Inferring LTI Dynamics of Inverter Based Resources in Isolated and Networked Scenarios
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This thesis demonstrates multiple methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs) and aggregating those dynamics into larger networks. First, a model of a GFM as a current-controlled voltage source is examined. The nonlinear power dynamics are analytically linearized and the behavior is compared to the original nonlinear model. Second, the power and frequency outputs of a more complex GFM model are fed into system identification software in order to fit them to a predetermined linear time-invariant (LTI) system and learn system parameters such as inertia and droop. For comparison, the same system identification techniques are then applied to the outputs of a simplified synchronous generator model. Finally, a modified IEEE 14-bus network configuration including five GFMs and three variable loads is simulated, and the resulting power and frequency dynamics are fit to the same LTI system. The intent is threefold: first, to demonstrate the appropriateness of LTI models in describing the power dynamics of individual GFMs and synchronous generators, in order to facilitate analysis of larger networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool for discovering the values of important parameters in the absence of explicit vendor models.
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University of Minnesota M.S.E.C.E. thesis. May 2024. Major: Electrical/Computer Engineering. Advisor: Sairaj Dhople. 1 computer file (PDF); vi, 30 pages.
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Helman, Sam. (2024). Inferring LTI Dynamics of Inverter Based Resources in Isolated and Networked Scenarios. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/264257.
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