Abstract

Pollution on the surface of an insulator, combined with conditions such as fog, dew, or moisture, directly influences partial discharge (PD) activity, a key indicator of an insulator's aging process. Using an artificial pollution chamber, three classes of insulators were subjected to saline dew across multiple test runs, during which radio frequency emissions were recorded by an antenna. The contamination process was monitored and characterized using three indicators derived from the cumulative energy per interval, the cumulative average time between signals, and the cumulative distributed energy. By employing segmented regression analysis on these variables, it was possible to detect and quantify changes in PD activity as measured in the ultrahigh-frequency (UHF) band by a nearby antenna. Compared with galvanic, leakage-current-based approaches and artificial intelligence (AI)-based models, the proposed scheme has lower computational cost and provides operator tunable alert states for training-free, noncontact, trend-based early warning. In every test run, the developed algorithm identified at least one trend change by 80% of the test duration. Analyzing each trend change with the three indicators enabled graded alert states, demonstrating flexibility for assessing flashover (FO) risk. In all experiments conducted on the different types of insulators, FO consistently occurred during the highest alarm state.