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

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

Sr. No.	NAME	ORGANIZATION	PROJECT ROLE
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:

PROTOTYPE TOOLS

1. Synchrophasor Anomaly and Event Detection (SyncAED) Tool: Data anomaly and event detection using machine learning, statistical algorithms, and physics-based rules. This tool provides situational awareness to grid operators, especially in extreme events.

JOURNAL PAPERS

1. S. Eisele, T. Eghtesad, K. Campanelli, P. Agrawal, A. Laszka, and A. Dubey, “Safe and Private Forward-Trading Platform for Transactive Microgrids“, Transactions on Cyber-Physical Systems, 2020.
2. S. Pandey, A. Srivastava and B. Amidan, “A Real Time Event Detection, Classification and Localization using Synchrophasor Data,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.2986019.
3. Y. Jiang and A. K. Srivastava, “Data-Driven Event Diagnosis in Transmission Systems With Incomplete and Conflicting Alarms Given Sensor Malfunctions,” in IEEE Transactions on Power Delivery, vol. 35, no. 1, pp. 214-225, Feb. 2020, doi: 10.1109/TPWRD.2019.2947671
4. I. Kiaei, S. Lotfifard, “A Two-stage Fault Location Identification Method in Multi-area Power Grids Using Heterogeneous Types of Data” IEEE Transactions on Industrial Informatics, vol. 15, no.7, pp. 4010 – 4020, 2019.
5. Tushar, V. Venkataramanan, A. Srivastava and A. Hahn, “CP-TRAM: Cyber-Physical Transmission Resiliency Assessment Metric,” in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.2996137

CONFERENCE PAPERS

1. Syed M Hur Rizvi, K. S. Sajan, and A. Srivastava, “Real-time ZIP Load Parameter Tracking using Adaptive Window and Variable Elimination with Realistic Synthetic Synchrophasor Data,” Accepted for IEEE Industry Applications Society Annual Meeting, October, 2020.
2. K. S. Sajan, M. Bariya, S. Basak, A. Srivastava, A. Dubey, A. von Meier, and G. Biswas, “ Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification,” 2020 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, Sydney, Australia, 2020, pp. 1-6, doi: 10.1109/MSCPES49613.2020.9133691.
3. C. Barreto, T. Eghtesad, S. Eisele, A. Laszka, A., Dubey, X. Koutsoukos.  “Cyber-attacks and mitigation in blockchain-based transactive energy systems“. In 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2020).
4. A. Chhokra, S. Hasan, A. Dubey, and G. Karsai, “A Binary Decision Diagram Based Cascade Prognostics Scheme For Power Systems“, in 2020 American control conference, 2020.
5. S. Basak, A. Dubey, and L. Bruno, “Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 2144-2153.
6. M. Bariya, K. Moffat and A. Von Meier, “Physically Meaningful Grid Analytics on Voltage Measurements using Graph Spectra,” 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2020, pp. 1-5, doi: 10.1109/ISGT45199.2020.9087671.
7. S. Pandey, N. Patari, and A. Srivastava, “Cognitive Flexibility of Power Grid Operator and Decision Making in Extreme Events ” IEEE PES General Meeting, August 2019, Atlanta, GA, USA.

Project Management

Check the project details here.