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Stable deep Koopman model predictive control for solar parabolic-trough collector field.
- Source :
-
Renewable Energy: An International Journal . Oct2022, Vol. 198, p492-504. 13p. - Publication Year :
- 2022
-
Abstract
- Concentrated Solar Power plants (CSP) have the energy storage capability to generate electricity when sunlight is scarce. However, due to the highly non-linear dynamics of these systems, a simple linear controller will not be able to overcome the variable dynamics and multiple disturbance sources affecting it. In this paper, a deep Model Predictive Control (MPC) based on the Koopman operator is proposed and applied to control the Heat Transfer Fluid (HTF) temperature of a distributed-parameter model of the ACUREX solar collector field located at Almería, Spain. The Koopman operator is an infinite-dimensional linear operator that fully captures a system's non-linear dynamics through the linear evolution of functions of the state-space. However, one of the major problems is identifying a Koopman linear model for a non-linear system. Koopman eigenfunctions are involved in converting a non-linear model to a Koopman-based linear model. In this paper, a deep Long Short-Term Memory (LSTM) autoencoder is designed to calculate Koopman eigenfunctions of the solar collector field. The Koopman linear model is then used to design a linear MPC with terminal components to ensure closed-loop stability guarantees. Simulation results are utilized to show the satisfactory tracking performance of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09601481
- Volume :
- 198
- Database :
- Academic Search Index
- Journal :
- Renewable Energy: An International Journal
- Publication Type :
- Academic Journal
- Accession number :
- 159189170
- Full Text :
- https://doi.org/10.1016/j.renene.2022.08.012