• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.3
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• pp.41-49
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• 1999

In this paper, we design a controller using the $\mu$-synthesis method and apply it for the spring-mass system with noncollocated sensors and actuators. We assume that the values of the spring stiffness and load mass of the plant are uncertain. The plant is modeled with parametric uncertainty by using the state space equation, especially the descriptor form. The $H_\infty$ controller designed by the $\mu$-synthesis method is compared with the standard $H_\infty$ controller To compare performances of two $H_\infty$ controllers, it is assumed that both controllers were designed with same weighting functions except that the $\mu$-synthesis controller has structured uncertainties. By compared with the standard $H_\infty$ controller, we show that the designed controller has satisfactory robust performance as well as robust stability by simulations and experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.423-426
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• 2005

In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.570-581
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• 2018

Recently, a software program has been used in nuclear power plants (NPPs) to digitalize many instrumentation and control systems. To guarantee NPP safety, the reliability of the software used in safetycritical instrumentation and control systems must be quantified and verified with proper test cases and test environment. In this study, a software testing method using a simulation-based software test bed is proposed. The test bed is developed by emulating the microprocessor architecture of the programmable logic controller used in NPP safety-critical applications and capturing its behavior at each machine instruction. The effectiveness of the proposed method is demonstrated via a case study. To represent the possible states of software input and the internal variables that contribute to generating a dedicated safety signal, the software test cases are developed in consideration of the digital characteristics of the target system and the plant dynamics. The method provides a practical way to conduct exhaustive software testing, which can prove the software to be error free and minimize the uncertainty in software reliability quantification. Compared with existing testing methods, it can effectively reduce the software testing effort by emulating the programmable logic controller behavior at the machine level.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.611-622
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• 2012

"Scaling" plays an important role for safety analyses in the licensing of water cooled nuclear power reactors. Accident analyses, a sub set of safety analyses, is mostly based on nuclear reactor system thermal hydraulics, and therefore based on an adequate experimental data base, and in recent licensing applications, on best estimate computer code calculations. In the field of nuclear reactor technology, only a small set of the needed experiments can be executed at a nuclear power plant; the major part of experiments, either because of economics or because of safety concerns, has to be executed at reduced scale facilities. How to address the scaling issue has been the subject of numerous investigations in the past few decades (a lot of work has been performed in the 80thies and 90thies of the last century), and is still the focus of many scientific studies. The present paper proposes a "roadmap" to scaling. Key elements are the "scaling-pyramid", related "scaling bridges" and a logical path across scaling achievements (which constitute the "scaling puzzle"). The objective is addressing the scaling issue when demonstrating the applicability of the system codes, the "key-to-scaling", in the licensing process of a nuclear power plant. The proposed "road map to scaling" aims at solving the "scaling puzzle", by introducing a unified approach to the problem.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.207-220
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• 2005

Nuclear power plants (NPP) are complex socio-technological systems that rely on the success of both hardware and human components. Empirical studies of plant operating experience show that human errors are important contributors to accidents and incidents, and that organizational factors play an important role in creating contexts for human errors. Current probabilistic safety assessments (PSA) do not explicitly model the systematic contribution of organizational factors to safety. As some countries, like the United States, are moving towards increased use of risk information in the regulation and operation of nuclear facilities, PSA quality has been identified as an area for improvement. The modeling of human errors, and underlying organizational weaknesses at the root of these errors, are important sources of uncertainty in existing PSAs and areas of on-going research. This paper presents a review of research into the following questions: Is there evidence that organizational factors are important to NPP safety? How do organizations contribute to safety in NPP operations? And how can these organizational contributions be captured more explicitly in PSA? We present a few past incidents that illustrate the potential safety implications of organizational deficiencies, some mechanisms by which organizational factors contribute to NPP risk, and some of the methods proposed in the literature for performing root-cause analyses and including organizational factors in PSA.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.264-270
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• 1998

This research broadens the prime concern of nuclear power plant operations from safe performance to both economic and safe performance. First emergency diesel generator is identified as one of main contributors for the lost plant availability through the review of plants forced outage records. The framework of an integrated architecture for performing modern on-line condition for operational availability improvement is configured in this work. For the development of the comprehensive sensor networks for complex target systems, an integrated methodology incorporating a structural hierarchy, a functional hierarchy, and a fault-system matrix is formulated. The second part of our research is development of intelligent diagnosis and maintenance advisory system, which employs Bayesian Belief networks (BBNs) as a high level reasoning tool incorporating inherent uncertainty use in probabilistic inference. Our prototype diagnosis algorithms are represented explicitly through topological symbols and links between them in a causal direction. As new evidence from sensor network development is entered into the model especially, our advisory of system provides operational advice concerning both availability and safety, so that the operator is able to determine the likely modes, diagnose the system state, locate root causes, and take the most advantageous action. Thereby, this advice improves operational availability

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.77-84
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• 1998

Speed control systems of the governor in power station used in this study is organized by the regulator (PID controller), actuator and turbine. Considering parameter uncertainties and disturbances in this system, the performance may not be achieved by the PID control. Therefore, a design technique is necessary that accomplish the desired system performance tolerance in despite of plant uncertainty i\I1d disturbances. In this study, we used QFT(Quantitative Feedback Theory) to provide stable operation in power plant and presented the genetic algorithm for loop shaping approximation technique of QFT. And we designed speed control systems for the governor using the above approach.proach.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.113-123
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• 2017

In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1331-1344
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• 2021

This study is to evaluate the seismic capacity of the fire-damaged cabinet facility in a nuclear power plant (NPP). A prototype of an electrical cabinet is modeled using OpenSees for the numerical simulation. To capture the nonlinear behavior of the cabinet, the constitutive law of the material model under the fire environment is considered. The experimental record from the impact hammer test is extracted trough the frequency-domain decomposition (FDD) method, which is used to verify the effectiveness of the numerical model through modal assurance criteria (MAC). Assuming different temperatures, the nonlinear time history analysis is conducted using a set of fifty earthquakes and the seismic outputs are investigated by the fragility analysis. To get a threshold of intensity measure, the Monte Carlo Simulation (MCS) is adopted for uncertainty reduction purposes. Finally, a capacity estimation model has been proposed through the investigation, which will be helpful for the engineer or NPP operator to evaluate the fire-damaged cabinet strength under seismic excitation. This capacity model is presented in terms of the High Confidence of Low Probability of Failure (HCLPF) point. The results are validated by the proper judgment and can be used to analyze the influences of fire on the electrical cabinet.