A salt fog environment simulation system for insulator performance detection

Abstract

The application discloses a salt fog environment simulation system for insulator performance detection, and relates to the technical field of performance detection.The system comprises an environment simulation module, an evaluation monitoring module, an analysis processing module, a simulation prediction module and a safety protection module, and is applied to a closed environment.The technical points are as follows: the simulation prediction module can not only predict the future insulator fog flashover voltage, but also combine the lightning voltage estimation value and the resistance value as self factors, and combine the temperature, humidity and wind speed as environmental factors, so as to comprehensively obtain an insulator performance evaluation value Ipev which can directly reflect the insulator performance.By comparing the insulator performance evaluation value Ipev with an evaluation threshold value Vol, whether the insulator performance is abnormal can be known according to the comparison result, and the insulator with abnormal performance can be timely warned, so that the reliability and efficiency of the system in use are embodied.

Description

Technical Field

[0001] This invention relates to the field of performance testing technology, specifically to a salt spray environment simulation system for insulator performance testing. Background Technology

[0002] Insulator performance testing is a process of evaluating the electrical, mechanical, and physical properties of insulators through a series of tests and assessments. Specifically, insulator performance testing typically includes electrical performance testing, mechanical performance testing, physical performance testing, thermal performance testing, and humidity performance testing. Electrical performance testing includes breakdown voltage testing, flashover voltage testing, and insulation resistance testing, used to evaluate the insulator's withstand voltage and insulation performance under a given electric field condition. Mechanical performance testing includes wind resistance, vibration resistance, and mechanical strength testing, used to evaluate the insulator's structural strength and its ability to withstand earthquakes and wind. Physical performance testing includes insulator visual inspection, color change, surface contamination, and flashover testing, used to evaluate the insulator's appearance and physical condition.

[0003] The existing Chinese patent application CN115932603A, entitled "Detection Device System and Detection Method for Insulation Performance of Fuel Cell Stacks," discloses a technical solution including an environmental control device and a data acquisition device. The data acquisition device includes an insulation detector, and the fuel cell stack under test is placed in the environmental control device. The data acquisition device and the environmental control device are electrically connected for recording the insulation performance of the fuel cell stack under test. The environmental control device includes a coolant circulation simulation device, a reaction gas chamber simulation device, and a humidity condition simulation device. This invention establishes a test platform simulating the humidity, temperature changes, and airflow of a fuel cell stack under actual operating conditions. This helps to identify the actual causes affecting the insulation resistance value of the fuel cell stack under operating conditions, thus contributing to improving the insulation resistance value. While it can simulate the required operating environment, it does not comprehensively consider various factors; it only provides an assessment of insulation performance based on changes in insulation value, without offering specific and clear indications regarding the overall performance of the battery.

[0004] Based on the aforementioned patents and existing technologies, the performance testing of porcelain insulators typically involves constructing a closed environment to simulate various actual conditions in order to obtain the insulation performance of the porcelain insulators. However, no corresponding simulation experiments have been conducted on the flashover mechanism of porcelain insulators under salt spray conditions. Furthermore, the overall performance evaluation of the porcelain insulator does not take into account comprehensive factors, and simply considering the resistance change of the porcelain insulator cannot determine the accuracy of subsequent performance evaluations. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a salt spray environment simulation system for insulator performance testing. Utilizing a simulation prediction module, it can not only predict future insulator fog flashover voltage but also combine the predicted flashover voltage and resistance value as intrinsic factors, along with temperature, humidity, and wind speed as environmental factors, to comprehensively derive an insulator performance evaluation value Ipev that intuitively reflects insulator performance. By comparing this value with the evaluation threshold Vol, the system can determine whether there are any abnormalities in insulator performance and promptly issue warnings for insulators with abnormal performance. This demonstrates the system's reliability and efficiency in use and solves the problems mentioned in the background technology.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A salt spray environment simulation system for insulator performance testing, the system comprising:

[0010] The environmental simulation module includes an environmental control unit and a load simulation unit. The environmental control unit provides environmental parameters for insulators placed in a closed environment, and the load simulation unit simulates the electrical loads borne by the insulators under working conditions.

