A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

Wenskovitch, John E.; Jefferson, Brett A.; Anderson, Alexander A.; Baweja, Jessica A.; Ciesielski, Danielle K.; Fallon, Corey K.

doi:10.3389/fdata.2022.897295

DOE OSTI · 1875035

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

Wenskovitch, John E.·Jefferson, Brett A.·Anderson, Alexander A.·Baweja, Jessica A.·Ciesielski, Danielle K.·Fallon, Corey K.

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Abstract

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

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29 ENERGY PLANNING, POLICY, AND ECONOMY cognitive load contingency analysis human-machine teaming power grid trust evaluation

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Wenskovitch, John E., Jefferson, Brett A., Anderson, Alexander A., Baweja, Jessica A., Ciesielski, Danielle K., Fallon, Corey K.. 2022-06-14. A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis. https://doi.org/10.3389/fdata.2022.897295

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