CAP: AI-Assisted Supervisory Control of Wind Farm Connection to the Grid for Stability Monitoring

California State University San Marcos Corporation, San Marcos CA

Investigators

Abstract

This project is an ExpandAI Capacity building pilot (CAP), which focuses on establishing and growing AI related activities at the California State University at San Marcos. This will be accomplished by growing a pool of AI practitioners that will drive a sustainable expansion of AI-powered research and education at the institution. As we embrace renewable energy sources, we face the challenge of maintaining a stable energy grid. Artificial Intelligence (AI) can pave the path for optimized use of renewables by enhancing prediction capabilities to increase power systems awareness before failures occur. On the educational side, activities will result in self-contained, hands-on projects and modular training tutorials that will enrich Computer Science and Electrical Engineering undergraduate and graduate curricula. The project aims to develop novel AI and machine learning models for supervisory control of wind farm connection to the grid for stability monitoring. State-of-the-art techniques convert power measurement to waveform images for analysis which has a considerable overhead for real-time analysis. This project will instead focus on developing innovative AI/ML models that can directly analyze raw data for accurate fault prediction and detection to achieve improved response during faults or other emergency conditions. This is essential for maintaining grid stability. Additionally, the project will establish new cyberinfrastructure to provide multi-disciplinary research opportunities in AI and power systems. While oscillation prediction and mitigation in wind farms is the target problem, the same concept can be applied more generally to grid anomaly detection and cybersecurity challenges. In addition, the AI/ML models developed could potentially pave the way for other asset monitoring applications, such as electrified transportation systems and healthcare monitoring. Given the plethora of applications where the approach can be applied, the project is likely to have significant broader impacts. The ExpandAI Program supports AI-powered education and workforce development, infrastructure and research at Minority Serving Institutions to strengthen and diversify U.S. research and education pathways and provide historically marginalized communities with new opportunities in STEM careers. 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.