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

CAP：人工智能辅助风电场并网监控以进行稳定性监测

• 批准号: 2334256
• 负责人: Ali Ahmadinia
• 金额: $ 39.93万
• 依托单位: California State University San Marcos Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334256&HistoricalAwards=false
• 关键词: CAP; AI; Assisted; Supervisory; Control

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.

该项目是 ExpandAI 能力建设试点 (CAP)，重点是在加州州立大学圣马科斯分校建立和发展人工智能相关活动。这将通过扩大人工智能从业者队伍来实现，这将推动该机构人工智能驱动的研究和教育的可持续扩展。当我们拥抱可再生能源时，我们面临着维持稳定的能源网络的挑战。人工智能 (AI) 可以增强预测能力，提高电力系统在故障发生前的意识，从而为可再生能源的优化使用铺平道路。在教育方面，活动将带来独立的实践项目和模块化培训教程，这将丰富计算机科学和电气工程本科生和研究生课程。该项目旨在开发新颖的人工智能和机器学习模型，用于监督控制风电场与电网的连接以进行稳定性监测。 最先进的技术将功率测量转换为波形图像进行分析，这对于实时分析来说具有相当大的开销。该项目将专注于开发创新的人工智能/机器学习模型，这些模型可以直接分析原始数据以进行准确的故障预测和检测，从而在故障或其他紧急情况下提高响应能力。这对于维持电网稳定性至关重要。此外，该项目还将建立新的网络基础设施，为人工智能和电力系统提供多学科研究机会。虽然风电场的振荡预测和缓解是目标问题，但相同的概念可以更广泛地应用于电网异常检测和网络安全挑战。此外，开发的人工智能/机器学习模型可能为其他资产监控应用铺平道路，例如电气化交通系统和医疗保健监控。鉴于可以应用该方法的应用程序过多，该项目可能会产生更广泛的影响。 ExpandAI 计划支持少数族裔服务机构的人工智能教育和劳动力发展、基础设施和研究，以加强和多样化美国的研究和教育途径，并为历史上边缘化的社区提供 STEM 职业的新机会。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。