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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has since been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that Unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. local time on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
J. Wesley Hines, Darryl J. Wrest, Robert E. Uhrig
Nuclear Technology | Volume 119 | Number 2 | August 1997 | Pages 181-193
Technical Paper | Reactor Control | doi.org/10.13182/NT97-A35385
Articles are hosted by Taylor and Francis Online.
An adaptive neural fuzzy inference system modeling technique is introduced for sensor and associated instrument channel calibration validation. This method uses an inferential-modeling technique after a genetic algorithm search is used to empirically determine the appropriate combinations of input variables to optimally model each signal to be monitored. These variables are used as input to a fuzzy inference system that is trained to estimate the monitored signals. The estimates are compared with the actual signals, and a statistical decision technique known as the sequential probability ratio test is used to detect sensor anomalies. The sensor fault detection system is demonstrated using data supplied from Florida Power Corporation’s Crystal River Unit 3 nuclear power generating station.