Principal Investigator: Dr. Chanyeop Park
Ensuring the dielectric integrity of power electronic components and medium voltage (MV) distribution systems is becoming more challenging as power semiconductor technology continue to outpace those of electrical insulation and health prognostic techniques. While the increasing voltage rating and switching speed of power semiconductors provide a myriad of benefits including high power density and high efficiency, they also increase the risk of electrical aging and premature system failure compared to the conventional power systems. Dielectric materials, insulation coordination approaches, and prognostic tools currently available are rendered ineffective in systems driven by power electronics as the recurring, steep voltage pulses created by wide bandgap (WBG) power electronic switches induce larger and more frequent partial discharge (PD) and degrade electrical insulators at a higher pace. Therefore, developing novel dielectric materials that suppress PD and algorithms that accurately detect PD under recurring, steep voltage pulses is key to ensuring the dielectric integrity and continued success of grid modernization through GRAPES. This project is aimed at developing i) novel dielectric materials that show high dielectric strength and immunity to electrical aging and ii) learning-based PD source detection and classification techniques. The goal of this project is to address both topics outlined in the request for proposals (RFP) developed by the Cross Cutting Technology Working Group: 1) Novel insulation materials and 2) Partial discharge prognostics.