Crwn.ai - Fortis - IEL demo 23

The goal of this project was to show that CRWN can detect, categorize and localize a number of issues that affect grid reliability and performance such as corona, tracking and arcing. CRWN tested the ability to distinguish between several degrees of partial discharge activity: 1 No Activity 2 Light Corona 3 Medium Corona 4 Tracking 5 Arcing CRWN did this using ultrasonic sensors and machine learning models. The demonstration includes the compiled results from one month of monitoring on live transmission lines with ten devices. CRWN worked with FortisBC to select poles with varying degrees of degradation to give the best chance of capturing arcing and tracking within a short time frame.