[0011] The assessment and monitoring module builds a salt spray simulation model, records and analyzes the collected insulator-related parameters according to the preset experimental plan, evaluates the relationship between the duration of salt spray pollution and the insulator-related parameters based on the analysis results, and establishes a monitoring model to predict changes in the insulator-related parameters.

[0012] The analysis and processing module conducts experimental observations on insulators under different environmental parameters in a preset salt spray environment, obtains the electrical load of the insulator and the environmental parameters when fog flashover occurs, uses analysis to obtain the correlation between environmental parameters and fog flashover characteristics, builds a mechanism explanation model, and explains the fog flashover mechanism of insulators under preset salt spray environment conditions based on the correlation results.

[0013] The simulation prediction module acquires the monitoring parameters and the corresponding actual fog flashover voltage values ​​as a dataset. After correlation analysis, a calculation model for the fog flashover voltage of the insulator is established. After the calculation model is optimized, the corresponding monitoring parameters are input to obtain the calculated fog flashover voltage prediction value.

[0014] The assessment and early warning module includes an assessment unit and an early warning unit. The assessment unit obtains assessment parameters, builds a data analysis model, and generates an insulator performance assessment value Ipev. The early warning unit compares the insulator performance assessment value Ipev at different times with a preset assessment threshold Vol. If there is a situation where the insulator performance assessment value Ipev does not exceed the assessment threshold Vol, a first-level early warning signal is issued.

[0015] Furthermore, in the environmental control unit, environmental parameters include, but are not limited to, salt spray concentration, wind speed, wind direction, temperature, and humidity. In the load simulation unit, electrical loads include actual current, actual voltage, leakage current, and resistance value.

[0016] Furthermore, in the assessment and monitoring module, the insulator-related parameters collected include leakage current and resistance value.

[0017] Furthermore, the collected insulator-related parameters were analyzed. Correlation and regression analysis techniques were used to assess the relationship between the duration of salt spray pollution and the insulator-related parameters. The specific process of establishing a monitoring model to predict changes in the insulator-related parameters is as follows:

[0018] S101. First, conduct a correlation analysis to calculate the correlation coefficient between the duration of salt spray pollution and relevant parameters of the insulator.

[0019] S102. If the correlation analysis shows that there is a linear relationship, a simple linear regression model is established. In this model, the duration of salt spray pollution is used as the independent variable and the relevant parameters of the insulator are used as the dependent variable. The regression coefficients are obtained by fitting the regression equation.

[0020] S103. If there are several environmental parameters, use multiple linear regression analysis. In this model, in addition to the duration of salt spray pollution, other environmental parameters are included as independent variables, and insulator-related parameters are included as dependent variables. By fitting the regression equation, the relative impact of different environmental parameters on insulator-related parameters is evaluated.

[0021] S104. Use the fitted regression model to evaluate and predict parameters. Determine the degree of influence of each independent variable on the relevant parameters of the insulator through the regression coefficients of the regression equation. Use the regression model to predict the parameters. Based on the given salt spray exposure time, predict the changes in the relevant parameters of the insulator.

[0022] Furthermore, the process of obtaining the flashover mechanism of insulators under preset salt spray environmental conditions is as follows:

[0023] S201. In the set salt spray environment, conduct experimental observations on insulators under different salt spray conditions and record the fog flashover phenomenon of insulators under different environmental conditions.

[0024] S202. Collect the electrical load of the insulator and environmental parameters during fog flashover;

[0025] S203. Process and analyze the collected data to obtain the influence of various factors, including fog water conductivity, temperature and wind speed, on the fog flashing characteristics of the insulator. The analysis method used is either statistical analysis or correlation analysis to obtain the correlation between different environmental factors and fog flashing characteristics.

