite insulators may coexist within the same corridor. Their material formulations, surface characteristics, and skirt structures differ significantly, leading to varying responses to the same environmental conditions. Using a uniform model for assessment would result in an overestimation of the risk of highly hydrophobic insulators or an underestimation of the risk of surface-damaged insulators, affecting the accurate allocation of maintenance resources. This embodiment simultaneously collects three types of data: environmental, pollution source, and insulator body parameters, and establishes a calculable correlation between them. The specific implementation is as follows: Step S11: Along the target power transmission corridor, deploy miniature weather stations at intervals of 5-10 kilometers to continuously monitor and upload data such as temperature, relative humidity, wind speed, wind direction, rainfall, and atmospheric pressure; near known industrial emission sources, main traffic arteries, saline-alkali land, or bird habitats within the corridor, add sampling points or image monitoring devices for specific pollutants such as SO2, NOx, salt density, and bird droppings residue to form a regional pollution source information database, which records the type, spatial location, intensity level, and changes over time of pollution sources; Step S12: Extract or measure the material structure parameters of each insulator in the line from the equipment ledger, factory test report, and supplementary tests. These parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance. The first parameter is the saturated moisture absorption rate or moisture diffusion coefficient of the insulator sheath material, obtained from the material report of its silicone rubber or similar polymer sheath material. If no direct data is available, it is indirectly calculated by measuring the static contact angle and comparing it to a pre-calibrated contact angle-moisture absorption rate curve. This parameter quantifies the insulator surface's inherent ability to resist moisture penetration and delay the formation of a conductive water film. The second parameter is based on the Barcol hardness or Shore hardness D standard. The surface hardness value, measured by a portable hardness tester, is obtained by taking the average value after multiple measurements at representative locations on the insulator sheath surface. This value is used to assess the insulator surface's resistance to mechanical damage. The lower the hardness, the easier it is to produce permanent scratches or pits, which can easily lead to dirt accumulation and moisture retention. The third parameter is the equivalent creepage distance calculated based on the geometry of the insulator skirts, obtained from three-dimensional drawings or images of the insulator. If the insulator is equipped with independent bird protection devices, such as large-diameter skirts or bird spikes, it is necessary to assess the contribution of these devices to the incremental air gap between adjacent skirts and incorporate this incremental contribution into the calculation of the effective creepage distance in an equivalent manner. This gap increment directly affects the insulator's creepage path and insulation withstand capability. Step S13: Establish a unified timestamp and spatial coordinates for all monitoring data. Use the data platform to associate streaming data from different sensors with the static insulator parameter database to ensure that at any analysis time, real-time environmental data of the location of a specific insulator and its own material structure properties can be obtained.

[0024] In this embodiment, by deploying a differentiated monitoring network, constructing an individual insulator characteristic database, establishing a unified spatiotemporal benchmark for multi-source data, and achieving structured correlation, a data foundation supporting differentiated assessment of insulator pollution flashover risk is formed. Spatiotemporal correlation ensures that the real-time environmental conditions and attributes of a specific insulator can be accurately matched at any analysis time, avoiding risk misjudgment caused by unified model assessment.

