CAREER: Robust Data Based Control and Estimation for Resilient DC Microgrids

职业：基于稳健数据的弹性直流微电网控制和估计

• 批准号: 2339434
• 负责人: Luis Herrera
• 金额: $ 50万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2029-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339434&HistoricalAwards=false
• 关键词: CAREER; Robust; Data; Based; Control

This NSF CAREER project aims to promote the widespread adoption of complex DC microgrids. While DC distribution networks hold substantial advantages over traditional ac systems in terms of efficiency, their adoption has been gradual due to the lack of research to address the unique protection and stability challenges. This project will bring transformative change by enabling broader implementation of dc microgrids across various scales and industries, ranging from energy efficient buildings and electric vehicle charging stations to aerospace applications. This will be achieved by the development of innovative data-driven methodologies for control, fault and cyber-attack detection, and stability analysis. The intellectual merits of the project include advancing data driven control theory and fault detection and identification, and the development of algorithms to improve the power quality and resiliency of dc microgrids. The broader impacts of the project include research and education tasks for training the next generation of power engineers through new course developments, internship opportunities with industry partners, and efforts to increase the participation of underrepresented students in STEM areas. The expected results have potential applications beyond their immediate scope, extending to areas such as ac power systems, robotics, and vehicle control.This proposal aims to address the current challenges which hinder the broader adoption of DC power systems, including high impedance fault detection, vulnerability to cyber-attacks, and power management issues associated with nonlinear constant power loads. Robust Koopman and behavioral data-based control strategies will be developed for the primary and secondary control of dc microgrids with constant power loads, ensuring stability and power quality. Data driven fault and cyber-attack detection methods will be developed to enhance the resiliency of DC microgrids, revolutionizing traditional model-based protection strategies. In addition, online data driven stability methods will be devised by combining Koopman theory and machine learning techniques. They will provide DC microgrids operators with an accurate knowledge of the stability margins of the network, improving their reliability and robustness.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 NSF 职业项目旨在促进复杂直流微电网的广泛采用。 尽管直流配电网络在效率方面比传统交流系统具有显着优势，但由于缺乏解决独特保护和稳定性挑战的研究，直流配电网络的采用一直是渐进的。该项目将通过在各种规模和行业（从节能建筑和电动汽车充电站到航空航天应用）更广泛地实施直流微电网，带来变革。这将通过开发用于控制、故障和网络攻击检测以及稳定性分析的创新数据驱动方法来实现。 该项目的智力优势包括先进的数据驱动控制理论和故障检测和识别，以及开发算法以提高直流微电网的电能质量和弹性。该项目更广泛的影响包括通过新课程开发培训下一代电力工程师的研究和教育任务、与行业合作伙伴的实习机会以及努力增加 STEM 领域代表性不足的学生的参与。预期结果具有超出其直接范围的潜在应用，扩展到交流电源系统、机器人和车辆控制等领域。该提案旨在解决当前阻碍直流电源系统更广泛采用的挑战，包括高阻抗故障检测、容易受到网络攻击，以及与非线性恒定功率负载相关的电源管理问题。将开发鲁棒的库普曼和基于行为数据的控制策略，用于恒定功率负载的直流微电网的初级和次级控制，确保稳定性和电能质量。将开发数据驱动的故障和网络攻击检测方法，以增强直流微电网的弹性，彻底改变传统的基于模型的保护策略。此外，结合库普曼理论和机器学习技术，将设计在线数据驱动的稳定性方法。他们将为直流微电网运营商提供有关网络稳定性裕度的准确知识，从而提高其可靠性和稳健性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。