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Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control
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DOI：10.1109/JSYST.2023.3253041.png)
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摘要
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This article presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power grid. The approach involves a novel data-driven basis-dictionary free lifting of the system dynamics into a higher dimensional linear space over which a model predictive control is exercised, making it both scalable and rapid for practical real-time implementation. A Koopman-inspired deep neural network (KDNN) encoder-decoder architecture for the linear embedding of the underlying networked dynamics under distributive control is presented, in which the end-to-end components of the KDNN, comprising of a triple of transforms is learned from the system trajectory data in one go: a neural network (NN)-based lifting to a higher dimension, a linear dynamics within that higher dimension, and an NN-based projection to the original space. This data-learned approach relieves the burden of the ad hoc selection of the nonlinear basis functions (e.g., radial or polynomial) used in conventional approaches for lifting to a higher dimensional linear space. We validate the efficacy and robustness of the approach via application to the standard IEEE 39-bus system.
Keyword:
Data-driven method
Koopman operator
model predictive control (MPC)
neural network (NN)
voltage control
期刊
I
IF:
2.4
论文数:
4.5K
被引数:
387
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