[0025] In some embodiments of the present invention, existing micrometeorological-driven pollution flashover risk assessments often use a single threshold judgment mode to identify condensation and pollutant deposition time windows. This involves setting fixed temperature, humidity, and wind speed thresholds, and determining the formation of a window when the monitored data reaches the threshold. This ignores the coupling relationship between micrometeorological parameters. Condensation formation is not only related to temperature and humidity but also affected by wind speed and atmospheric pressure. Low wind speeds favor water vapor retention, while high pressure accelerates water vapor condensation. Single-parameter judgment is prone to misjudgment. Furthermore, it does not consider regional micrometeorological differences. Different transmission corridors have different terrains and vegetation cover, resulting in varying critical meteorological conditions for condensation and pollutant deposition. Fixed thresholds cannot be adapted to the entire scenario. This embodiment constructs a dynamic multi-parameter coupled judgment system, combined with pollution source characteristics, to achieve accurate identification of time windows. The specific implementation is as follows: Step S21: Extract continuous micro-meteorological data collected by distributed monitoring nodes within the target power transmission corridor, including at least temperature, relative humidity, wind speed, wind direction, precipitation, and atmospheric pressure data. Step S22: Preprocess the collected data: Use the moving average method to smooth the temperature and relative humidity data to eliminate instantaneous sensor errors; correct the wind speed data based on the terrain features of the power transmission corridor, using the difference between the elevation of the monitoring node and the average elevation of the corridor as the correction factor, with a correction coefficient of 1.05 for every 100m increase in elevation; remove outliers in the precipitation data with instantaneous precipitation intensity greater than 5mm / h, as these data correspond to rainfall processes and are not related to condensation or dry deposition of pollutants; after preprocessing, a standardized micro-meteorological time-series dataset is formed, with the data format uniformly as: timestamp-temperature-relative humidity-corrected wind speed-wind direction-effective precipitation-atmospheric pressure; Step S23: The preset logic for determining condensation formation is that the dynamic temperature and humidity coupling evolution meets the critical condition for water vapor condensation and there is no effective precipitation interference. The specific determination process is as follows: a. Based on the standardized micro-meteorological time series dataset, extract the temperature change rate and relative humidity change rate within a continuous time period. The temperature change rate is calculated as the ratio of the temperature difference between two adjacent data sets to the time interval, and the relative humidity change rate is calculated in the same way. b. Set dynamic triggering conditions for condensation formation: Three or more consecutive sets of data meet the conditions of "temperature change rate ≤ -0.1℃ / 10min" and "relative humidity change rate ≥ 1% / 10min", while the relative humidity of the current period is ≥ 85% and the effective precipitation is 0mm; c. Determine the start and end times of the first time window: The start time is the timestamp when the above dynamic triggering conditions are first met; the end time is the first timestamp when the triggering conditions are no longer met. The criteria for determination are: temperature change rate > -0.1℃ / 10min and relative humidity < 80%, or the moment when effective precipitation > 0mm occurs. d. Perform continuity verification on the identified time windows and remove windows with a duration of less than 30 minutes. The condensation formed by such short windows cannot form a continuous conductive film on the surface of the insulator and does not contribute to the actual wetting risk. Step S24: The preset judgment logic for pollutant deposition is that the wind speed conditions are suitable for pollutant particle deposition, there is no precipitation scouring, and there is a spatiotemporal overlap with the emission period of regional pollution sources. The specific judgment process is as follows: a. Extract the dominant emission periods and pollutant types of each pollution source. Different pollutant types correspond to different minimum settling wind speed thresholds. b. Based on the standardized micro-meteorological time series dataset, the time periods with effective precipitation of 0 mm within a continuous period are selected to exclude the scouring effect of precipitation on pollutants. c. During the above-mentioned periods without precipitation, wind speed thresholds are set according to the type of pollution source: wind speed thresholds for industrial dust pollution sources are ≤2m / s, wind speed thresholds for agricultural particulate matter pollution sources are ≤1.5m / s, and wind speed thresholds for bird droppings and debris pollution sources are ≤1m / s. At the same time, the angle between the wind direction and the azimuth of the pollution source relative to the power transmission corridor is required to be ≤45° to ensure that pollutants can diffuse to the target line area. d. Determine the start and end times of the second time window: The start time is the first timestamp when the following conditions are met simultaneously: no effective precipitation, wind speed ≤ the corresponding threshold, wind direction meets the requirements, and the period is the period when pollution sources are the main source of emissions; the end time is the first timestamp when any of the above conditions are not met. e. Verify the cumulative duration of the identified time windows and remove windows with a continuous duration of less than 60 minutes, as the amount of pollutant deposition within such short time windows cannot reach the critical value that affects the insulation performance of the insulator. Step S25: Sort the identified first and second time windows in order of timestamps. Each window is labeled with the corresponding key micro-meteorological parameter feature values, including the average temperature, average relative humidity, and minimum temperature of the first time window, and the average wind speed, prevailing wind direction, and corresponding pollution source type of the second time window. The output format is: window type-start timestamp-end timestamp-key parameter feature value, forming a structured time window dataset.

