[논문]Smart support system for diagnosing severe accidents in nuclear power plants

Yoo, Kwae Hwan; Back, Ju Hyun; Na, Man Gyun; Hur, Seop; Kim, Hyeonmin

doi:10.1016/j.net.2018.03.007

Smart support system for diagnosing severe accidents in nuclear power plants 원문보기

Nuclear engineering and technology : an international journal of the Korean Nuclear Society, v.50 no.4, 2018년, pp.562 - 569

Yoo, Kwae Hwan (Department of Nuclear Engineering, CHOSUN University) , Back, Ju Hyun (Department of Nuclear Engineering, CHOSUN University) , Na, Man Gyun (Department of Nuclear Engineering, CHOSUN University) , Hur, Seop (Korea Atomic Energy Research Institute) , Kim, Hyeonmin (Korea Atomic Energy Research Institute)

Abstract ▼ AI-Helper

Recently, human errors have very rarely occurred during power generation at nuclear power plants. For this reason, many countries are conducting research on smart support systems of nuclear power plants. Smart support systems can help with operator decisions in severe accident occurrences. In this study, a smart support system was developed by integrating accident prediction functions from previous research and enhancing their prediction capability. Through this system, operators can predict accident scenarios, accident locations, and accident information in advance. In addition, it is possible to decide on the integrity of instruments and predict the life of instruments. The data were obtained using Modular Accident Analysis Program code to simulate severe accident scenarios for the Optimized Power Reactor 1000. The prediction of the accident scenario, accident location, and accident information was conducted using artificial intelligence methods.

주제어

AI 본문요약
AI-Helper

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문제 정의

Human error is one factor that can cause severe accidents in NPPs. This study focused on constructing a smart support system to resolve the current problem.

제안 방법

In this module, the basis scenarios are classified by seven initiating events, calculated for an OPR1000 plant: hot-leg LOCA, coldleg LOCA, steam generator tube rupture, station blackout, main steam line break, feed water line break, and total loss of feed water. These seven initial events are expected to contribute to high core damage frequency.
In this study, a smart support system was developed to predict severe accidents. The prediction of accident scenarios, accident locations, and accident information is conducted using AI methodology.
The methods of estimating the signal using the AAKR model are divided into methods of using all signals and methods of grouping signals [10]. In this study, signals grouped using signal correlations were used. Fig.
In this study, we applied various methods to diagnose early accidents in NPPs. The MAAP code was used to describe accident situation, and the 66 measured signal data elements were used to diagnose severe accidents in NPPs.

이론/모형

4. Accident identification method using the trained SVC model.
The diagnosis module of instrument failure was developed to predict the integrity of instruments in severe accidents. The auto-associative kernel regression (AAKR) and sequential probability ratio test (SPRT) were used to diagnose the integrity of measurement signals under accidental conditions. The methods of estimating the signal using the AAKR model are divided into methods of using all signals and methods of grouping signals [10].

참고문헌 (11)

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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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원문보기 상세보기
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