Project Summary

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 lives 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 a new generation of online knowledge fusion, event detection, cyber-physical-human analysis in the operational environment can be applied to augment human operators during extreme events and provide energy to critical facilities like hospitals, city halls and essential infrastructure to keep our citizens safe and avoid a huge 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. Our training objectives will leverage existing educational efforts and outreach activities, and we will publicize the multidisciplinary outcomes through multiple venues.

This 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 is being developed for data-driven event detection, anomaly flag processing, root cause analysis, and decision support using semi-supervised and unsupervised learning improved for online learning and decision making. Additionally, visualization tools will be developed using cognitive factor analysis and human error analysis. The project will utilize a training process driven by cognitive and psychometric analysis and inspired by our experience in operators training in multiple domains: the power grid, aircraft, and spacecraft flight simulators. A systematic approach for human operator decision making is developed using quantifiable human and engineering analysis indices for power grid resiliency.
This project is sponsored by the National Science Foundation:

  1. Award#1840192 (Link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840192&HistoricalAwards=false)
  2. Award#1840052 (Link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840052&HistoricalAwards=false)
  3. Award#1840083 (Link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840083&HistoricalAwards=false)

FW-HTF Research Objectives

The objective of this project is to enhance reliability and accuracy of decision making in the power system control room by proposing methodologies that systematically augment operator situational awareness by achieving the right balance between information maintenance and updating:

  1. Use a dynamic attentional control (DAC) framework to precisely identify the cognitive mechanisms that are good targets for decision support tools in a control room operations context,
  2. Augment cognitive performance of human operators during extreme events using advanced machine learning based decision support tools and an adaptive Human-Machine system,
  3. Advancing cognitive performance of human operators with improved training simulators for enhanced grid resiliency that allow operators to gain the most benefit from tools designed to provide the feedback needed to adapt decision making to fast changing system scenarios.

Research Team

1 Anurag K. Srivastava Washington State University Lead PI
2 Paul Whitney Washington State University Co-PI
3 Adam Hahn Washington State University Co-PI
4 Saeed Lotfifard Washington State University Co-PI
5 Anjan Bose Energy Systems Innovation Center/ Power Engineering Partnership Co-PI
6 Alexandra von Meier University of California, Berkeley/ CIEE Co-PI
7 Gautam Biswas Vanderbilt University Co-PI
8 Abhishek Dubey Vanderbilt University Co-PI
9 Robin Podmore IncSys/PowerSimulator® Senior Personnel
10 Michael Legatt ResilientGrid Senior Personnel
11 Jodi Heintz Obradovich ResilientGrid Senior Personnel
12 Sean Murphy PingThings Senior Personnel
13 Eric Andersen Pacific Northwest National Lab Consultant
18 Michael Cassiadoro Total Reliability Solutions Consultant
19 Hongming Zhang Peak Reliability Consultant
20 Khalid Abdul-Rahman California ISO Unpaid Consultant
21 Aaron Janisko Snohomish PUD Unpaid Consultant
22 K S Sajan Washington State University Post-Doc
23 Nie Zhijie Washington State University PhD Student
24 Hussain, Mohammed Mustafa Washington State University PhD Student
25 Mohammad Ghanaatian-Jobzari Washington State University PhD Student
26 Anthony Stenson Washington State University PhD Student
27 Mohini Bariya University of California, Berkeley PhD Student
28 Miles Rusch University of California, Berkeley PhD Student
29 Ajay Chhokra Vanderbilt University Post-Doc
30 Carlos Barreto Vanderbilt University Post-Doc
31 Sanchita Basak Vanderbilt University PhD Student

FW-HTF Outcomes

As of August, 2020:




Project Management

Check the project details here.