[0026] This embodiment constructs a dynamic multi-parameter coupled judgment system, introducing dynamic parameters such as temperature change rate and relative humidity change rate. It also incorporates differentiated judgment thresholds based on pollution source type, integrates spatial correlation verification between wind direction and pollution source location, and supplements time window continuity and cumulative duration verification, achieving a transformation from static threshold matching to dynamic scenario adaptation. In this embodiment, smoothing correction and outlier removal in the data preprocessing stage eliminate sensor errors and extreme weather interference, ensuring the reliability of micro-meteorological data. The dynamic multi-parameter coupled judgment logic avoids misjudgments caused by a single threshold; for example, the coordinated judgment of temperature change rate and relative humidity change rate accurately captures the initial stage of condensation formation. Correlation verification between pollution source characteristics and wind direction ensures that pollutant deposition window identification matches the actual pollution source, avoiding misjudging non-target pollution source impact periods as valid windows. The time window verification stage eliminates invalid short-term windows, ensuring that all output windows have the actual ability to affect insulator contamination accumulation or wetting.

[0027] In practical implementation, as one example, existing methods for correcting the wetting risk window often adopt a uniform approach, that is, applying the same correction rule to all insulators within the same transmission corridor, without considering the differential influence of the first parameter of different insulators. The numerical difference of the first parameter directly determines the rate of water vapor adsorption and diffusion on the insulator surface, thus affecting the start time of conductive channel formation. Uniform correction can lead to overestimation or underestimation of the wetting risk window for some insulators, failing to match the actual wetting response characteristics of individual insulators. This embodiment achieves personalized and accurate correction of the first time window by constructing a correction system based on first parameter classification and dynamic duration calibration. The specific implementation steps are as follows: Step S31: Extract the first parameter of the insulator and standardize it; for the saturated moisture absorption rate parameter, normalize it to the [0,1] interval by using the actual value / the maximum value of the saturated moisture absorption rate of the industry standard hydrophobic insulator; for the moisture diffusion coefficient parameter, normalize it to the [0,1] interval by using the actual value / the maximum value of the moisture diffusion coefficient of the industry standard hydrophobic insulator, and form a standardized first parameter value. Step S32: Based on the insulator hydrophobicity level classification standard, establish the correspondence between the standardized first parameter value and the correction coefficient; for example, divide the standardized first parameter value into three intervals: interval 1 [0, 0.3] corresponds to high hydrophobicity level, and the correction coefficient is 0.8; interval 2 (0.3, 0.7] corresponds to medium hydrophobicity level, and the correction coefficient is 1.0; interval 3 (0.7, 1.0] corresponds to low hydrophobicity level, and the correction coefficient is 1.5; the calibration basis of the correction coefficient is the hydrophobicity test data of the insulator, that is, the difference in the time of forming a continuous conductive channel on the surface of insulators with different hydrophobicity levels under the same condensation environment; Step S33: Extract the core feature parameters of the first time window, including the average relative humidity and average temperature within the window. Determine the base duration based on these feature parameters: when the average relative humidity is ≥90% and the average temperature is ≥10℃, the base duration is set to 20 minutes; when the average relative humidity is ≥90% and the average temperature is <10℃, the base duration is set to 30 minutes; when the average relative humidity is <90% and the average temperature is ≥10℃, the base duration is set to 15 minutes; when the average relative humidity is <90% and the average temperature is <10℃, the base duration is set to 25 minutes. The base duration is set based on the influence of condensation environment characteristics on water vapor adsorption rate. Under high humidity and low temperature conditions, the water vapor adsorption rate is faster, requiring a longer base duration to match the delay effect formed by the conductive channel. Step S34: Substitute the standardized first parameter value and the corresponding correction coefficient into the formula: First duration = Basic duration × Standardized first parameter value × Correction coefficient, to obtain the first duration of each insulator; The first duration is positively correlated with the value of the first parameter, that is, the larger the standardized first parameter value, the longer the first duration, which is consistent with the actual characteristics of low hydrophobicity insulators having a weaker delay effect in the formation of conductive channels on the surface and requiring a longer delay time. Step S35: Extract the start timestamp of the first time window, and shift this timestamp forward by one duration to obtain the corrected start timestamp; for example, if the original start timestamp is 2025-06-01 04:00:00 and the first duration is 20 minutes, then the corrected start timestamp is 2025-06-01 04:20:00; keep the original end timestamp of the first time window unchanged to form the preliminary corrected time window, i.e., corrected start timestamp - original end timestamp; Step S36: Calculate the duration of the pre-corrected window, i.e., the difference between the end timestamp and the corrected start timestamp; set the effective window duration threshold to 15 minutes. When the pre-corrected window duration is ≥15 minutes, it is determined to be an effective window and is used as the actual effective wetting risk window for the insulator; when the pre-corrected window duration is <15 minutes, it is determined to be an invalid window. At this time, it is necessary to re-check the standardized processing result of the first parameter and the calculation process of the first duration. If the check is correct and it is still an invalid window, it is determined that the insulator has no actual wetting risk within the corresponding first time window, and an indicator of no effective wetting risk window is output. Step S37: Mark the corresponding correction basis for the effective window, including the first parameter type, standardized first parameter value, correction coefficient and first duration, to form a structured actual effective wetting risk window dataset. The data format is: insulator number - corrected start timestamp - end timestamp - window duration - correction basis.

