Transmission Simulator Based MIMO Response Reconstruction
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It features a framework of a firmware and software architecture in support of building a digital low-level RF control system for accelerators, where precised digital RF generation and measurement are needed across many RF channels. It primarily supports the Xilinx RFSoC chips (xczu48dr, xczu47dr, xczu29dr) and their evaluation boards (zcu208, zcu216), for a highly integrated solution enabling the need for synchronous low-level RF systems, including: multi-tile synchronization, external reference for sampling clocks, deterministic delay, aligned NCO phase for digital mixers, and built-in EPICS IOC.
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Abstract not provided.
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Explore the source record for details and available documents.
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Explore the source record for details and available documents.
Abstract not provided.
Modeling of power distribution system components that are valid for a wide range of frequencies are crucial for highly accurate modeling of electromagnetic transient (EMT) events. This has recently become of interest due to the improvements needed for the resilient operation of distribution systems. Vector fitting (VF) is a very popular and commonly used algorithm for wide band representations of power system components in EMT simulations. In this research, we present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages with respect to VF using examples of approximations of admittance matrices discussed in the literature. We show that MIAAA not only outperforms VF in terms of achieving better accuracy using lesser number of poles, but also has no numerical issues achieving convergence. In contrast to VF, MIAAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles. The novelty of this research work is the use of recent mathematical results to solve existing challenges in distribution system modeling and to develop rational approximations for power system models that intend to be optimal in terms of accuracy and performance.