Naturalistic Decision Making for Power System Operators

The concepts of recognition primed decision making, situation awareness levels, and cognitive task analysis are applied for the first time to training of electric power system operators to provide a viable framework for systematic training management.

Abstract

Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three t's: trees, training, and tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (ndm) model was developed. Transcripts of conversations were analyzed to reveal and assess ndm-based performance criteria. An item analysis indicated that the operators' situation awareness levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/visualization tools. This study applies for the first time the concepts of recognition primed decision making, situation awareness levels, and cognitive task analysis to training of electric power system operators. The ndm approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.