[0026] S204. Build a mechanism explanation model and, based on the results of data analysis, explain the fog flashover mechanism of insulators under the preset salt spray environment conditions.

[0027] Furthermore, in the simulation prediction module, the monitored parameters include the conductivity of the mist water and the density of contaminant salts on the insulator surface.

[0028] Furthermore, the process of establishing a calculation model for insulator flashover voltage after correlation analysis is as follows: perform correlation analysis on the dataset, select and extract the required features based on the analysis results, including the product, difference, and correlation coefficient between data in the dataset, and perform data transformation. Select support vector regression as the modeling method based on the required features to establish a calculation model for insulator flashover voltage, train the calculation model using the dataset, and evaluate the model using cross-validation to obtain the evaluation index as the evaluation result.

[0029] Furthermore, in the evaluation unit, the evaluation parameters include the estimated fog flashover voltage, resistance value, temperature, humidity, and wind speed, and the evaluation parameters are dimensionless before generating the insulator performance evaluation value Ipev.

[0030] Furthermore, the formula used to generate the insulator performance evaluation value Ipev is as follows:

[0031]

[0032] In the formula, Ev represents the estimated value of fog flashing voltage, Re represents the resistance value, Te represents the temperature, Hu represents the humidity, Wi represents the wind speed, a1, a2, a3, a4, and a5 are the preset proportional coefficients of the estimated value of fog flashing voltage, resistance value, temperature, humidity, and wind speed, respectively, a1>a2>a3>a4>a5>0, and G is a constant correction coefficient.

[0033] Furthermore, after comparing the insulator performance evaluation value Ipev at different times with the preset evaluation threshold Vol, if the insulator performance evaluation value Ipev exceeds the evaluation threshold Vol, no response is made; if there is a situation where the insulator performance evaluation value Ipev does not exceed the evaluation threshold Vol, a first-level warning signal is issued.

[0034] (III) Beneficial Effects

[0035] This invention provides a salt spray environment simulation system for insulator performance testing, which has the following advantages:

[0036] 1. Design the entire environmental simulation system. Parameters or equipment can be flexibly selected according to actual needs. The system simulates the real working conditions of the insulator to ensure the accuracy of the test results. By combining the established simulation prediction module, the system can comprehensively consider the influence of fog conductivity and surface contamination salt density on insulator flashover and predict the flashover voltage of the insulator under different conditions, facilitating further research and evaluation of the insulator's performance.

[0037] 2. Combining the simulation prediction module and the evaluation and early warning module, the simulation prediction module can not only predict the future flashover voltage of insulators, but also combine the flashover voltage estimate and resistance value as intrinsic factors, and temperature, humidity and wind speed as environmental factors to comprehensively derive an insulator performance evaluation value Ipev that can intuitively reflect the insulator performance. By comparing it with the evaluation threshold Vol, the system can determine whether there are any abnormalities in the insulator performance and can promptly issue early warnings for insulators with abnormal performance, demonstrating the reliability and efficiency of the system in use. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the modular structure of the salt spray environment simulation system for insulator performance testing according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 This invention provides a salt spray environment simulation system for insulator performance testing. The system includes an environmental simulation module, an evaluation and monitoring module, an analysis and processing module, a simulation and prediction module, and a safety protection module. The entire system is used in a closed environment to ensure the accuracy and stability of the simulation data, and the insulator, such as a porcelain insulator, is placed in the closed environment.

[0041] The environmental simulation module includes an environmental control unit and a load simulation unit;

[0042] The environmental control unit provides controllable and stable environmental parameters for insulators placed in a closed environment. These environmental parameters include salt spray concentration, wind speed, wind direction, temperature, and humidity. By providing these environmental parameters, the testing requirements can be met.