[0028] This embodiment constructs a standardized processing and hierarchical correction coefficient system based on the type and numerical differences of the first parameter. It dynamically determines the delay duration by combining the condensation environment characteristics of the first time window, achieving a shift from unified correction to personalized and precise correction. Furthermore, a validity verification process ensures the rationality of the correction results, guaranteeing that the corrected window matches the actual wetting response characteristics of the insulator. In this embodiment, the standardized processing and hierarchical correction coefficient calibration of the first parameter eliminate dimensional differences between different types of first parameters, ensuring the uniformity and specificity of the correction logic. The dynamic determination logic of the first duration, combined with the condensation environment characteristics and the hydrophobic properties of the insulator, makes the delay duration setting more closely match the actual conductive channel formation pattern, avoiding correction deviations caused by a single delay. The validity verification process eliminates short-term invalid windows, preventing windows that do not meet the conditions for conductive channel formation from being included in the risk assessment, ensuring that the output actual effective wetting risk window has a real risk contribution.

[0029] In a specific implementation, as one example, given the significant differences in the tolerance of insulators with different hardness to various pollution sources, especially bird-induced pecking damage and bird droppings contamination, the vulnerability of the insulator is directly determined by its surface hardness. Therefore, determining the probability of surface contamination requires considering both the macroscopic distribution of pollution sources in the area and the surface hardness characteristics of the insulator itself. This embodiment achieves accurate determination of the probability of irreversible surface contamination by constructing a probability assessment system that includes pollution source type subdivision, second parameter quantification matching, and multi-factor coupling judgment. The details are as follows: Step S41: Extract regional pollution source information; classify pollution sources into Category A (bird-related pollution sources), Category B (industrial dust pollution sources), and Category C (agricultural particulate matter pollution sources) according to pollution mechanisms; for Category A pollution sources, extract risk factors including the core period of bird activity, the straight-line distance between habitat and power transmission corridor, and bird species. The core period of bird activity is obtained through regional bird monitoring data statistics, the straight-line distance between habitat is calculated through a geographic information system, and the bird species are determined based on ecological survey data; for Category B pollution sources, extract risk factors including the average particle size of industrial dust, emission intensity, and the angle between the pollution source and the dominant diffusion direction of the power transmission corridor. The dust particle size and emission intensity are taken from pollution source discharge permit filing data, and the angle between the dominant diffusion direction is calculated by combining meteorological statistics and a geographic information system; for Category C pollution sources, extract risk factors including the release period of agricultural particulate matter and particulate matter adhesion coefficient. The data are all taken from the regional agricultural production plan and environmental monitoring annual report; form a structured pollution source information table with pollution source type, risk factors, and factor quantification values; Step S42: Extract the second parameter of the insulator and standardize it; for the Barcol hardness value, normalize it to the [0,1] range by using the actual value / the maximum value of the industry standard for Barcol hardness of rigid composite insulators; for the Shore hardness D scale value, normalize it to the [0,1] range by using the actual value / the maximum value of the industry standard for Shore hardness D scale of rigid composite insulators. Step S43: Based on the insulator anti-pollution