Intelligent Robust Control Strategies and Estimation Techniques for Smart Power Girds

智能电网的智能鲁棒控制策略和估计技术

• 批准号: RGPIN-2019-05118
• 负责人: Karimipour, Hadis
• 金额: $ 2.04万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715375
• 关键词: Intelligent; Robust; Control; Strategies; Estimation

The size and complexity of the smart grid poses significant computational challenges on system control through dynamic state estimation (DSE). In addition, smart grid comes with many cyber security concerns. As far back as 2002, 70% of energy companies experienced some kind of cyber-attack. Cyber-attacks are intelligently designed to bypass existing fault identification approaches. In case of a successful attack, a domino effect may follow which will result in irreparable damage in different parts of the system. A long-term research goal of this program is to develop new robust and intelligent controllers that adapt to different operating modes and faults, modeling uncertainties, and attacks. Such a system can successfully predict the occurrence of cyber-attack and take appropriate control actions in advance. In short-term, this research program aims to address a number of important questions: How to take advantages of high performance computing to avoid tradeoff between accuracy and speed? How to leverage online information to identify/predict the cyber-attack in highly complex systems? How to achieve optimal control solutions without sacrificing stability? The novelty of the proposed work lies in the unique approaches in combining massive parallelization, robust control theory and intelligent estimation strategies. My research group seek to advance the use of machine learning, probability-based estimation techniques, and massively parallel programming on control, security, and estimation problems in complex large-scale systems. New massively parallel estimation strategies utilizing Byesian and composite likelihood estimators will be derived to overcome the limitations of traditional DSE such as numerical instability and slow response rate. Cyber-physical models that enable identification and prediction of a coordinated cyber-attack by pattern and time series analysis (e.g. Granger causality) will be developed. An adaptive algorithm based on feedback control and state-of-the-art machine learning techniques that leverages multi-view fuzzy consensus clustering will be developed to assess different weighted views to a variety of attacks, and estimate source of the attack. The expected outcome of this work is to develop new techniques and strategies that yield improved robustness to uncertainties and cyber intrusions, increased accuracy of the estimation, and reduced cycle time in faults clearance. This offers a number of benefits to society (safety) as well as the economy (reduced maintenance costs and fewer catastrophic events). Aside from their application in smart grid, these methods can be applied to a number of different engineering systems, including automotive, defense, and aerospace. In addition to technical advancements, my research group trains at least 7 HQP who help meet Canada's demand for engineers and will contribute to its economic success.

智能电网的规模和复杂性给通过动态状态估计(DSE)进行系统控制带来了巨大的计算挑战。此外，智能电网还伴随着许多网络安全问题。早在2002年，70%的能源公司就经历了某种网络攻击。网络攻击被智能地设计为绕过现有的故障识别方法。在攻击成功的情况下，多米诺骨牌效应可能随之而来，这将导致系统的不同部分造成无法弥补的损害。 该计划的一个长期研究目标是开发新的健壮和智能控制器，以适应不同的运行模式和故障、建模不确定性和攻击。这样的系统可以成功地预测网络攻击的发生，并提前采取适当的控制措施。短期内，该研究计划旨在解决一些重要问题：如何利用高性能计算的优势，避免在精度和速度之间进行权衡？如何利用在线信息在高度复杂的系统中识别/预测网络攻击？如何在不牺牲稳定性的情况下获得最优控制方案？ 提出的工作的新颖性在于将大规模并行化、鲁棒控制理论和智能估计策略相结合的独特方法。我的研究小组寻求推动机器学习、基于概率的估计技术和大规模并行编程在复杂大型系统中的控制、安全和估计问题上的使用。利用贝叶斯估计和复合似然估计的新的大规模并行估计策略将克服传统DSE的局限性，如数值不稳定和响应速度慢。将开发能够通过模式和时间序列分析(例如格兰杰因果关系)识别和预测协同网络攻击的网络物理模型。将开发一种基于反馈控制和最新机器学习技术的自适应算法，利用多视图模糊共识聚类来评估各种攻击的不同加权视图，并估计攻击源。 这项工作的预期结果是开发新的技术和战略，以提高对不确定性和网络入侵的稳健性，提高估计的准确性，并缩短故障排除的周期时间。这为社会(安全)和经济(降低维护成本和减少灾难性事件)提供了许多好处。除了在智能电网中的应用外，这些方法还可以应用于许多不同的工程系统，包括汽车、国防和航空航天。除了技术进步，我的研究小组还培训了至少7名HQP，他们帮助满足加拿大对工程师的需求，并将为加拿大的经济成功做出贡献。