Deep Koopman model predictive control for enhancing transient stability in power grids

In this article, we propose a deep Koopman model predictive control (MPC) strategy to improve the transient stability of power grids in a fully data-driven manner. Due to the high-dimensionality and the nonlinearity of the transient process, we use the Koopman operator to map the original nonlinear dynamics into an infinite dimensional linear system. To facilitate the control design, we first utilize the deep neural network method to efficiently train observable functions to approximate the Koopman operator so that the obtained dynamics in the high dimensional space is a linear system. We then propose an MPC strategy for the obtained high dimensional linear system. The proposed control strategy utilizes energy storage units, which inject or absorb real power at the synchronous generator buses to enhance the transient stability. Simulation studies implemented on the IEEE 9-bus 3-machine test system and the IEEE 39-bus 10-machine test system illustrate the performance of the proposed deep Koopman MPC strategy. The results demonstrate that the proposed control strategy effectively enhances the transient stability of the system even in the presence of severe faults.

Automated summary

This study introduces a deep Koopman model predictive control strategy to enhance the transient stability of power grids in a data-driven manner. By utilizing observable functions trained using deep neural networks to approximate the Koopman operator, the high-dimensional dynamics is transformed into a linear system. Implementing energy storage units to inject or absorb real power at synchronous generator buses effectively improves transient stability even in the presence of severe faults.