Coordination of Damping Controllers: A Novel Data-Informed Approach for Adaptability

TL;DR

The paper addresses damping controller coordination in power systems with high renewable penetration by minimizing electromechanical oscillations through optimal on/off switching of damping controllers. It replaces model-based TA sensitivities with a data-informed approach that uses a deep neural network to approximate the total action $\hat{S}_{\infty}=f(\mathbf{r})$ from online measurements and controller statuses, enabling real-time switching decisions. Key contributions include a three-part coordination structure, a training pipeline based on large-scale wNAPS simulations, and demonstrated superiority over model-based methods across disturbances and operating conditions with robustness to time delays. The results show that the data-informed coordination (DIC) can achieve substantial TA reductions (up to around $73\%$ in tested cases) and operate under optimistic and pessimistic delay scenarios, offering a non-intrusive, energy-efficient damping framework for inverter-based resources. This approach has notable practical impact by enabling faster adaptive damping, reducing oscillations, and avoiding unnecessary energy curtailment while leveraging IBRs for wide-area damping.

Abstract

This paper explores the novel concept of damping controller coordination, which aims to minimize the Total Action metric by identifying an optimal switching combination (on/off) of these controllers. The metric is rooted in power system physics, capturing oscillation energy associated with all synchronous generators in the grid. While coordination has shown promising results, it has relied on computing linear sensitivities based on the grid model. This paper proposes a data-informed framework to accurately estimate total action and subsequently determine an optimal switching combination. The estimation is provided by a multivariate function approximator that captures the nonlinear relationship between system-wide area measurements, the status of damping controllers, and the conditions of the disturbance. By enabling real-time coordination, electromechanical oscillations are reduced, enhancing power system stability. The concept is tested in the Western North America Power System (wNAPS) and compared with the model-based approach for coordination. The proposed coordination outperforms the model-based approach, demonstrating effective adaptability and performance in handling multi-mode events. Additionally, the results show significant reductions in low-frequency electromechanical oscillations even under various operating conditions, fault locations, and time delay considerations.

Figures (14)

• Figure 1: DCs coordination framework
• Figure 2: Active power control loop of $\ell$-th IBR.
• Figure 3: Input feature correction.
• Figure 4: 179-bus, 30-machine, 7-DC test system.
• Figure 5: DNN as a TA function approximator.