Open AccessHuman-Centric Contingency Analysis Metrics for Evaluating Operator Performance and Trust
Author(s) -
Alexander A. Anderson,
Brett A. Jefferson,
Slaven Kincic,
John E. Wenskovitch,
Corey K. Fallon,
Jessica A. Baweja,
Yousu Chen
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3322133
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
A novel set of system-state and control-action penalty functions are introduced as an alternative to traditional performance index contingency ranking. The novel system state penalty metrics are formulated based on piecewise linear functions of the system voltage and branch flow, guided by Weber’s Law of human cognition. Novel continuous and discrete control action metrics are also developed to measure the inherent cost and risk associated with every action taken by human power system operator to resolve violations on a pre-contingent basis. These new metrics are combined with traditional human factors indices for measuring human-machine trust and cognitive workload to create a systematic framework for measuring and evaluating operator trust and reliance on artificial intelligence (AI) algorithms for control room use. An existing AI-based contingency analysis recommender tool using a semi-supervised action algorithm is selected for a series of experiments with operations engineering staff using the IEEE 118 Bus System. The penalty metrics presented are demonstrated for both steady-state contingency analysis and transient stability studies, with the operations participants able to reduce the total system penalty in 85% of scenarios through remedial actions. A human-machine team was able to achieve equal or lower continuous control action penalty scores than the participant without availability of the recommender in 57% of experiment scenarios and lower continuous control action penalty scores than the AI tool alone in 83% of scenarios.