F. Patrick McCluskey1, Clifton Buxbaum1, Sudip K. Mazumder2, Arif Sarwat3, Matt Ursino4, and Miles C. Russell4
1University of Maryland, College Park, Maryland, United States
2University of Illinois, Chicago, Illinois, United States
3Florida International University, Miami, Florida, United States
4Yaskawa Solectria Solar, Lawrence, Massachusetts, United States
For more information about this article and related research, please contact Prof. Patrick McCluskey
One of the most important elements for market acceptance of new technologies is ensuring reliability. Nowhere is this truer than in the shift from well characterized fossil fuel technologies to newer renewable and sustainable energy technologies. The key enabling technology driving these shifts is the development of power converters and inverters. Conventional approaches to assess reliability of these devices have severe drawbacks. Frequent redesigns, often with new parts having no historical data, limit the usefulness of methods based on historical data. Conversely, physics-of-failure approaches often do not capture the most relevant failure mechanisms, including those related to operationally induced electrical overstress and software. In this paper, we will discuss a revolutionary new reliability assessment approach that utilizes advancements in artificial intelligence (AI), machine learning, and data analytics, along with new techniques for characterizing and modeling failure mechanisms to improve power electronics reliability.
The reliability assessment method combines AI and machine learning algorithms for analyzing field failure data, with top down models that translate the impacts of grid-connected and grid-parallel mode dynamics and mode-transition dynamics on power systems, and reliability physics degradation models for key failure mechanisms that simulate the effects of both electrical and environmental degradation under field operational stresses. These models can be embedded in digital twins created specifically to replicate the design of current and new inverters. The output of these digital twins reflects the effects of aging and component degradation on system performance and will be transferable to multiple power electronic systems and platforms.
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