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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Defu Zhang, Gary A. Hallock, Alan J. Wootton, Robert H. Flake, John R. Uglum
Fusion Science and Technology | Volume 32 | Number 3 | November 1997 | Pages 431-443
Technical Paper | Plasma Control Issues for Tokamaks | doi.org/10.13182/FST97-A6
Articles are hosted by Taylor and Francis Online.
A linear, second-order transformer model using magnetic sensor coils has been developed to describe the position control of a tokamak. This model is used to analyze the behavior of a proportional-derivative controller, which has been implemented on TEXT-Upgrade (TEXT-U). The magnetic sensor coils may be placed internal or external to the conducting vacuum vessel. If placed externally, however, eddy currents induced in the vessel wall introduce an error in the position measurement. It is found that this error signal introduces a positive zero in the system transfer function. The transfer function becomes a non-minimum-phase function, which restricts the response speed, stable area, and utilization of the power supply capability. Although the position control system is stabilized by use of a proportional-derivative controller, the controller cannot affect the positive zero. This analysis has been experimentally verified on TEXT-U. With external sensors, the stable operating area is small, and the sensors exhibit an initial undershoot to a step position change, as expected. The observed stable area is predicted by the model, although the model overestimates the size of the actual stable area.