• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.78-91
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• 2002

In the thermal power plant, there are six manipulated variables: main steam flow, feedwater flow, fuel flow, air flow, spray flow, and gas recirculation flow. There are five controlled variables: generator output, main steam pressure, main steam temperature, exhaust gas density, and reheater steam temperature. Therefore, the thermal power plant control system is a multinput and output system. In the control system, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, strict control of the steam temperature must be maintained to avoid thermal stress. Maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature versus changes in fuel flow rate, difficulty of control of the main steam temperature control and the reheater steam temperature control system owing to the dynamic response characteristics of changes in steam temperature and the reheater steam temperature, and the fluctuation of inner fluid water and steam flow rates during the load-following operation. Up to the present time, the Proportional-Integral-Derivative Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on the characteristic comparison of the PID controller and the modified 2-DOF PID Controller (Two-Degrees-Freedom Proportional-Integral-Derivative) on the DCS (Distributed Control System). The method is to design an optimal controller that can be operated on the thermal generating plant in Seoul, Korea. The modified 2-DOF PID controller is designed to enable parameters to fit into the thermal plant during disturbances. To attain an optimal control method, transfer function and operating data from start-up, running, and stop procedures of the thermal plant have been acquired. Through this research, the stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Also, this paper addressed whether an intelligent tuning method based on immune network algorithms can be used effectively in tuning these controllers.

• Nuclear Engineering and Technology
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• 제25권1호
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• pp.125-135
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• 1993

안전하고 효율적인 원자력 발전소의 운전은 디지탈 기술을 이용한 발전소 자동화로 이루어질 수 있다는 인식과 함께 이같은 발전소 자동화는 차세대 원자력 발전소의 중요한 목표중의 하나가 되고 있다. 전체적인 발전소 수준의 자동화를 위해서는 일차적으로 각 주요 시스템에 대한 디지털화가 요구되며 본 논문에서는 증기발생기의 수위조절 시스템에 대해 연구하였다. 이를 위해 증기발생기의 열수력학적 모델을 이용하여 증기발생기에 작용하는 여러가지 입력과 수위와의 관계를 전달함수로 표시하였으며 이를 이용하여 기존의 발전소에서 사용되고 있는 3 요소 제어시스템을 검토하였다. 본 논문에서의 제어구성은 증기발생기 그 자체를 시스템내에 플랜트로서 포함시킨 것이기 때문에 전체적인 시스템 차수가 증가하며 디지탈 과정중 수치적 불안정이 야기된다. 이러한 문제와 아울러 저출력에서는 궤환신호로 작용하는 급수유량의 신뢰도가 작음을 고려하여 2 요소 제어시스템 및 그에 따른 디지탈 제어기에 대해 연구하였다. 이 시스템의 디지탈 비례적분제어기는 그 이득 및 적분시간상수가 초기출력에 따라 변하며 전체적인 시스템의 응답특성이 안정성 및 기타 제어 특성을 동시에 만족시키도록 하고 있다. 이러한 제어기를 사용한 2 요소 제어시스템은 초기출력에만 의존하므로 정의하기가 간단하며 또 이러한 시스템의 수위응답은 그에 대응하는 아날로그 시스템의 결과와 비슷함을 보이고 있다.

• Nuclear Engineering and Technology
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• 제50권3호
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• pp.356-367
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• 2018

A series of tests dedicated to station blackout (SBO) accident scenarios have been recently performed at the $Prim{\ddot{a}}rkreislauf-Versuchsanlage$ (primary coolant loop test facility; PKL) facility in the framework of the OECD/NEA PKL-3 project. These investigations address current safety issues related to beyond design basis accident transients with significant core heat up. This work presents a detailed analysis using the best estimate thermal-hydraulic code TRACE (v5.0 Patch4) of different SBO scenarios conducted at the PKL facility; failures of high- and low-pressure safety injection systems together with steam generator (SG) feedwater supply are considered, thus calling for adequate accident management actions and timely implementation of alternative emergency cooling procedures to prevent core meltdown. The presented analysis evaluates the capability of the applied TRACE model of the PKL facility to correctly capture the sequences of events in the different SBO scenarios, namely the SBO tests H2.1, H2.2 run 1 and H2.2 run 2, including symmetric or asymmetric secondary side depressurization, primary side depressurization, accumulator (ACC) injection in the cold legs and secondary side feeding with mobile pump and/or primary side emergency core coolant injection from the fuel pool cooling pump. This study is focused specifically on the prediction of the core exit temperature, which drives the execution of the most relevant accident management actions. This work presents, in particular, the key improvements made to the TRACE model that helped to improve the code predictions, including the modeling of dynamical heat losses, the nodalization of SGs' heat exchanger tubes and the ACCs. Another relevant aspect of this work is to evaluate how well the model simulations of the three different scenarios qualitatively and quantitatively capture the trends and results exhibited by the actual experiments. For instance, how the number of SGs considered for secondary side depressurization affects the heat transfer from primary side; how the discharge capacity of the pressurizer relief valve affects the dynamics of the transient; how ACC initial pressure and nitrogen release affect the grace time between ACC injection and subsequent core heat up; and how well the alternative feeding modes of the secondary and/or primary side with mobile injection pumps affect core quenching and ensure stable long-term core cooling under controlled boiling conditions.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.