In the salt spray corrosion environment, the chemical corrosion resistance of pressed polymer insulators for low voltage overhead insulated conductors is related to the safe and stable operation of the power system, and the life prediction model provides a scientific basis for operation and maintenance decisions. The following systematically analyzes the relevant strategies and methods from material properties, environmental impact to model construction.
First, the material composition and structure of pressed polymer insulators for low voltage overhead insulated conductors directly determine their chemical corrosion resistance. Common polymer materials such as silicone rubber and ethylene propylene diene monomer (EPDM) have different tolerances to salt spray corrosion due to the differences in the stability of chemical bonds in their molecular structures. The Si-O bond energy in silicone rubber molecules is high, with good chemical inertness and hydrophobicity, which can effectively resist the corrosion of chloride ions, sodium ions, etc. in salt spray; although EPDM materials have certain elasticity and weather resistance, their carbon-carbon double bonds and other structures are easily oxidized in strong salt spray environments, resulting in material aging and performance degradation. In addition, the type and ratio of fillers also affect the corrosion resistance. For example, the addition of fillers such as nano-alumina and titanium dioxide can enhance the compactness of the polymer matrix, hinder the penetration of salt spray, and delay the process of chemical corrosion.
Secondly, the complexity of the salt spray corrosion environment exacerbates the aging process of pressed polymer insulators for low voltage overhead insulated conductors. After the electrolytes such as sodium chloride in the salt spray are deposited on the surface of the insulator, they form a conductive liquid film when they meet water, which reduces the insulation resistance on the surface of the insulator and causes partial discharge. At the same time, chloride ions have extremely strong penetration ability, which can destroy polymer molecular chains and accelerate material degradation. The coastal areas have high humidity and strong sunshine. Salt spray, ultraviolet rays and moisture work together to cause powdering and cracking on the surface of the insulator. In addition, temperature changes cause the insulator to expand and contract, which will cause microcracks inside the material, providing a channel for salt spray penetration, further accelerating chemical corrosion, and shortening the actual service life of the insulator.
Furthermore, the construction of a life prediction model requires comprehensive consideration of the coupling of multiple factors. Traditional life prediction methods are mostly based on a single environmental factor, which makes it difficult to accurately reflect the actual aging of insulators in a salt spray corrosion environment. Modern life prediction models usually use multi-physical field coupling analysis to take into account factors such as salt spray concentration, humidity, temperature, and ultraviolet radiation intensity. By establishing a material aging kinetic equation and combining it with the finite element analysis method, the diffusion process of salt spray in the insulator material, the electrochemical reaction process, and the mechanical stress change are simulated to quantify the weight of each factor on the attenuation of insulator performance, so as to more accurately predict the life of the insulator. For example, the Arrhenius equation is used to describe the effect of temperature on the material aging rate, and the diffusion law of salt spray ions is analyzed in combination with Fick's law to construct a comprehensive prediction model.
Then, data acquisition and monitoring technology is the basis for building an accurate life prediction model. In a salt spray corrosion environment, by installing temperature and humidity sensors, salt density monitors, partial discharge sensors, etc. on the surface of the insulator, environmental parameters and insulator status data are collected in real time. At the same time, the insulators are sampled and tested regularly to analyze the physical properties (such as tensile strength and elongation at break), chemical properties (such as changes in molecular chain structure), and electrical properties (such as surface resistivity and flashover voltage) of the materials. These data provide a large number of training samples for the life prediction model. Through machine learning algorithms such as neural networks and support vector machines, the data are analyzed and processed, the laws behind the data are mined, the model parameters are continuously optimized, and the accuracy and reliability of the prediction are improved.
Next, the accelerated aging test is an important means to quickly obtain the aging data of the insulator. Since the actual service life of pressed polymer insulators for low voltage overhead insulated conductors is as long as decades, by conducting accelerated aging tests and simulating extreme salt spray environments, the performance attenuation data of the insulator can be obtained in a relatively short time. In the test, the aging process of the material is accelerated by increasing the salt spray concentration, temperature, ultraviolet intensity and other conditions. For example, the insulator is placed in a salt spray chamber, the salt spray deposition, temperature and humidity are controlled, the performance changes of the insulator are regularly detected, and the accelerated aging test data is combined with the actual operation data to verify the effectiveness of the life prediction model. At the same time, according to the test results, the aging law of the insulator under different accelerated conditions is analyzed to provide a basis for the parameter adjustment of the life prediction model.
In addition, the verification and correction of the model runs through the entire process of life prediction. After the life prediction model is built, the model prediction results need to be compared and verified with the actual operation data. If there is a deviation between the prediction result and the actual situation, it is necessary to analyze the cause of the deviation. It may be that the model does not consider certain key factors, or the model parameter settings are unreasonable. By continuously adjusting the model structure, optimizing parameters, and introducing new influencing factors, the model is corrected and improved. In addition, with the increase of insulator operation time and changes in environmental conditions, the model input data is continuously updated and the model parameters are dynamically adjusted to ensure that the life prediction model can always accurately reflect the actual aging state of the insulator, providing reliable support for the operation and maintenance decision-making of the power system.
Finally, the practical application of the life prediction model is of great significance to the operation and maintenance of the power system. Based on accurate life prediction, the power department can formulate a scientific and reasonable insulator replacement plan to avoid power outages caused by insulator aging and failure, and reduce operation and maintenance costs. At the same time, by predicting the life of insulators in different regions and different types, the selection and configuration of insulators can be optimized to improve the overall reliability of the power system. In addition, the life prediction model can also provide direction for the research and development of new materials and process improvements, promote the performance improvement of pressed polymer insulators for low voltage overhead insulated conductors in salt spray corrosion environments, and promote the sustainable development of the power industry.