test data, calibrate the preset threshold. That is, select the same type of hard composite insulators with different hardness levels, conduct bird peck simulation tests and various pollutant adhesion tests, and count the minimum hardness value that can lead to irreversible pollution. Set the standardized value corresponding to the minimum hardness value as the preset threshold. Step S44: Combining the calibrated preset thresholds and extracted risk factors, construct judgment logic for each of the three types of pollution sources, and clarify the superimposed judgment rules for cross-type pollution sources: The logic for determining Class A pollution sources is as follows: When the core period of bird activity overlaps with the second time window and the straight-line distance between the habitat and the bird is ≤500 meters, the hardness coupling determination is initiated: If the standardized second parameter value is <preset threshold, the probability level of irreversible fouling is determined to be high; if the standardized second parameter value is ≥preset threshold, the bird species type is further combined for determination, with large birds corresponding to a medium probability level and small birds corresponding to a low probability level; if the above prerequisites are not met, the probability level is determined to be low. The logic for determining Class B pollution sources is as follows: When the industrial dust emission intensity is ≥50mg / m³ and the included angle of the dominant diffusion direction is ≤30°, the determination is based on the second parameter: when the standardized second parameter value is <preset threshold, dust is easily embedded in the surface micropores, leading to irreversible fouling, and the probability level is medium; when the standardized second parameter value is ≥preset threshold, dust adhesion stability is low, and the probability level is low; when the preconditions are not met, the probability level is low. The logic for determining Class C pollution sources is as follows: When the release period of agricultural particulate matter overlaps with the second time window and the particulate matter adhesion coefficient is ≥0.6, the second parameter is used for determination: when the standardized second parameter value is <preset threshold, particulate matter is prone to adhesion and penetration into the surface, and the probability level is medium; when the standardized second parameter value is ≥preset threshold, the probability level is low; when the preconditions are not met, the probability level is low. Cross-type pollution source superposition judgment rule: When two or more types of pollution sources act simultaneously within the same second time window, the highest probability level corresponding to each type of pollution source is taken as the initial probability level after superposition. If the superposition results in a high level, no adjustment is required; if it is a medium level, it is further verified in conjunction with the second parameter. If the standardized second parameter value is less than the preset threshold, it is upgraded to a high level; otherwise, it remains a medium level. Step S45: Set the probability levels to low, medium, and high. For each level, the irreversible contamination probability is defined as: low level - contamination probability < 30%, medium level - 30% ≤ contamination probability ≤ 70%, high level - contamination probability > 70%. Retrieve historical contamination event data for the target transmission corridor over the past 5 years and compare the current insulator's standardized second parameter value, the pollution source environment, and the matching degree of the insulator in historical contamination events. If the matching degree is ≥ 80%, the preliminary probability level is confirmed to be valid. If the matching degree is < 80%, the level is fine-tuned based on historical data, and adjustments are only allowed between adjacent levels. After verification, output the irreversible surface contamination probability level of each insulator within the corresponding second time window, and simultaneously label the judgment criteria, including pollution source type, core risk factor quantification value, standardized second parameter value, and whether a preset threshold is triggered, forming a structured dataset of insulator number - second time window identifier - probability level - judgment criteria.

