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

NSF CAREER项目旨在促进复杂DC微电网的广泛采用。 虽然直流配电网在效率方面比传统的交流系统有很大的优势，但由于缺乏研究来解决独特的保护和稳定性挑战，直流配电网的采用是渐进的。该项目将带来变革，使直流微电网在各种规模和行业中得到更广泛的应用，从节能建筑和电动汽车充电站到航空航天应用。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，以及改进直流微电网的电能质量和弹性的算法开发。该项目的更广泛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。预期的结果有潜在的应用超出其直接的范围，扩展到领域，如交流电源系统，机器人和车辆控制。该提案旨在解决当前的挑战，阻碍了更广泛地采用直流电源系统，包括高阻抗故障检测，网络攻击的脆弱性，以及与非线性恒功率负载相关的电源管理问题。鲁棒的Koopman和基于行为数据的控制策略将被开发用于具有恒定功率负载的直流微电网的初级和次级控制，以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，以提高直流微电网的弹性，彻底改变传统的基于模型的保护策略。此外，将结合Koopman理论和机器学习技术设计在线数据驱动的稳定性方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.