[0043] Salt spray is atomized salt water, sprayed using electrically powered atomizing nozzles. Uniformity of the salt spray must be maintained, simulating the required concentration. Wind speed and direction are provided directly by fans or air conditioning. The outlet angle of these fans or air conditioners is adjustable to accommodate different wind directions, and wind speed can be adjusted by changing the power required by the fans or air conditioners. Temperature is adjusted by installing a controllable heater in the enclosed environment. Humidity corresponds to the amount of salt spray added to the enclosed environment; the greater the amount of salt spray, the greater the humidity. Therefore, humidity can be adjusted by regulating the duration of the atomizing nozzle spray.

[0044] The load simulation unit is used to simulate the electrical loads that insulators bear under working conditions, including but not limited to actual current, actual voltage, leakage current, and resistance value. It uses a power supply and a load simulator to generate the required electrical loads to simulate the real working conditions of the insulators. The power supply supplies power to the load simulator and controls the parameters of the load simulator, which can generate different magnitudes of current or voltage to simulate the changes that the insulators bear under real working conditions.

[0045] The evaluation and monitoring module builds a salt spray simulation model, records and analyzes the collected insulator-related parameters according to the preset experimental plan, evaluates the relationship between the duration of salt spray pollution and the insulator-related parameters based on the analysis results, and establishes a monitoring model to predict the changes in the insulator-related parameters, reflecting the impact of the duration of salt spray pollution on parameters such as leakage current and insulation resistance of insulators.

[0046] The above study investigated the effects of the duration of salt spray pollution on relevant parameters of insulators, including leakage current and resistance, under different environmental conditions. The specific process is as follows:

[0047] The salt spray simulation model was built to generate and control salt spray concentration, wind speed, wind direction, temperature, and humidity.

[0048] Different environmental parameters, such as wind speed, wind direction, temperature and humidity, as well as the range and variation step of the duration of pollution, are selected. The selection of these parameters is based on the actual application environment or research requirements.

[0049] Based on the selected environmental parameter range and step size, a pre-set experimental plan is formulated, including setting different environmental parameter conditions and duration of action, and recording relevant parameters of the insulator under each condition, including leakage current and resistance value. Under each experimental condition, corresponding sensors and instruments, such as current sensors and resistance sensors, are used to collect the leakage current and resistance value of the insulator. Note: In actual application, other parameters can also be collected to ensure the accuracy and reliability of the collected data. Pay attention to the time of data recording and experimental conditions.

[0050] The collected insulator-related parameters were analyzed, and correlation and regression analysis techniques were used to evaluate the relationship between the duration of salt spray pollution and the insulator-related parameters. The specific process of establishing a monitoring model to predict changes in the insulator-related parameters is as follows:

[0051] Correlation analysis: First, a correlation analysis is performed to calculate the correlation coefficient between the duration of salt spray pollution and relevant parameters of the insulator, such as the Pearson correlation coefficient. The value of the correlation coefficient is used to determine the strength and direction of the linear relationship between the two variables, such as positive or negative correlation.

[0052] Simple linear regression: If the correlation analysis shows that there is a linear relationship, a simple linear regression model is established. In this model, the duration of salt spray pollution is used as the independent variable, and the relevant parameters of the insulator, such as leakage current or resistance value, are used as the dependent variable. By fitting the regression equation, the regression coefficients are obtained, which represent the influence of the duration of pollution on the parameter changes.

[0053] Multiple linear regression: If there are several environmental parameters, such as wind speed, temperature and humidity, multiple linear regression analysis is used. In this model, in addition to the duration of salt spray pollution, other environmental parameters are also included as independent variables, such as wind speed, wind direction, temperature and humidity, and insulator-related parameters are used as dependent variables. By fitting the regression equation, the relative influence of different environmental parameters on insulator-related parameters is evaluated.

[0054] Model evaluation and prediction: The fitted regression model is used for parameter evaluation and prediction. The regression coefficients of the regression equation are used to determine the degree of influence of each independent variable on the insulator-related parameters. Using the regression model, parameter prediction can be performed to predict the changes in the insulator-related parameters based on the given salt spray exposure time.

