Improved power loss estimation for device- to system-level analysis

Power converters are found nearly everywhere electric power is used and are ubiquitous in renewable energy generation and electric vehicles. All power converters suffer from losses, Modern power converters have very high efficiency, often reaching peak efficiency > 95%. However, the losses in these systems are still significant and must be considered for thermal and financial purposes. To enable maximum loss reduction, accurate estimation of the losses at the design stage is mandatory. Gallium Nitride (GaN) power switches are an emerging technology due to their high efficiency operation and smaller size compared to traditional Silicon devices. To date, simplistic power loss models have been employed for loss predication and thermal management design with Gallium Nitride (GaN). However, these simplistic models do not provide accurate loss prediction, resulting in over-design of the thermal management systems. This work proposes a comprehensive method to predict losses in GaN devices using high-accuracy thermal measurement. The proposed model is validated experimentally and provides a four-fold increase in loss predication accuracy compared to traditional methods. Having established accurate converter-level loss prediction, a higher level of abstraction is then considered. Existing system-level analysis focuses on distribution losses and oversimplifies converter losses by assuming fixed efficiency. In reality, converter losses are highly variable under different operating conditions. In this work, the Rapid Loss Estimation equation (RLEE) is proposed to provide computationally simple loss prediction under all operating conditions. The RLEE extracts detailed loss behavior from multi-domain simulation into a computationally simple parametric equation. Using the RLEE high accuracy and high speed loss estimation is obtained, as demonstrated in a DC microgrid with three different converters. Ultimately, the tools developed in this work improve loss estimation in power converters from the component level up to the system level. The proposed techniques, while explained through specific examples, are widely applicable and can be readily implemented to other devices, topologies and systems. Improved loss estimation is valuable at all levels, from designing thermal management systems for individual devices in a converter to optimizing the financial outcomes of a complex grid with multiple power converters.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat