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Accident diagnosis algorithm with untrained accident identification during power-increasing operation

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  • Yang, Jaemin
  • Kim, Jonghyun

Abstract

To ensure the safety of nuclear power plants (NPPs) from accidents or anomalies, regulatory bodies provide procedures that describe safety regulations that must be followed. However, even if well-designed procedures are provided to operators, diagnostic activity in an emergency scenario is classified as an extremely demanding task. Moreover, the diagnosis of accidents occurring under various operation modes, such as power increasing, is expected to be extremely difficult, owing to the diverse behaviors and availability of systems and components. With regard to such emergency response issues, artificial neural network-based methods are regarded as one of the most promising approaches, because of their noticeable achievements. However, regarding the application of neural networks, in the case of an untrained accident, there is no capability to answer “do not know.†This study aims to develop algorithms that can cover various NPP operation modes and deal with untrained accidents. To address the various NPP operation modes, the major changes that can affect the plant states are classified. Furthermore, to deal with untrained accidents, the applied diagnostic algorithms use long short-term memory and an autoencoder. Following this, this paper presents the implementation and test results of the accident diagnosis algorithms.

Suggested Citation

  • Yang, Jaemin & Kim, Jonghyun, 2020. "Accident diagnosis algorithm with untrained accident identification during power-increasing operation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305330
    DOI: 10.1016/j.ress.2020.107032
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    References listed on IDEAS

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    1. Santosh, T.V. & Vinod, Gopika & Saraf, R.K. & Ghosh, A.K. & Kushwaha, H.S., 2007. "Application of artificial neural networks to nuclear power plant transient diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1468-1472.
    2. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    3. Rocco S., Claudio M. & Zio, Enrico, 2007. "A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 593-600.
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    Cited by:

    1. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.
    2. Joo, Semin & Lee, Yeonha & Song, Seok Ho & Song, Kyusang & Seo, Mi Ro & Kim, Sung Joong & Lee, Jeong Ik, 2025. "Leveraging explainable AI for reliable prediction of nuclear power plant severe accident progression," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    3. Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).

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