Asset health prediction using medium voltage breaker travel curve data and artificial intelligence

Medium Voltage Switchgear and Breakers are key elements of the electrical distribution grid that need to be highly reliable. With the introduction of renewables, the number of breaking operations is expected to increase, multiplying the stress on installed breakers. In order to effectively plan maintenance and replacements, a good indication of the current asset health is needed, ideally as a single KPI that can be compared to a threshold. In this paper, we compare AI-generated anomaly scores to traditional metrics to understand their value as asset health KPIs.