ERI: scalable machine learning frameworks for stability enhancement in inverter-dominated power systems

The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded $199,984 to the University of Michigan-Dearborn on June 1, 2026, under the Engineering Research Initiation program (CFDA 47.041) to develop scalable machine learning frameworks for stability enhancement in inverter-dominated power systems.

The project delivers physics-informed neural networks and federated learning methods to estimate real-time power system behavior and recommend corrective actions, addressing the challenge of monitoring and controlling modern grids increasingly reliant on distributed generation and battery storage. The hybrid framework combines physics-based modeling with artificial intelligence to enable scalable, privacy-preserving system impedance estimation and real-time decision-making for stability enhancement without requiring utilities and devices to share sensitive operational data. Deliverables include open-source tools for utilities and researchers, alongside workforce development through student training.

Performance occurs in Dearborn, Michigan with a period of performance from June 1, 2026, through May 31, 2028. The project aims to reduce large-scale power outage risk and improve national energy security by providing utilities with advanced monitoring and control capabilities that address the limitations of traditional analysis tools in the context of fast, complex dynamics introduced by inverter-based resources.

Abstract

This NSF ERI project aims to enhance the stability and resilience of modern power systems as they increasingly rely on inverter-based resources, such as distributed power generation and battery energy storage systems. While these technologies are essential for a sustainable energy future, they introduce fast and complex dynamics that make power grids more difficult to monitor and control. Traditional analysis tools are no longer sufficient due to limited visibility into how these devices operate and interact. The project will bring transformative changes by developing intelligent, data-driven methods that can estimate system behavior in real time and recommend actions to prevent instability. This will be achieved by combining machine learning with physical knowledge of power systems to create scalable and privacy-preserving solutions. The intellectual merit of the project includes the development of new learning frameworks that integrate physics-based modeling with advanced artificial intelligence for real-time system monitoring and control. The broader impacts of the project include improving national energy security by reducing the risk of large-scale power outages, enabling safer and more reliable grid operation, supporting workforce development through student training, and providing open-source tools for utilities and researchers. The project addresses two core technical challenges: accurate system modeling under limited data availability and real-time decision-making for stability enhancement. First, IT develops a hybrid framework that combines physics-informed neural networks, which are machine learning models guided by physical laws, with federated learning, a distributed training approach that allows multiple devices to collaboratively build models without sharing sensitive data. This enables scalable, real-time estimation of system impedance, a key indicator of stability, using only local measurements. Second, the project designs a deep reinforcement learning framework, specifically based on the soft actor-critic algorithm, to learn optimal control strategies that improve system stability. The framework uses stability-related metrics, including damping and participation factors, to guide safe and effective decision-making. These methods will be validated using high-fidelity hardware-in-the-loop platforms that emulate real-world grid conditions. The expected contributions include a unified framework for real-time monitoring and control, improved stability margins in inverter-dominated systems, and practical tools for deployment in next-generation power grids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.