[0030] This embodiment establishes a quantitative matching relationship between the second parameter and various fouling mechanisms through pollution source type subdivision and risk factor extraction. In particular, it constructs a coupled judgment logic of risk factor-hardness threshold for bird damage risk, and introduces historical data verification to realize the transformation from macro-unified judgment to individual precise coupled judgment, ensuring that the judgment result is completely matched with the insulator's actual anti-fouling capability and the polluted environment. In this embodiment, through pollution source classification analysis and risk factor extraction, the judgment logic is made to fit the essential characteristics of different fouling mechanisms, avoiding the deviation caused by single judgment. Through the standardization of the second parameter and the experimental calibration of the preset threshold, the dimensional differences of different types of hardness parameters can be eliminated, ensuring the accuracy of the quantitative assessment of hardness impact. Through type-based coupled judgment and historical data verification, the probability level can be accurately calibrated, especially ensuring the accuracy of the judgment of bird-related fouling probability.

[0031] In a specific implementation, as one example, to achieve accurate determination of dynamic flashover risk levels and targeted output of operation and maintenance decisions, the following steps are implemented: Step S51, pre-calibration of the risk mapping table, the process includes: a. Select common types of rigid composite insulators for the target power transmission corridor and construct multiple finite element models according to the combination of material and structural parameters; b. Input different wind speed and wind direction parameters into each finite element model to simulate the pollution settling process within the second time window, and output the adhesion distribution law and cumulative amount data of pollution on the insulator surface. c. Based on the pollution distribution data output by fluid dynamics simulation, input different humidity and temperature parameters into the model to simulate the spatiotemporal distribution and temperature rise characteristics of leakage current on the insulator surface, and output the flashover voltage margin under different operating conditions. d. Using the standardized value of the third parameter - the actual effective wetting risk window duration - the irreversible fouling probability level as the input dimension and the flashover voltage margin as the output dimension, establish a three-dimensional correspondence database; divide the flashover voltage margin into four intervals, which correspond to the dynamic flashover risk levels respectively: Level I: extremely low, Level II: low, Level III: medium, and Level IV: high, and generate an initial risk mapping table accordingly. Step S52: Extract the third parameter of the insulator and normalize it to the [0,1] interval by using the actual value / the maximum value of the third parameter of the insulator in the target transmission corridor to form a standardized third parameter value; Step S53: Combining the actual effective wetting risk window, the probability level of irreversible fouling, and the standardized third parameter value, conduct risk level matching, including: a. The actual effective wet risk window duration is divided into three intervals (short: <30 minutes, medium: 30-60 minutes, long: >60 minutes), which, together with the probability level (low, medium, high) and the standardized third parameter value, constitute a three-dimensional matching input; b. Using the standardized third parameter as an anchor point, adjust the input dimension weights of the initial risk mapping table. When the standardized value is ≥0.8, the matching weights are tilted towards the third parameter, weakening the influence of window duration and probability level; when the standardized value is <0.5, the weights of window duration and probability level are strengthened. c. Substitute the preprocessed 3D input parameters into the adjusted risk mapping table and iterate through the matching to obtain the initial risk level; d. If there are multiple second time windows and actual effective wetting risk windows corresponding to the same insulator, the highest value of the risk level matched by each window shall be taken as the final dynamic flashover risk level. Step S54: Based on the historical flashover event data of the target transmission corridor over the past 5 years and the corresponding insulator material structure parameters, revise the initial risk mapping table, including: a. Extract the insulator material structural parameters, micro-meteorological conditions at the time of the event, and pollution status corresponding to historical pollution flashover events to form a historical matching dataset; b. Compare the matching risk level of the historical dataset in the initial risk mapping table with the actual pollution flashover results, and calculate the deviation value; if the initial matching level is Level II but pollution flashover actually occurs, the flashover voltage margin range of the corresponding input dimension will be lowered by one level; if the initial matching level is Level III but pollution flashover does not occur, the corresponding range will be raised by one level. c. Update the three-dimensional correspondence of the risk mapping table according to the calibrated flashover voltage margin range to ensure that the mapping table matches the actual operating environment of the target transmission corridor; the update cycle is set to once a year, and if three or more pollution flashover events occur within the year, a temporary update will be triggered. Step S55: Heat map generation. A two-dimensional coordinate system is constructed with the tower location number of the target transmission corridor as the horizontal axis and the phase sequence as the vertical axis. The pollution flashover risk level is used as the basis for color depth rendering, and the higher the level, the darker the color. At the same time, the actual effective wetting risk window duration and probability level are used as color saturation adjustment factors. The longer the window duration and the higher the probability level, the higher the color saturation. Step S56: Calculate the average value of the third parameter of the insulators in the target transmission corridor. If the third parameter of an insulator is greater than the average value and the dynamic flashover risk level is ≥ Level III, initiate a partial discharge review based on image recognition. The review is based on the fact that if the risk level of an insulator with a large equivalent creepage distance is too high, there is a high probability of local insulation defects, which need to be accurately located. Extract the first parameter of the insulator. If the hydrophobicity level corresponding to the first parameter is higher than the preset standard and the dynamic flashover risk level is ≤ Level II, extend its routine inspection cycle by 30%-50%. The adjustment is based on the fact that the surface wetting risk of highly hydrophobic insulators is low, and there is no need for regular inspection frequency, which can reduce operation and maintenance costs.