[0055] Specifically, the aforementioned temperature, humidity, and wind speed are parameters that reflect the influence of environmental factors on the duration of salt spray. Temperature affects the duration of salt spray; generally, higher temperatures may promote the evaporation and drying of salt spray, thus shortening its duration, while lower temperatures may prolong it. Humidity is a key factor affecting the duration of salt spray; higher humidity usually leads to condensation and deposition of salt spray, extending its duration, while lower humidity accelerates the evaporation and dissipation of salt spray, thus shortening its duration. Wind speed has a significant impact on the propagation and dilution of salt spray. At higher wind speeds, salt spray is likely to be rapidly diluted and dispersed, resulting in a relatively shorter duration, while at lower wind speeds, salt spray may accumulate and linger more easily, resulting in a relatively longer duration.

[0056] The analysis and processing module simulates the environment near the ocean or a salt lake under a preset salt fog environment, i.e., a high salt fog environment. This high salt fog environment refers to a simulated environment with a high salt fog concentration. Under this environment, a large amount of salt is suspended in the air, forming particulate salt fog. Experimental observations are conducted on insulators under different environmental parameters to obtain the electrical load of the insulator and the environmental parameters when fog flashover occurs. The correlation between environmental parameters and fog flashover characteristics is obtained by analysis, and a mechanism explanation model is built. Based on the correlation results, and combined with known research results, the fog flashover mechanism of insulators under the preset salt fog environment conditions can be explained.

[0057] The process of obtaining the fog flashover mechanism of insulators under preset salt spray environment conditions is as follows:

[0058] Experimental observation: In the set high salt spray environment, experiments were conducted on insulators under different salt spray conditions to record the fog flashing phenomenon of insulators under different environmental conditions, such as corona discharge and arc discharge.

[0059] Data acquisition: Using appropriate measuring equipment, such as voltage and current sensors, collect the electrical parameters (i.e., electrical load) of the insulators and the environmental parameters during fog flashover, such as voltage, current, wind speed, and temperature.

[0060] Data analysis: The collected data is processed and analyzed to obtain the influence of various factors, including fog conductivity, temperature and wind speed, on the fog flashover characteristics of insulators. The analysis method used is statistical analysis or correlation analysis to obtain the correlation between different environmental factors and fog flashover characteristics.

[0061] Mechanism Explanation: A mechanism explanation model is built. Based on the results of data analysis and combined with existing theoretical knowledge or research results, the fog flashover mechanism of insulators under the pre-set salt spray environment conditions is explained, including the ionization ability of salt spray and the influence of surface contamination salt density on the electric field distribution on the insulator surface.

[0062] For example, a series of predetermined experiments were designed to understand the flashover mechanism of insulators under high salt spray conditions. Experimental conditions with different salt spray concentrations, temperatures, and wind speeds were set. Under each condition, corona discharge and arc discharge phenomena of the insulators were observed, and the corresponding electrical parameters (i.e., electrical loads) and environmental parameters were recorded. Based on the analysis of the collected data, it was concluded that the conductivity of the mist water, temperature, and wind speed have a significant impact on the flashover voltage and frequency of the insulators. Under the predetermined salt spray conditions, the increase in mist water conductivity led to a decrease in the flashover voltage of the insulators. Simultaneously, the frequency of corona discharge and arc discharge was higher under high wind speed and low temperature conditions. By explaining the mechanism, it was concluded that under the predetermined salt spray conditions, the increased excitation ion density of salt water droplets on the surface led to an increase in flashover discharge of the insulators.

[0063] The simulation prediction module acquires the monitoring parameters and the corresponding actual fog flashover voltage values ​​as a dataset. After correlation analysis, a calculation model for the fog flashover voltage of the insulator is established. After the calculation model is optimized, the corresponding monitoring parameters are input to obtain the calculated fog flashover voltage prediction value.

[0064] Monitoring parameters include the conductivity of the mist water and the density of contaminant salts on the insulator surface;

[0065] The conductivity of mist water is obtained by using a conductivity meter or conductivity instrument. These instruments immerse the conductivity sensor in a closed environment and determine the conductivity of the mist water by measuring the degree of conductivity.

