Degradation-Aware Self-Healing Control of Power Electronics Systems

Mississippi State University, Mississippi State MS

Investigators

Abstract

Computational power is everywhere. Sensors are increasingly low-cost and ubiquitous. Despite the extensive resources, modern power electronics systems (PESs) cannot pinpoint its degradation status and, hence, cannot perform self-healing to prevent costly failures. For example, wind turbines or photovoltaic (PV) systems are subjected to extreme temperature and humidity swings from -30ºC to 55ºC and 30% to 100% (e.g., offshore applications). Such a harsh climate and thermal (C&T) swings rapidly increase the failure rate and maintenance costs by up to 30% of the overall generation cost. Suppose their degradation and, hence, remaining useful lifetime (RUL) can be accurately measured or precisely predicted in advance. In that case, we can utilize existing PESs software or hardware to perform proactive self-healing through the adaptive control of degradation evolution, accumulation, acceleration, and, hence, RUL changes of the building blocks of power electronics in increasingly complicated modern energy systems. This could substantially enhance reliability, scheduling flexibility, and controllability while preventing costly downtimes. The outcome of this project will be utilized for interactive and hands-on learning programs to inspire K-12 children’s interest in STEM fields. This project will model the degradation of wide bandgap (WB) power switches under real-world C&T swings, which poses the critical bottleneck of exploiting degradation-aware self-healing (DASH) control in modern PESs. Specifically, we will develop a cascade generative adversarial networks learning and data purification strategy to effectively model the large reliability data of power electronics under a real-world C&T condition. The formulated data-driven models and multi-sensory tools will be fundamentally more accurate than state-of-the-art. Moreover, we will develop a systematic DASH control framework, enabling lifetime managed PES operations by understanding four system health conditions (healthy, intermediate degradation, self-healing, and failure) instead of a traditional heuristic assumption (healthy and failure). The formulated RUL estimation and DASH control tools are able to fundamentally transform the current design and control practices, creating a seamless integration of reliable WB switches into the wide spectrum of power electronics and energy systems under diverse C&T conditions. This will accelerate the migration toward an energy-efficient grid and transportation electrification while minimizing development cost and period and preventing unplanned downtime and catastrophic failures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.