Xu, X, Li, K orcid.org/0000-0001-6657-0522, Qi, F et al. (1 more author) (2017) Data-driven dynamic prediction of interrelated heat and electric outputs of microturbines. In: Proceedings of 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 09-12 Oct 2016, Ljubljana, Slovenia. IEEE ISBN 978-1-5090-3358-4
Abstract
This paper proposes a simplified microturbine (MT) model which allows for dynamic heat and power output prediction. Considering the time-scale difference of various dynamic processes occuring within MTs, the electromechanical subsystem is treated as a fast quasi-linear system while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A subspace model identification method is utilized to capture dominant dynamics and predict outputs of the electromechanical subsystem. For the thermo-mechanical process, a fast recursive algorithm assisted radial basis function model is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics and produce a good model to predict the interrelated heat and electricity outputs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Microturbine; dynamic behavior; modeling; system identification |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 13:00 |
Last Modified: | 05 Nov 2019 13:00 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/isgteurope.2016.7856174 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153042 |