[0032] This embodiment uses the third parameter as a benchmark anchor point to construct a three-dimensional coupled judgment system of material structure parameters, environmental window, and contamination probability. The risk mapping table is optimized through multi-physics simulation calibration and dynamic updating of historical data. Simultaneously, differentiated early warnings are achieved based on the characteristics of the first and third parameters of the insulator, enabling personalized and accurate assessment, dynamic calibration, and differentiated operation and maintenance. In this embodiment, the third parameter benchmark anchoring and three-dimensional coupling matching ensure that the risk level judgment aligns with the individual insulation characteristics of the insulator, avoiding the deviation of homogenized risk levels for different insulators in the same environment. Multi-physics simulation calibration and dynamic updating of the risk mapping table ensures its environmental adaptability and data timeliness, correcting deviations between simulation and actual scenarios. Differentiated early warning operations are specifically matched to the insulator material characteristics, optimizing the allocation of operation and maintenance resources while ensuring power grid safety. This advantage is guaranteed by experimental data on the hydrophobicity characteristic of the first parameter and the insulation redundancy characteristic of the third parameter. The risk heatmap's coordinate and color rendering visualizes the line risks, facilitating rapid location of high-risk areas by operation and maintenance personnel.

[0033] Based on the same inventive concept as the method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions in the foregoing embodiments, this invention also provides a system for predicting the risk of flashover of rigid insulators considering micrometeorological conditions, such as... Figure 2 As shown, the system includes: The data acquisition module is used to acquire micro-meteorological monitoring data of the target transmission corridor, regional pollution source information, and material structure parameters of each insulator in the line; the material structure parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance; The time window identification module is used to identify and output the first time window that meets the preset condensation formation conditions and the second time window that meets the preset pollutant deposition conditions based on micrometeorological monitoring data. The wetting risk window correction module is used to correct the first time window for each insulator based on the first parameter to obtain the actual effective wetting risk window corresponding to that insulator. The fouling probability assessment module is used to determine the probability of irreversible surface fouling occurring within a second time window based on the second parameter and regional pollution source information, and output the probability level. The pollution flashover risk warning module is used to determine the dynamic pollution flashover risk level of the insulator and issue a warning by using the third parameter as a benchmark, combined with the actual effective wetting risk window and probability level, and by traversing the preset risk mapping table.

