Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants

Farber, Jacob A.; Al Rashdan, Ahmad Y.; Reese, Randall D.; Sundaram, Arvind; Abdel-Khalik, Hany

doi:10.2172/2348877

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DOE OSTI · 2348877

Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants

Farber, Jacob A.·Al Rashdan, Ahmad Y.·Reese, Randall D.·Sundaram, Arvind·Abdel-Khalik, Hany

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Abstract

Historically, nuclear power plants have operated predominantly at or near full power, meaning that data driven anomaly detection methods can likely perform well at full power operations. This presents a challenge when the power drops (referred to as a transient) and may result in false alarms due to the lack of historical data at those new power levels. The current approach to handling this challenge is to turn detectors off during transients, which makes it impossible to use the algorithms to detect anomalies during these periods, i.e., causing missed detection.

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Farber, Jacob A., Al Rashdan, Ahmad Y., Reese, Randall D., Sundaram, Arvind, Abdel-Khalik, Hany. 2023-08-31. Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants. https://doi.org/10.2172/2348877

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