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In this work, we propose a differentiable programming approach to data-driven modeling of distribution systems for electromechanical transient stability analysis. Our approach combines the traditional ZIP load model with a deep neural network formulated as a constrained nonlinear least-squares problem. We will discuss the formulation, setup, and training of the proposed model as a differentiable program. Finally, we will compare and investigate the performance of this new load model and present the results on a medium-scale 350-bus transmission-distribution network.
Author Information
Jan Drgona (Pacific Northwest National Laboratory)
I am a data scientist in the Physics and Computational Sciences Division (PCSD) at Pacific Northwest National Laboratory, Richland, WA. My current research interests fall in the intersection of model-based optimal control, constrained optimization, and machine learning.
Andrew August (Pacific Northwest National Lab)
I'm a Data Scientist at Pacific Northwest National Lab, based in Seattle, WA, USA.
Elliott Skomski (Pacific Northwest National Laboratory)
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