CPS: Breakthrough: Collaborative Research: WARP: Wide Area assisted Resilient Protection

CPS：突破：协作研究：WARP：广域辅助弹性保护

• 批准号: 1855854
• 负责人: Sukumar Brahma
• 金额: $ 8.17万
• 依托单位: Clemson University
• 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: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855854&HistoricalAwards=false
• 关键词: CPS; Breakthrough; Collaborative; Research; WARP

The electric power grid experiences disturbances all the time that are routinely controlled, managed, or eliminated by system protection measures- designed by careful engineering studies and fine-tuned by condensing years of operational experience. Despite this, the grid sometimes experiences disruptive events that can quickly, and somewhat unstoppably catapult the system towards a blackout. Arresting such blackouts has remained elusive - mainly because relays (protection devices) operate on local data, and are prone to hidden faults that are impossible to detect until they manifest, resulting in misoperations that have sometime been precipitators or contributors to blackouts. Inspired by the Presidential policy directive on resilience -- meaning the ability to anticipate, prepare, withstand, and recover from disruptive events, this project proposes "WARP: Wide Area assisted Resilient Protection", a paradigm that adds a layer of finer (supervisory) intelligence to supplement conventional protection wisdom - which we call resilient protection. Exploiting high fidelity measurements and computation to calculate and analyze energy function components of power systems to identify disturbances, WARP would allow relays to be supervised - correct operations would be corroborated, and misoperations will be remedied by judiciously reversing the relay operation in a rational time-frame. The project also envisions predicting instability using advanced estimation techniques, thus being proactive. This will provide power grid the ability to auto-correct and bounce back from misoperations, curtailing the size, scale and progression of blackouts and improving the robustness and resilience of the electric grid -- our nation's most critical infrastructure.In WARP, disruptive events are deciphered by using synchrophasor data, energy functions, and dynamic state information via particle filtering. The information is fused to provide a global data set and intelligence signal that supervises relays, and also to predict system stability. Resilience is achieved when the supervisory signal rectifies the misoperation of relays, or endorses their action when valid. This endows relays with post-event-auto-correct abilities 
- a feature that never been explored/understood in the protection-stability nexus. Architectures to study the effect of latency and bad data are proposed. WARP introduces new notions: global detectability and distinguishability for power system events, stability prediction based on
the sensitivity of the energy function components and uses a novel factorization method: (CUR) preserving data interpretability to reduce data dimensionality. All the proposed
tools will be wrapped into a simulation framework to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved. The effectiveness of the proposed scheme during extreme events will be measured by reenacting two well-documented blackout sequences. In addition, simulations on benchmarked systems will be performed to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved.

电网一直在经历干扰，这些干扰通常通过系统保护措施进行控制、管理或消除-通过仔细的工程研究进行设计，并通过凝聚多年的运行经验进行微调。尽管如此，电网有时会经历一些破坏性事件，这些事件可能会迅速地、某种程度上不可阻挡地将系统推向停电。阻止这种停电仍然很难--主要是因为继电器(保护装置)根据本地数据运行，而且容易出现隐藏的故障，这些故障在显露出来之前是不可能检测到的，导致误操作，有时会导致停电。受总统关于复原力的政策指令的启发--意味着预测、准备、抵御和从破坏性事件中恢复的能力，该项目提出了“WARP：广域辅助复原力保护”，这是一种增加了一层更精细的(监督)智能以补充传统保护智慧的范例--我们称之为复原力保护。利用高保真测量和计算来计算和分析电力系统的能量函数分量来识别干扰，WARP将允许对继电保护进行监督-正确的操作将得到证实，错误操作将通过在合理的时间框架内明智地颠倒继电保护操作来纠正。该项目还设想使用先进的估计技术来预测不稳定性，从而具有主动性。这将为电网提供自动纠正和从误操作中恢复的能力，减少停电的规模、规模和进展，并提高电网的健壮性和弹性--电网是我国最关键的基础设施。在WARP中，通过粒子过滤使用同步相量数据、能量函数和动态信息来破译破坏性事件。这些信息被融合，以提供监督继电保护的全球数据集和情报信号，并预测系统稳定性。当监控信号纠正继电器的误操作，或在有效时认可其操作时，就实现了弹性。这赋予继电器事后自动校正能力--这一特性在保护-稳定性关系中从未被探索/理解过。提出了研究延迟和不良数据影响的体系结构。WARP引入了新的概念：电力系统事件的全局可检测性和可区分性，基于能量函数分量灵敏度的稳定性预测，并使用了一种新的因式分解方法：(CUR)保持数据的可解释性以降低数据维度。所有建议的工具都将被包装到一个模拟框架中，以评估可伸缩性和准确性-运行时权衡，并量化实现的弹性程度。在极端事件期间，建议方案的有效性将通过重演两个记录良好的停电序列来衡量。此外，将在基准系统上进行模拟，以评估可伸缩性和准确性-运行时权衡，并量化实现的弹性程度。