• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.1-13
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• 2020

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.868-870
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• 1999

In this paper we obtain a discrete mathmatical model of a Boiler control system from expermental data, we find appropriate input signal and parameter estimation algorithm for identification of the Boiler control system in power plant. Under these conditions experimental data are collected from real system and parameters are estimated by the Recursive Least Square algorithm. The computer simulation results show the parameter estimation algorithm for identification and the effectiveness of controller design of the Boiler control system.

• Journal of Environmental Science International
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• v.26 no.12
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• pp.1333-1339
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• 2017

Coal-fired power plants emit various Particulate Matter(PM) at coal storage pile and ash landfill as well as the stack, and affect the surrounding environment. Field Emission Scanning Electron Microscopy and Energy Dispersive X-ray analyzer(FE-SEM/EDX) were used to develop identification factor and the physico-chemical analysis of PM emitted from a power plant. In this study, three samples of pulverized coal, bottom ash, and fly ash were analyzed. The pulverized coal was spherical particles in shape and the chemical composition of C-O-Si-Al and C/Si and C/Al ratios were 200~300 on average. The bottom ash was spherical or non-spherical particles in shape, chemical composition was O-C-Si-Al-Fe-Ca and C/Si and C/Al ratios were $4.3{\pm}4.6$ and $8.8{\pm}10.0$. The fly ash was spherical particles in shape, chemical composition was O-Si-Ai-C-Fe-Ca and C/Si and C/Al ratios were $0.5{\pm}0.2$ and $0.8{\pm}0.5$.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2888-2898
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• 2021

Vital area identification (VAI) is an essential procedure for the design of physical protection systems (PPSs) for nuclear power plants (NPPs). The purpose of PPS design is to protect vital areas. VAI has been improved continuously to overcome the shortcomings of previous VAI generations. In first-generation VAI, a sabotage fault tree was developed directly without reusing probabilistic safety assessment (PSA) results or information. In second-generation VAI, VAI model was constructed from all PSA event trees and fault trees. While in third-generation VAI, it was developed from the simplified PSA event trees and fault trees. While VAIs have been performed for NPPs in full-power operations, VAI for NPPs in low-power and shutdown (LPSD) operations has not been studied and performed, even though NPPs in LPSD operations are very vulnerable to sabotage due to the very crowded nature of NPP maintenance. This study is the first to research and apply VAI to LPSD operation of NPP. Here, the third-generation VAI method for full-power operation of NPP was adapted to the VAI of LPSD operation. In this study, LPSD VAI for a few plant operational states (POSs) was performed. Furthermore, the operation strategy of vital areas for both full-power and LPSD operations was discussed. The LPSD VAI method discussed in this paper can be easily applied to all POSs. The method and insights in this study can be important for future LPSD VAI that reflects various LPSD operational states. Regulatory bodies and electric utilities can take advantage of this LPSD VAI method.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2038-2045
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• 2021

The main objective of this work is to propose a reliable routine standard operation procedures (SOP) for structural health monitoring and diagnosis of nuclear power plants (NPPs). At present, NPPs have monitoring systems that can be used to obtain the quantitative health record of containment (CTMT) buildings through system identification technology. However, because the measurement signals are often interfered with by noise, the identification results may introduce erroneous conclusions if the measured data is directly adopted. Therefore, this paper recommends the SOP for signal screening and the required identification procedures to identify the dynamic characteristics of the CTMT of NPPs. In the SOP, three recommend methods are proposed including the Recursive Least Squares (RLS), the Observer Kalman Filter Identification/Eigensystem Realization Algorithm (OKID/ERA), and the Frequency Response Function (FRF). The identification results can be verified by comparing the results of different methods. Finally, a preliminary CTMT healthy record can be established based on the limited number of earthquake records. It can be served as the quantitative reference to expedite the restart procedure. If the fundamental frequency of the CTMT drops significantly after the Operating Basis Earthquake and Safe Shutdown Earthquake (OBE/SSE), it means that the restart actions suggested by the regulatory guide should be taken in place immediately.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1008-1011
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• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

• Journal of Power System Engineering
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• v.3 no.2
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• pp.34-41
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• 1999

This paper identifies a linear combustion model of Refuse Incineration Plant(RIP) which characterizes its combustion dynamics, where the proposed model has thirteen-inputs and one-output. The structure of the RIP model is given as an ARX model which obtained from the theoretical analysis. And then, some unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. In result, it is shown that the proposed model well approximates the input-output combustion characteristics riven by experimental data sets.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.897-902
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• 2001

This paper describes the evaluation of noise influence for residental and boundary areas in 75MW thermal power plant. It includes the measurements of noise level around the boundary area of the plant, identification of noise propagating path, and discussion on the measures. Noise assessments are carried out based on the ISO 3744, ISO 9613-1 and ISO 9613-2 to predict the noise distribution of specific locations from the noise sources such as power transformers, flash vent-pipe, I.D.fan, and stack. It is identified the vent-pipe of flash tank in thermal power plant as the root cause of the environmental noise during the plant commissioning process.

• Korean Journal of Construction Engineering and Management
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• v.18 no.1
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• pp.48-57
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• 2017

The construction of the first nuclear power plant (Kori #1) in Korea started in 1971, Korea operates 24 nuclear power plants and is constructing 4 in 2015. During about 45 years of construction experience, insufficient studies have been investigated for the identification of critical success factors(CSFs) for nuclear power plant construction. Based on literature reviews and focus group interviews, this study presented a list of CSFs for construction of nuclear power plants. A survey for validating the results of CSFs was conducted with 164 experts. This study indicates that attentions should be placed upon the lowest price-based contract awarding policy, the need for reasonable pricing standard implementation, database development, and deployment for experienced nuclear power plant construction workers, identification and nurturing of competitive subcontractors, and minimization of lag times in construction activities.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1449-1454
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• 2003

For many industrial processes there are good static models used for process design and steady state operation. By using system identification techniques, it is possible to obtain black-box models with reasonable complexity that describe the system well in specific operating conditions [1]. But black-box models using inductive modeling(IM) is not suitable for model based control because they are only valid for specific operating conditions. Thus we need to use deductive modeling(DM) for a wide operating range. Furthermore, deductive modeling is several merits: First, the model is possible to be modularized. Second, we can increase and decrease the model complexity. Finally, we are able to use model for plant design. Power plant must be able to operate well at dramatic load change and consider safety and efficiency. This paper proposes a simplified nonlinear model of an industrial boiler, one of component parts of a power plant, by DM method and applies optimal control to the model.