Development of Statistical Fault Detection Algorithms for Modern Power Grid Networks

国家电网在过去十年中经历了转型变革，面临着新的挑战。电网不再由煤炭、水电和核电厂等电力供应商组成，这些供应商以大致恒定的速度供电。可再生能源由于对天气因素的依赖，以更不可预测的方式提供电力。许多零售和服务设施不再是纯粹的能源消费者，而是通过安装大型太阳能发电场成为能源生产者。在这种新的环境下，重要的是要开发新的工具，及时检测故障。由于可预测的电力输送和消耗以及可能的攻击，故障检测必须基于对电网运行的随机结构的理解，其特征可以通过新一代设备来测量。这些设备产生大量数据集，尚未被广泛利用。PI将解决与电网故障检测和识别相关的许多具体问题。PI将使用从现代测量设备获得的数据，并开发最先进的统计方法。这项研究的进展将有助于美国的能源安全。该项目将为研究生和本科生提供培训。PI将开发先进的统计理论和大规模计算工具，为旨在检测和识别电网故障的工程实施奠定基础。这将在变化点检测方法的框架内进行。具体而言，PI将1）开发用于筛选影响特定保护设备的传感器输入的算法; 2）创建用于故障监测的算法，具有受控的显著性水平和预期检测时间; 3）为智能电网网络中的后验变点测试开发理论和实践基础; 4）研究了受故障影响的子电网的识别和故障分类算法; 5）通过引入高分辨率传感器读数的特性来增强PSCAD/EMTDC仿真工具。这是一个合作项目，结合了两名统计学家的专业知识，分别在随机网络和大规模计算（Wang）和时间序列分析（Kokoszka）领域工作，以及一名网络工程师，专门从事智能电网和信号处理（Yang）。该奖项反映了NSF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。

The national power grid has been undergoing transformational changes and facing new challenges over the last decade. The grid no longer consists of power suppliers, like coal, hydroelectric and nuclear plants, which supply power at a roughly constant rate. Renewable energy sources deliver power in a much less predictable manner due to their dependence on weather factors. Many retail and service facilities are no longer pure energy consumers but have become energy producers by installing large solar farms. In this new environment, it is important to develop new tools for timely detection of faults. Due to the less predictable power delivery and consumption, and possible attacks, fault detection must be based on the understanding of the random structure of the grid operation whose characteristics can be measured by a new generation of devices. These devices generate massive data sets, which have not yet been widely utilized. The PIs will address many specific questions related to the detection and identification of power grid faults. The PIs will use data obtained from modern measurements devices and develop state of the art statistical methodology. Advances made in this research will contribute to the energy security of the United States. The project will provide training to graduate and undergraduate students. The PIs will develop advanced statistical theory and large-scale computational tools that will form a foundation for engineering implementations aimed at detecting and identifying power grid faults. This will be done within a framework of change point detection methodology. Specifically, the PIs will 1) develop algorithms for screening sensor inputs that impact specific protection devices; 2) create algorithms for fault monitoring, with controlled significance level and expected time to detection; 3) develop theoretical and practical foundations for a posteriori change point testing in smart grid networks; 4) work out algorithms for the identification of subgrids affected by faults and the classification of faults; and 5) enhance PSCAD/EMTDC simulation tools by incorporating characteristics of high resolution sensor readings. It will be a collaborative effort combining the expertise of two statisticians working, respectively, in stochastic networks and large-scale computing (Wang) and time series analysis (Kokoszka), and a network engineer specializing in smart grids and signal processing (Yang).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.