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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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.”
Young Do Koo, Ju Hyun Back, Man Gyun Na (Chosun Univ)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 440-447
If the undesired situations such as a transient or an accident improperly affecting normal operation occur in nuclear power plants (NPPs), accurately checking the NPP state by the operators using temporary trends of several instrumentation signals in a short time can be constrained. Therefore, this study was carried out to provide the transient identification information to the operators in a short time after the reactor trip according to the abnormal circumstance occurrence using the deep learning since the diagnosis of the NPP states is prior for effective accident management. To establish the deep learning model identifying the initial events of the NPPs, the simulated accident data were applied to train the deep learning model. These data were obtained by simulating the postulated scenarios using the modular accident analysis program (MAAP). The data from the MAAP code are used to calculate the time-integrated values of the simulated instrumentation signals. That is, the deep learning model is trained to find the optimized classifier to identify the events using the simulated signals of the accident data showing the behaviors of each accident circumstance. Utilized simulated signals were considered as some of the highly correlative accident monitoring variables. In this study, deep neural networks (DNNs) were used for identifying the transients of the NPPs. The identification performance of the DNN model, and moreover the support vector machine (SVM) model in the previous study is able to be checked in this paper. In addition, performance of the artificial intelligence methods as advanced technologies monitoring and diagnosing the NPP states can be assessed.