[0066] The method for obtaining the surface contamination salt density of an insulator is as follows: The surface contamination salt density of an insulator can be measured using a salt density meter. The measuring head is placed on the surface of the insulator, a certain amount of salt water is dripped onto the measuring head, and the contamination salt density is calculated by measuring the change in density.

[0067] The process of establishing the calculation model for insulator flashover voltage after correlation analysis is as follows:

[0068] Correlation analysis is performed on the dataset. Based on the analysis results, the required features are selected and extracted, including the product, difference, and correlation coefficient between data in the dataset. Data transformations are then performed, such as normalization. Based on the required features, an appropriate modeling method is selected to establish a calculation model for insulator fog flashover voltage, such as decision tree, neural network, and support vector regression. In this application, support vector regression is used. The calculation model is trained using the dataset, and cross-validation is used to evaluate the model. The evaluation index is obtained as the evaluation result, and the evaluation index is the root mean square error.

[0069] The process of optimizing a computational model involves adjusting its hyperparameters, such as regularization parameters and learning rate, based on the model's evaluation results, in order to improve the model's accuracy and generalization ability.

[0070] By adopting the above technical solution, the entire environmental simulation system can be designed, allowing for flexible selection of parameters or equipment combinations according to actual needs. The system simulates the actual working conditions of the insulator, ensuring the accuracy of the test results. By combining the established simulation prediction module, the system can comprehensively consider the influence of fog conductivity and surface contamination salt density on insulator flashover, and can predict the flashover voltage of the insulator under different conditions, facilitating further research and evaluation of the insulator's performance.

[0071] The assessment and early warning module includes an assessment unit and an early warning unit;

[0072] The evaluation unit is used to acquire evaluation parameters, including the estimated fog flashover voltage, resistance value, temperature, humidity, and wind speed. After dimensionless processing of the evaluation parameters, a data analysis model is built to generate the insulator performance evaluation value Ipev, based on the following formula:

[0073]

[0074] In the formula, Ev represents the estimated fog flash voltage, Re represents the resistance value, Te represents the temperature, Hu represents the humidity, Wi represents the wind speed, a1, a2, a3, a4, and a5 are the preset proportional coefficients for the estimated fog flash voltage, resistance value, temperature, humidity, and wind speed, respectively, a1>a2>a3>a4>a5>0, and G is a constant correction coefficient, the specific value of which can be adjusted and set by the user or generated by fitting the analysis function.

[0075] It should be noted that: in the above formula, the predicted fog flashover voltage and resistance value are factors related to the insulator itself, while temperature, humidity, and wind speed are factors of the environment in which the insulator is located. Therefore, they need to be calculated separately. Generally, a higher predicted fog flashover voltage means that the insulator has better withstand voltage performance, and the insulator performance evaluation value Ipev will also be higher, showing a positive correlation. Fog flashover voltage is a characterization of the insulator's withstand voltage. The more the insulator can resist fog flashover voltage, the higher its performance evaluation value. The resistance value is usually related to the leakage current of the insulator. A lower resistance value means that the leakage current of the insulator is smaller, the reliability is higher, and the evaluation value will also be higher, showing an inverse correlation. Good insulation performance will prevent the current from flowing between the insulator surface and the environment.

[0076] Generally, lower temperatures are beneficial to insulator performance, resulting in higher evaluation values. This is because high temperatures lead to thermal decomposition and degradation of the insulator material, thus affecting its insulation performance. Within a suitable range, increased humidity usually reduces insulator performance, leading to relatively lower evaluation values. This is because humidity promotes the formation of conductive paths on the insulator surface, increasing leakage current and flashover risk. Higher wind speeds may increase the shedding and impact of deposits (such as dust and salt particles) on the insulator surface, further reducing insulator performance and resulting in lower evaluation values. The formula uses a weighted average of the predicted fog flashover voltage and the resistance value as the denominator. It also sums the values ​​of temperature, humidity, and wind speed and takes the square root to avoid an excessively large sum of these factors in the denominator. The sum of these factors in the denominator is also used as a correction value and added to the weighted average value. Finally, the result is multiplied by a constant correction coefficient G to obtain a second-corrected, accurate insulator performance evaluation value, Ipev. This Ipev represents the insulator performance evaluation value at the same moment.

