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Fukushima accident was worse by instrument inability. Eventually, the accident was not mitigated and keeping the integrity of reactor was failed since the operators was not able to quickly understand the situation and take necessary actions. Therefore, in this study, reactor vessel (RV) water level considered as one of the parameters to keep the integrity of reactor is predicted in loss of coolant accident (LOCA) situation using the deep neural network (DNN) method. This is in an effort to provide supporting information under the severe circumstance. The simulation data obtained from modular accident analysis program (MAAP) are applied to the DNN method to check the prediction performance of the RV water level. The prediction performance of RV water level using the proposed DNN model is presented as root mean square error (RMSE). Although the data of several circumstances among a variety of LOCAs are applied, good prediction performance is shown using the proposed DNN method.

The reactor protection system (RPS) is designed to minimize a propagation of the abnormal or accident conditions of nuclear power plants. We have developed a KINCS-RPS prototype by using POSAFE PLC. The main design feature of the KNICS RPS is to satisfy the APR 1400 design requirements and to improve the availability and maintainability. The application software and hardware of the RPS are being developed according to the safety critical software life cycle and the safety H/W criteria. Especially, the software requirement specification of the RPS application software is written in a formal language for a correct software verification and validation. Also, the KNICS RPS has undergone an equipment qualification (EQ) test. The RPS prototype satisfied the nuclear EQ criteria except for some minor points. It is expected that the digital reactor protection system being developed through the 2nd phase of the KNICS project will be able to be applied to the existing NPPs for a replacement or to a new nuclear power plant.

This paper proposes an effective selection method of the control weighting for constrained GPC. Considered are the constraint in the control and the constraint in the rate of the control as well. The effectiveness of the proposed method is shown through an illustrative example of level control of the steam generator in low power operation.