AMPS: Collaborative Research: Efficient Algorithms for Ultra-Fast Detection of Power System Contingencies in the Transient Regime

AMPS：协作研究：瞬态状态下电力系统突发事件超快速检测的高效算法

• 批准号: 1736437
• 负责人: Ilias Bilionis
• 金额: $ 2万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing 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=1736437&HistoricalAwards=false
• 关键词: AMPS; Collaborative; Research; Efficient; Algorithms

The stability of the electric power system is of the utmost importance for its reliable operation. This work focuses on the impact of large disturbances, such as the loss of a transmission line due to a short-circuit, or the sudden disconnection of a thermal generating unit due to an unpredictable malfunction. When such faults occur, the system undergoes a transient event of highly nonlinear nature. The ongoing large-scale integration of renewable energy resources in the grid challenges conventional transient stability analysis, and reduces the 'inertia' of the system. At the same time, there is a proliferation of phasor measurement units (PMUs) on the grid. These sensors provide accurate, high-frequency information about voltage, current, and frequency at various locations around the system, and are instrumental in increasing our system-level situation awareness. Nevertheless, the transformation of the incoming data to actionable information is still an open research problem. In the context of transient regimes, it is important to establish as soon as possible, i.e., within a few electrical cycles, what has gone wrong, in order to predict the evolution of the system dynamics over the period of time following the fault. Such 'ultra-fast' detection is critical, since it may allow appropriate actions to be taken by a wide-area monitoring and control system that can actuate various assets around the grid, such as flexible ac transmission system (FACTS) devices or even the converters of renewable resources.The main objective of this work is to develop computationally scalable, yet statistically efficient, real-time algorithms in order to detect and identify contingencies in the early stages of the transient regime of the power system based on PMU outputs. The validity and efficacy of these algorithms will be based on a stochastic model for the power system during the transient stability regime following a contingency. This is a very challenging problem that cannot be addressed with existing uncertainty propagation methodologies, because of the high-dimensionality and the computational complexity of the power grid dynamical system. A key component is the reformulation of the uncertainty propagation problem which overcomes the curse of dimensionality, enables the fast re-estimation of the statistics as the power system operating point changes, and allows for the adaptive selection of power system simulations. This work will rely on combination of diverse techniques, such as uncertainty quantification, power grid modeling, and sequential detection.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.

电力系统的稳定性对电力系统的可靠运行至关重要。这项工作的重点是大干扰的影响，如传输线的损失，由于短路，或突然断开的火力发电机组由于不可预测的故障。当这种故障发生时，系统经历高度非线性性质的瞬态事件。 可再生能源在电网中的大规模整合对传统的暂态稳定分析提出了挑战，并降低了系统的“惯性”。与此同时，电网上的相量测量单元（PMU）激增。这些传感器提供有关系统周围各个位置的电压、电流和频率的准确高频信息，有助于提高我们的系统级态势感知能力。然而，输入数据的可操作的信息的转换仍然是一个开放的研究问题。在过渡时期，必须尽快建立，在几个电周期内，什么出了问题，以便预测故障后一段时间内系统动态的演变。这种“超快”检测是至关重要的，因为它可以允许广域监测和控制系统采取适当的行动，该广域监测和控制系统可以驱动电网周围的各种资产，例如灵活交流输电系统（FACTS）设备或甚至可再生资源的转换器。实时算法，以检测和识别基于PMU输出的电力系统暂态状态的早期阶段的意外事故。这些算法的有效性和功效将基于一个随机模型的电力系统在暂态稳定政权以下的意外。 这是一个非常具有挑战性的问题，不能解决与现有的不确定性传播方法，因为高的维数和计算复杂性的电网动态系统。一个关键的组成部分是不确定性传播问题，克服了灾难的维数，使快速重新估计的统计数据的电力系统工作点的变化，并允许自适应选择电力系统模拟的重新制定。 这项工作将依赖于多种技术的组合，如不确定性量化，电网建模和顺序检测。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。