[0077] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by those skilled in the art for each set of sample data. It can also be said that it is preset according to the actual situation, as long as it does not affect the proportional relationship between the parameter and the quantified value. The same explanation applies to the preset proportional coefficient and constant correction coefficient described in other formulas.

[0078] The early warning unit compares the insulator performance evaluation value Ipev at different times with the preset evaluation threshold Vol. If the insulator performance evaluation value Ipev exceeds the evaluation threshold Vol, it means that the insulator performance is normal and the system does not respond. If there is a situation where the insulator performance evaluation value Ipev does not exceed the evaluation threshold Vol, it means that the insulator performance is abnormal and a first-level early warning signal is issued to remind the staff that the insulator performance is poor or unqualified, and further testing or scrapping can be carried out.

[0079] By adopting the above technical solution—combining a simulation prediction module and an evaluation and early warning module—the simulation prediction module can not only predict the future flashover voltage of insulators, but also combine the predicted flashover voltage and resistance value as intrinsic factors, and temperature, humidity, and wind speed as environmental factors to comprehensively derive an insulator performance evaluation value Ipev that can intuitively reflect the insulator performance. By comparing this value with the evaluation threshold Vol, the system can determine whether there are any abnormalities in the insulator performance and promptly issue early warnings for insulators with abnormal performance, demonstrating the reliability and efficiency of the system in use.

[0080] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are derived from the most recent real-world situation by collecting a large amount of data and conducting software simulations. The formulas are set by those skilled in the art according to the actual situation.

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0082] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across several network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A salt spray environment simulation system for insulator performance testing, the system comprising: An environmental simulation module, comprising an environmental control unit and a load simulation unit, provides environmental parameters for an insulator placed in a closed environment through the environmental control unit, and simulates the electrical load borne by the insulator under working conditions using the load simulation unit, characterized in that; The assessment and monitoring module builds a salt spray simulation model, records and analyzes the collected insulator-related parameters according to the preset experimental plan, evaluates the relationship between the duration of salt spray pollution and the insulator-related parameters based on the analysis results, and establishes a monitoring model to predict changes in the insulator-related parameters. The analysis and processing module conducts experimental observations on insulators under different environmental parameters in a preset salt spray environment, obtains the electrical load of the insulator and the environmental parameters when fog flashover occurs, uses analysis to obtain the correlation between environmental parameters and fog flashover characteristics, builds a mechanism explanation model, and explains the fog flashover mechanism of insulators under preset salt spray environment conditions based on the correlation results. The simulation prediction module acquires the monitoring parameters and the corresponding actual fog flashover voltage values ​​as a dataset. After correlation analysis, a calculation model for the fog flashover voltage of the insulator is established. After the calculation model is optimized, the corresponding monitoring parameters are input to obtain the calculated fog flashover voltage prediction value. The assessment and early warning module includes an assessment unit and an early warning unit. The assessment unit obtains assessment parameters, builds a data analysis model, and generates an insulator performance assessment value Ipev. The early warning unit compares the insulator performance assessment value Ipev at different times with a preset assessment threshold Vol. If there is a situation where the insulator performance assessment value Ipev does not exceed the assessment threshold Vol, a first-level early warning signal is issued. In the evaluation unit, the evaluation parameters include the estimated fog flashover voltage, resistance value, temperature, humidity and wind speed, and the evaluation parameters are dimensionless before generating the insulator performance evaluation value Ipev. The formula used to generate the insulator performance evaluation value Ipev is as follows: In the formula, This indicates the estimated fog flashover voltage. Indicates the resistance value. Indicates temperature. Indicates humidity. Indicates wind speed. These are preset proportional coefficients for the estimated fog flash voltage, resistance value, temperature, humidity, and wind speed, respectively. G is a constant correction coefficient.

