The modeling activities supporting studies on the contamination of insulators, as planned for the 2019-2021 period of the System Research, aim to develop a short-term forecasting service for insulator contamination levels. This is achieved through a model that robustly and accurately simulates and connects the environmental phenomena most influencing the contamination processes of insulators. The primary objective is to develop a modeling chain that, starting from an air quality forecasting system, implements appropriate parameterizations to specifically describe the formation and evolution of deposits at the geometric scale of the insulators. Both modeling and experimental activities are conducted for this purpose.

The modeling development is based on the SMOKE-WRF-CAMx air quality forecasting system, which has been operational at RSE for several years and has proven suitable for this purpose. In 2020, as a synthesis tool to evaluate modeling performance in forecasting pollution levels, a methodology was developed and applied following the guidelines of the Forum for Air Quality Modeling (FAIRMODE). The systematic assessment of the model’s reliability in predicting atmospheric concentration levels is an essential step in the overall process of verifying the modeling system’s ability to forecast the evolution of contaminant deposits.

At the same time, the study and modeling of processes occurring at the insulator scale continued, primarily focused on developing a parameterization of the deposition mechanism to be implemented in the forecasting system. This activity relies on a micro-scale modeling chain consisting of the ANSYS/CFX computational fluid dynamics (CFD) code and the SPRAY-WEB stochastic Lagrangian code, coupled with the DePaSITIA library, developed during the 2019 activities. The SPRAY-WEB stochastic Lagrangian model estimates the atmospheric particulate matter deposited on the insulator, while the ANSYS/CFX code reconstructs the flow field and fluid properties at the obstacle scale, which are necessary as input data for the SPRAY-WEB code.

In 2020, the SPRAY-WEB – DePaSITIA stochastic Lagrangian model was used to estimate the atmospheric particulate matter (PM) deposited on a chain of three cap-and-pin electric insulators. The software (Open-Source and freely available on github.com and gitlab.com) was enhanced for application at the scale of a single insulator and subsequently verified through a sensitivity analysis with relative model inter-comparison, as there are no available measurements for validation in this application field.

Also, in 2020, field installations for studying the vertical variability of deposition and the effects of meteorological parameters, particularly precipitation, on deposits were made operational. Furthermore, experimental studies continued on the relationship between electrical conductivity and the chemical composition of atmospheric particulate matter under varying atmospheric thermodynamic conditions (T and RH), with the development of laboratory instrumentation (smog chamber) and subsequent preliminary functionality verification. Both field and laboratory experimental activities are aimed at providing crucial insights into the contamination process, ultimately leading to the development and improvement of the modeling system to make it increasingly suitable for planning and verifying the safety of the electrical system.