[0034] The system described above in this invention can effectively realize a method for predicting the risk of flashover of rigid insulators that takes into account micrometeorological conditions. The technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0035] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for predicting the risk of flashover pollution in rigid insulators considering micrometeorological conditions, characterized in that, include: Acquire micro-meteorological monitoring data, regional pollution source information, and material and structural parameters of each insulator in the target transmission corridor; The material structure parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance; the first parameter is the saturated moisture absorption rate or moisture diffusion coefficient of the insulator sheath material; the second parameter is the surface hardness value measured based on Barcol hardness or Shore D scale; and the third parameter is the equivalent creepage distance calculated based on the geometry of the insulator skirts. Based on the micrometeorological monitoring data, a first time window that meets the preset condensation formation conditions and a second time window that meets the preset pollutant deposition conditions are identified and output. For each insulator, the first time window is corrected according to the first parameter to obtain the actual effective wetting risk window corresponding to that insulator; Based on the second parameter and the regional pollution source information, determine the probability of irreversible surface contamination occurring within the second time window and output the probability level; Using the third parameter as a benchmark, combined with the actual effective wetting risk window and the probability level, the dynamic pollution flashover risk level of the insulator is determined and an early warning is issued by traversing the preset risk mapping table; During the correction process, the first parameter is used to slow down the formation rate of surface conductive channels; The step of correcting the first time window based on the first parameter includes: delaying the start time of the first time window by a first duration, wherein the first duration is positively correlated with the value of the first parameter. The step of determining the probability of irreversible surface contamination within the second time window based on the second parameter and the regional pollution source information includes: when the regional pollution source information indicates a risk of bird damage and the second parameter is lower than a preset threshold, the probability level of bird pecking damage or highly adhesive bird droppings contamination is determined to be high.

2. The method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to claim 1, characterized in that, When the insulator is equipped with an independent bird protection device, the calculation of the equivalent creepage distance includes the increase in air gap between the insulators caused by the independent bird protection device.

3. The method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to claim 1, characterized in that, The risk mapping table is calibrated based on multiphysics simulation, and the calibration process includes: Establish a finite element model of an insulator that includes different material structural parameters; Fluid dynamics simulation was coupled into the finite element model of the insulator to simulate the pollution settling process under different wind speeds and wind directions; Electrothermal simulation was coupled into the finite element model of the insulator to simulate the surface leakage current distribution under different humidity and temperature conditions. Based on the simulation results, a database of the correspondence between the material structure parameter combination, the environmental time window and the flashover voltage margin is established, and the risk mapping table is generated accordingly.

4. The method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to claim 3, characterized in that, The risk mapping table is dynamically updated based on historical flashover event data of the target transmission corridor and the material and structural parameters of the corresponding insulators to correct the calibration results of the multiphysics simulation.

5. The method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to claim 1, characterized in that, The dynamic flashover risk level is used to generate a heat map of flashover risk for line insulators. The heat map uses tower location and phase sequence as coordinates and renders color depth according to the risk level. The duration of the actual effective wetting risk window and the probability level together affect the saturation of the color.

6. The method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions according to claim 1, characterized in that, The early warning operation includes: If the third parameter of the insulator is greater than the average value of the line insulator, then a partial discharge re-inspection based on image recognition is initiated. If the first parameter of the insulator shows that its hydrophobicity level is higher than the preset standard, its routine inspection cycle will be extended accordingly.

7. A system for predicting the risk of flashover of rigid insulators considering micrometeorological conditions, the system being applied to the method for predicting the risk of flashover of rigid insulators considering micrometeorological conditions as described in claim 1, characterized in that... The system includes: The data acquisition module is used to acquire micro-meteorological monitoring data of the target transmission corridor, regional pollution source information, and material structure parameters of each insulator in the line; the material structure parameters include at least a first parameter characterizing surface hydrophobicity, a second parameter characterizing surface hardness, and a third parameter characterizing effective creepage distance; The time window identification module is used to identify and output a first time window that meets the preset condensation formation conditions and a second time window that meets the preset pollutant deposition conditions based on the micrometeorological monitoring data. The wetting risk window correction module is used to correct the first time window for each insulator based on the first parameter to obtain the actual effective wetting risk window corresponding to the insulator. The contamination probability assessment module is used to determine the probability of irreversible surface contamination occurring within the second time window based on the second parameter and the regional pollution source information, and output the probability level. The pollution flashover risk warning module is used to determine the dynamic pollution flashover risk level of the insulator and issue a warning by using the third parameter as a benchmark, combining the actual effective wetting risk window and the probability level, and traversing a preset risk mapping table.

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