Daniel Gagnon

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About

Daniel Gagnon is a leading researcher in the field of electrical infrastructure asset management, with a particular focus on the aging and maintenance of overhead transmission line conductors. His work uniquely integrates robotics, sensor technology, and predictive aging modeling to develop comprehensive, data-driven strategies for utility grid reliability. Gagnon’s most-cited paper, "An Integrated Asset Management Strategy for Transmission Line Conductors, Based on Robotics, Sensors, and Aging Modelling" (2020), has garnered 5 citations and stands as a foundational contribution to modernizing how power utilities assess conductor degradation. By combining robotic inspection platforms with real-time sensor data and metallurgical aging models, he has advanced the ability to predict remaining conductor life and prioritize maintenance interventions—reducing both costs and outage risks. His research bridges the gap between field engineering and computational modeling, offering utilities a practical, scalable framework for transitioning from reactive to predictive maintenance. Gagnon’s work is particularly influential for engineers and researchers seeking to extend the lifespan of aging grid assets while integrating emerging technologies like drones and IoT sensors into legacy systems.