Collaborative Research: Hierarchical Intelligent and Adaptive Techniques to Enable Resilient DC Power Systems

协作研究：分层智能和自适应技术实现弹性直流电源系统

• 批准号: 1809957
• 负责人: Luis Herrera
• 金额: $ 24.23万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2018-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809957&HistoricalAwards=false
• 关键词: Collaborative; Research; Hierarchical; Intelligent; Adaptive

As the adoption of energy sources and loads with inherent dc voltage continues to increase, an electric system based on dc power can offer tremendous advantages over ac, with higher efficiency, less power conversion stages, smaller footprint, and higher reliability. For these reasons, dc power systems and microgrids are now used in electric vehicles, ships, aircraft, and in rural areas. However, electrical faults in dc power networks can lead to extremely dangerous situations which are more difficult to interrupt than their ac counterparts, particularly due to the lack of zero voltage crossings. Moreover, high impedance faults in the form of electrical arcs, such as those caused by loose connections or chafed wires, are very difficult to detect because of the low fault current. The high penetration of electronics loads with advanced controllers make the fault detection and localization even more challenging. To increase the safety and resiliency of dc based systems, the proposed project will address these technical challenges in detecting high impedance faults in dc power systems by developing intelligent and adaptive fault detection, localization, and isolation techniques that are built upon a comprehensive and systematic fault modeling and characterization study. These techniques can significantly improve the performance of existing and future dc systems to enable their wide adoption at larger scales, which can provide efficient and reliable interfaces to many renewable resources, energy storage units, and modern electronic loads and align with the nation's initiatives in using clean and green energy. This project is intrinsically multidisciplinary by bringing advanced and exciting modern control theories, artificial intelligence, and signal processing techniques into electric power engineering. The tasks in this project involve a wide range of expertise and experience from software simulation and control algorithms to hardware testing; from circuit level study to system level implementation, which provides a unique and high quality training opportunity for future engineers. The proposed educational activities will also broaden participation of women and other under-represented students.The goal of the proposed research is to develop fault detection, localization, and isolation techniques for modern dc power systems through a hierarchical approach with intelligent and adaptive functionalities. It addresses the most challenging issues in the protection of dc power systems with a systematic and transformative effort. The fault modeling and characterization study of the proposed project will generate fundamental and critical knowledge of high impedance faults in modern application settings through comprehensive experimental and analytical approaches. The proposed high impedance fault detection and localization techniques will take into account the effect of advanced controllers through dynamic parameter estimation. The adaptive and integrated fault detection and localization schemes to be developed will significantly enhance the existing protection system design and online stability assessment methodologies by adopting modern nonlinear control theory and artificial intelligence tools. The proposed research is expected to produce significant results of both theoretical and practical values to the field of dc power systems. When successfully completed, the project has the potential to revolutionize the control and protection aspects of dc power systems, minimizing the adverse impact of high impedance faults and constant power loads. The proposed techniques can be applied to dc systems in different scales ranging from isolated dc distribution networks to interconnected dc microgrids, to improve the fault protection effectiveness and therefore their resiliency.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.

随着具有固有直流电压的能源和负载的采用不断增加，基于直流电源的电力系统可以比交流电源提供巨大的优势，具有更高的效率、更少的功率转换级、更小的占地面积和更高的可靠性。 由于这些原因，直流电力系统和微电网现在用于电动汽车、船舶、飞机和农村地区。 然而，直流电网中的电气故障可能会导致极其危险的情况，这些情况比交流电网更难以中断，特别是由于缺乏零电压交叉。 此外，由于故障电流低，电弧形式的高阻抗故障（例如由连接松动或电线磨损引起的故障）非常难以检测。 具有先进控制器的电子负载的高渗透性使得故障检测和定位更具挑战性。 为了提高直流系统的安全性和弹性，拟议项目将通过开发基于全面和系统故障的智能和自适应故障检测、定位和隔离技术来解决检测直流电力系统中高阻抗故障的技术挑战。建模和表征研究。这些技术可以显着提高现有和未来直流系统的性能，使其能够在更大范围内得到广泛采用，为许多可再生资源、储能单元和现代电子负载提供高效可靠的接口，并与国家在使用直流电方面的举措保持一致。清洁绿色能源。 该项目本质上是多学科的，将先进且令人兴奋的现代控制理论、人工智能和信号处理技术引入电力工程。 该项目的任务涉及从软件仿真和控制算法到硬件测试的广泛专业知识和经验；从电路级学习到系统级实现，为未来工程师提供了独特且高质量的培训机会。拟议的教育活动还将扩大女性和其他代表性不足的学生的参与。拟议研究的目标是通过具有智能和自适应功能的分层方法开发现代直流电力系统的故障检测、定位和隔离技术。 它通过系统性和变革性的努力解决了直流电力系统保护中最具挑战性的问题。 该项目的故障建模和表征研究将通过综合实验和分析方法产生现代应用环境中高阻抗故障的基础和关键知识。 所提出的高阻抗故障检测和定位技术将通过动态参数估计来考虑先进控制器的影响。 即将开发的自适应集成故障检测和定位方案将通过采用现代非线性控制理论和人工智能工具，显着增强现有的保护系统设计和在线稳定性评估方法。所提出的研究预计将为直流电力系统领域产生具有理论和实际价值的重要成果。 成功完成后，该项目有可能彻底改变直流电力系统的控制和保护方面，最大限度地减少高阻抗故障和恒定功率负载的不利影响。 所提出的技术可应用于从孤立的直流配电网到互连的直流微电网等不同规模的直流系统，以提高故障保护的有效性，从而提高其弹性。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。