2. The salt spray environment simulation system for insulator performance testing according to claim 1, characterized in that: In the environmental control unit, environmental parameters include, but are not limited to, salt spray concentration, wind speed, wind direction, temperature, and humidity. In the load simulation unit, electrical loads include actual current, actual voltage, leakage current, and resistance value.

3. The salt spray environment simulation system for insulator performance testing according to claim 2, characterized in that: In the assessment and monitoring module, the insulator-related parameters collected include leakage current and resistance value.

4. The salt spray environment simulation system for insulator performance testing according to claim 3, characterized in that: The specific process of analyzing the collected insulator-related parameters, using correlation analysis and regression analysis techniques to evaluate the relationship between the duration of salt spray pollution and the insulator-related parameters, and establishing a monitoring model to predict changes in the insulator-related parameters is as follows: S101. First, perform correlation analysis to calculate the correlation coefficient between the duration of salt spray pollution and the insulator-related parameters. S102. If the correlation analysis shows that there is a linear relationship, a simple linear regression model is established. In this model, the duration of salt spray pollution is used as the independent variable and the relevant parameters of the insulator are used as the dependent variable. The regression coefficients are obtained by fitting the regression equation. S103. If there are several environmental parameters, use multiple linear regression analysis. In this model, in addition to the duration of salt spray pollution, other environmental parameters are included as independent variables, and insulator-related parameters are included as dependent variables. By fitting the regression equation, the relative impact of different environmental parameters on insulator-related parameters is evaluated. S104. Use the fitted regression model to evaluate and predict parameters. Determine the degree of influence of each independent variable on the relevant parameters of the insulator through the regression coefficients of the regression equation. Use the regression model to predict the parameters. Based on the given salt spray exposure time, predict the changes in the relevant parameters of the insulator.

5. A salt spray environment simulation system for insulator performance testing according to claim 4, characterized in that: The process of obtaining the flashover mechanism of insulators under preset salt spray environmental conditions is as follows: S201. In the set salt spray environment, conduct experimental observations on insulators under different salt spray conditions and record the fog flashover phenomenon of insulators under different environmental conditions. S202. Collect the electrical load of the insulator and environmental parameters during fog flashover; S203. Process and analyze the collected data to obtain the influence of various factors, including fog water conductivity, temperature and wind speed, on the fog flashing characteristics of the insulator. The analysis method used is either statistical analysis or correlation analysis to obtain the correlation between different environmental factors and fog flashing characteristics. S204. Build a mechanism explanation model and, based on the results of data analysis, explain the fog flashover mechanism of insulators under the preset salt spray environment conditions.

6. The salt spray environment simulation system for insulator performance testing according to claim 5, characterized in that: In the simulation prediction module, the monitored parameters include the conductivity of the mist water and the density of contaminant salts on the insulator surface.

7. A salt spray environment simulation system for insulator performance testing according to claim 6, characterized in that: The process of establishing a calculation model for insulator flashover voltage after correlation analysis is as follows: perform correlation analysis on the dataset, select and extract the required features based on the analysis results, including the product, difference and correlation coefficient between data in the dataset, and perform data transformation, select support vector regression as the modeling method based on the required features, establish a calculation model for insulator flashover voltage, train the calculation model using the dataset, and evaluate the model using cross-validation to obtain the evaluation index as the evaluation result.

8. A salt spray environment simulation system for insulator performance testing according to claim 1, characterized in that: After comparing the insulator performance evaluation value Ipev at different times with the preset evaluation threshold Vol, if the insulator performance evaluation value Ipev exceeds the evaluation threshold Vol, no response is made; if there is a case where the insulator performance evaluation value Ipev does not exceed the evaluation threshold Vol, a first-level warning signal is issued.

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