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Title: DriftSurf: Stable-State / Reactive-State Learning under Concept Drift
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Journal Name:
Proceedings of the 38th International Conference on Machine Learning, ICML'21
Sponsoring Org:
National Science Foundation
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  1. Steady-state modeling plays an important role in the design of advanced power converters. Typically, steady-state modeling is completed by time-stepping simulators, which may be slow to converge to steady-state, or by dedicated analysis, which is time-consuming to develop across multiple topologies. Discrete time state-space modeling is a uniform approach to rapidly simulate arbitrary power converter designs. However, the approach requires modification to capture state-dependent switching, such as diode switching or current programmed modulation. This work provides a framework to identify and correct state-dependent switching within discrete time state-space modeling and shows the utility of the proposed method within the power converter design process.