FW-HTF: Collaborative Research: Augmenting and Advancing Cognitive Performance of Control Room Operators for Power Grid Resiliency

人类技术前沿工作的未来（FW-HTF）是美国国家科学基金会宣布的未来投资十大新构想之一。FW-HTF跨部门计划旨在通过支持融合研究来应对不断变化的就业和工作环境的挑战和机遇。这个奖项部分实现了这一目标。电网运营商在极端事件（如波多黎各飓风玛丽亚、乌克兰网络攻击）中的有效决策取决于两个因素：通过培训和经验获得的运营商知识，以及适当的决策支持工具。极端恶劣事件下的电网运行决策直接影响到市民的生活。该项目将通过新的、以人为中心的决策支持工具和更好的、数据驱动的培训来增强人类操作员的认知能力，以管理电网，特别是在高度破坏性的条件下。新一代在线知识融合、事件检测、操作环境中的网络-物理-人分析工具的开发可以在极端事件中应用，并为医院、市政厅和重要基础设施等关键设施提供能源，以保障公民安全，避免国家经济损失。操作人员的更高绩效将改善工人的生活质量，并将增强国家的经济和社会福祉。该项目的培训目标将利用现有的教育工作和外展活动，我们将通过多个场所宣传多学科成果。拟议的项目将整合认知神经科学、人工智能、机器学习、数据科学、网络安全和电力工程的原理，以增强电网运营商的性能。从动态注意控制（DAC）框架来看，影响人类表现的两个关键参数是工作记忆（WM）容量，即在注意焦点中保持信息的能力，以及认知灵活性（CF），即在快速变化的系统场景中使用反馈来重新定向决策的能力。通过分析WM和CF以及极端事件下电网运营商的性能，实现项目目标；通过基于先进机器学习的决策支持工具和自适应人机系统增强认知性能；并开发理论驱动的训练模拟器，以提高人类操作员的认知性能，增强网格弹性。提出了一套新的算法，用于数据驱动的事件检测、异常标志处理、根本原因分析和决策支持，使用树增强朴素贝叶斯网络（TAN）结构，使用互信息（MI）度量的最小加权生成树（MWST），以及用于在线学习和决策的改进的无监督学习。此外，还提出了使用认知因素分析和人为错误分析的可视化工具。我们提出了一个由认知和生理分析驱动的培训过程，并受到我们在多个领域（电网，飞机和航天器飞行模拟器）操作员培训经验的启发。提出了一种基于可量化的电网弹性人与工程分析指标的系统的电网操作员决策方法。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。

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by the National Science Foundation. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. Effective decision making by power grid operators in extreme events (e.g., Hurricane Maria in Puerto Rico, the Ukraine cyber attack) depends on two factors: operator knowledge acquired through training and experience, and appropriate decision support tools. Decision making in electric grid operation during extreme adverse events directly impacts the life of citizens. This project will augment the cognitive performance of human operators with new, human-focused decision support tools and better, data-driven training for managing the grid especially under highly disruptive conditions. The development of new generation of tools for online knowledge fusion, event detection, cyber-physical-human analysis in operational environment can be applied during extreme events and provide energy to critical facilities like hospitals, city halls and essential infrastructure to keep citizens safe and avoid economic loss for the Nation. Higher performance of operators will improve worker quality of life and will enhance the economic and social well-being of the country. The project's training objectives will leverage existing educational efforts and outreach activities and we will publicize the multidisciplinary outcomes through multiple venues.The proposed project will integrate principles from cognitive neuroscience, artificial intelligence, machine learning, data science, cybersecurity, and power engineering to augment power grid operators for better performance. Two key parameters influencing human performance from the dynamic attentional control (DAC) framework are working memory (WM) capacity, the ability to maintain information in the focus of attention, and cognitive flexibility (CF), the ability to use feedback to redirect decision making given fast changing system scenarios. The project will achieve its goals through analyzing WM and CF and performance of power grid operators during extreme events; augmenting cognitive performance through advanced machine learning based decision support tools and adaptive human-machine system; and developing theory-driven training simulators for advancing cognitive performance of human operators for enhanced grid resilience. A new set of algorithms have been proposed for data-driven event detection, anomaly flag processing, root cause analysis and decision support using Tree Augmented naive Bayesian Net (TAN) structure, Minimum Weighted Spanning Tree (MWST) using the Mutual Information (MI) metric, and unsupervised learning improved for online learning and decision making. Additionally, visualization tools have been proposed using cognitive factor analysis and human error analysis. We propose a training process driven by cognitive and physiometric analysis and inspired by our experience in operators training in multiple domain: the power grid, aircraft and spacecraft flight simulators. A systematic approach for human operator decision making is proposed using quantifiable human and engineering analysis indices for power grid resiliency.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.