Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework

通过鲁棒自适应可能性算法实现网络物理系统安全：跨层框架

• 批准号: 1809739
• 负责人: Arturo Bretas
• 金额: $ 36万
• 依托单位: University of Florida
• 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: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809739&HistoricalAwards=false
• 关键词: Cyber; Physical; Systems; Security; through

The goal of this project is to develop a cross-layer cyber-physical security framework for the smart grid. The proposed research will improve the quality of real-time monitoring of the smart grid through anomaly analysis. This will lead to more reliable data for control, situation awareness to first responders and other improved applications to smart grids. The proposed research will improve the resilience of smart grids to cyber-attacks in meters, parameters, topology and communication infrastructure and large physical disturbances by developing new techniques for distributed control of large complex systems that guarantees secure and reliable performance. The project will foster education through enhancement to curriculum by building bridges among communications, machine learning, power and control systems. The PIs plan to teach short courses on smart grid security at conferences. In addition, they plan to engage under-represented minority students in their project. The project aims at developing a distributed nonlinear controller for transient stability enhancement. The new control layer will actuate on distributed energy storage systems, be robust to uncertainties in modelling and capable of compensating input time-delay while independent of operating conditions. Furthermore, the robust controller will not require exact knowledge of the system dynamics. Second, bad data analytics based on the innovation approach and cross-layered information provided by distributed software-defined network will be developed. The bad data analytics will consider the inherent interdependencies of the physical processes while providing a countermeasure. Third, an adaptive distributed robust machine learning approach will be developed. The overwhelming majority of supervised machine learning methods require large amounts of carefully labeled training data that is representative of the data distribution to be seen under test. However, in security applications, novel threats and malicious attacks are continuously being developed and attempted. Thus, approaches that rely on prior training data are unlikely to be robust in the case of behaviors never seen before, as would be the case in a rapidly changing threat environment. The novel distributed machine intelligence method that will be developed will be focused on being rapidly adaptive to identifying and distinguishing novel threats given even only one example of an anomalous novel threat.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.

该项目的目标是为智能电网开发一个跨层网络物理安全框架。本研究将通过异常分析提高智能电网的实时监测质量。这将为控制带来更可靠的数据，为急救人员提供态势感知，并改善智能电网的其他应用。拟议的研究将通过开发大型复杂系统分布式控制的新技术，提高智能电网对仪表、参数、拓扑和通信基础设施以及大型物理干扰的网络攻击的恢复能力，确保安全可靠的性能。该项目将通过在通信、机器学习、电力和控制系统之间建立桥梁，通过加强课程来促进教育。pi计划在会议上讲授有关智能电网安全的短期课程。此外，他们还计划让代表性不足的少数族裔学生参与他们的项目。该项目旨在开发一种分布式非线性控制器来增强暂态稳定性。新的控制层将驱动分布式储能系统，对建模中的不确定性具有鲁棒性，并且能够在独立于运行条件的情况下补偿输入时滞。此外，鲁棒控制器将不需要精确的系统动力学知识。其次，发展基于分布式软件定义网络提供的创新方法和跨层信息的坏数据分析。坏数据分析将在提供对策的同时考虑物理过程的内在相互依赖性。第三，将开发自适应分布式鲁棒机器学习方法。绝大多数监督式机器学习方法需要大量精心标记的训练数据，这些数据代表了待测数据的分布。然而，在安全应用中，新的威胁和恶意攻击不断被开发和尝试。因此，依赖于先前训练数据的方法在以前从未见过的行为的情况下不太可能是健壮的，就像在快速变化的威胁环境中一样。将开发的新型分布式机器智能方法将专注于快速适应识别和区分新威胁，即使只有一个异常新威胁的例子。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model

• DOI: 10.1016/j.ijepes.2021.106960
• 发表时间: 2021
• 期刊: International Journal of Electrical Power & Energy Systems
• 影响因子: 5.2
• 作者: J. Marín-Quintero;C. Orozco-Henao;J. Velez;A. Bretas

Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

• DOI: 10.1016/j.epsr.2021.107347
• 发表时间: 2021-05-15
• 期刊: ELECTRIC POWER SYSTEMS RESEARCH
• 影响因子: 3.9
• 作者: Monteiro, R. V. A.;de Santana, J. C. R.;Poma, C. E. P.
• 通讯作者: Poma, C. E. P.

WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

基于 WAM 的高阻抗故障检测特征值空间模型

• DOI: 10.3390/app112412148
• 发表时间: 2021
• 期刊: Applied Sciences
• 作者: Paramo, Gian;Bretas, Arturo S.
• 通讯作者: Bretas, Arturo S.

Ensemble CorrDet with adaptive statistics for bad data detection

• DOI: 10.1049/iet-stg.2020.0029
• 发表时间: 2020-06
• 作者: Keerthiraj Nagaraj;Sheng Zou;Cody Ruben;S. Dhulipala;Allen Starke;A. Bretas;A. Zare;J. Mcnair

Renewable and energy storage resources for enhancing transient stability margins: A PDE-based nonlinear control strategy

• DOI: 10.1016/j.ijepes.2019.105510
• 发表时间: 2020-03
• 期刊: International Journal of Electrical Power & Energy Systems
• 影响因子: 5.2
• 作者: Serhat Obuz;Muharrem Ayar;R. D. Trevizan;Cody Ruben;A. Bretas