Development of advanced I&amp;C in nuclear power plants: ADIOS and ASICS

Kim, Jung-Taek; Kwon, Kee-Choon; Hwang, In-Koo; Lee, Dong-Young; Park, Won-Man; Kim, Jung-Soo; Lee, Sang Jeong

doi:10.1016/s0029-5493(00)00430-1

Cited by 23 publications

(6 citation statements)

References 4 publications

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“…Sekimizu et al [43] developed an automation system for start-up operation and sequential control that executes the operating procedure through if-then logic. An automatic start-up intelligent control system (ASICS), which uses knowledge-based technology and a distributed control system, has also been proposed for controlling a pressurized water reactor (PWR) from the cold shutdown state to 2% reactor power [44].…”

Section: B High-level Automation In the Operation Of Nppsmentioning

confidence: 99%

Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

2020

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The power start-up operation of a nuclear power plant (NPP) increases the reactor power to the full-power condition for electricity generation. Compared to full-power operation, the power-increase operation requires significantly more decision-making and therefore increases the potential for human errors. While previous studies have investigated the use of artificial intelligence (AI) techniques for NPP control, none of them have addressed making the relatively complicated power-increase operation fully autonomous. This study focused on developing an algorithm for converting all the currently manual activities in the NPP power-increase process to autonomous operations. An asynchronous advantage actorcritic, which is a type of deep reinforcement learning method, and a long short-term memory network were applied to the operator tasks for which establishing clear rules or logic was challenging, while a rule-based system was developed for those actions, which could be described by simple logic (such as if-then logic). The proposed autonomous power-increase control algorithm was trained and validated using a compact nuclear simulator (CNS). The simulation results were used to evaluate the algorithm's ability to control the parameters within allowable limits, and the proposed power-increase control algorithm was proven capable of identifying an acceptable operation path for increasing the reactor power from 2% to 100% at a specified rate of power increase. In addition, the pattern of operation that resulted from the autonomous control simulation was found to be identical to that of the established operation strategy. These results demonstrate the potential feasibility of fully autonomous control of the NPP power-increase operation.

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Section: B High-level Automation In the Operation Of Nppsmentioning

confidence: 99%

Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

2020

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“…Decision Support Systems (DSSs) can aid the operators and reduce the possibility of errors [Ma et al, 2011;Hsieh et al, 2012;Park et al, 2015]. Examples of DSSs are: the Alarm and Diagnosis-Integrated Operator Support (ADIOS) system that attempts to avoid that too many alarms influence the operators judgement in a wrong way [Kim et al, 2001]; the Hidden Markov Model (HMM) for recognizing accidents in NPPs, proposed in [Kwon et al, 1999];…”

Section: Two Practical Issues Arementioning

confidence: 99%

“…the Fault Diagnosis Advisory System (FDAS), based on dynamic neural networks ; the Dynamic System Doctor (DSD), a system-independent interactive software for online state/parameter estimation in dynamic system [Aldemir et al, 2001]; the Analysis of Dynamic Accident Progression Trees (ADAPT) methodology [Hakobyan et al, 2008]; the unsupervised clustering technique for NPP components fault diagnosis, based on Haar Wavelet Transform (HWT) and Fuzzy C-Means (FCM) algorithm, in ; the hybrid approach for balancing false and missed alarms, based on Correlation Analysis (CA), Genetic Algorithms (GAs) and Sequential Probability Ratio Tests (SPRTs) in [Di Maio et al, 2013]; the non-parametric decision strategy in [Al-Dahidi et al, 2014] to detect whether NPP components are in abnormal conditions, using Prediction Intervals (PIs) and Auto-Associative Kernel Regression (AAKR) [Hines et al, 1998]. However, there is no guarantee of improvement in the operators' performance and sometimes the result could be the increase of operator workload, with negative implications on performance [Yoshikawa 2005;Kim et al, 2007;Hsieh et al, 2012].…”

Section: Two Practical Issues Arementioning

confidence: 99%

A Regional Sensitivity Analysis-based Expert System for safety margins control

Maio

Bandini

D’Amato

et al. 2018

Nuclear Engineering and Design

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Thermal-Hydraulic (TH) codes are used to simulate the response of nuclear safety systems under transient and accident conditions. The outcomes of the simulations are used to verify the safety margins required for safe operation and make decisions on how to maintain them. In this work, a novel Expert System (ES) based on Regional Sensitivity Analysis (RSA) is developed to guide a system undergoing an accident scenario towards the safest conditions in the optimal number of operation. The ES proceeds by firstly identifying the (uncertain) system controllable variables (i.e., control rods position, feedwater flow rate, void fraction inside the steam generator, etc.) that most affect the system response by RSA; then, the limit-state function is calibrated on a dataset of outcomes of TH code runs and the system failure boundary (i.e., the limit surface) is defined on the set of (uncertain) TH input variables. Application of the ES is firstly shown with respect to an analytical case study that artificially simulates the response of a NPP to an accident scenario and, then, to a practical case study concerning the response of the pressurizer of a Pressurized Water Reactor (PWR).

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“…However, according to several papers that assessed the evaluation results provided by decision support systems, such systems do not guarantee improvement in the operator's performance. Some support systems could actually increase the operator's mental workload during these critical times [8][9]. Therefore, an effective expert system must aid, not hinder, the operator's cognitive processes.…”

Section: Backgroundsmentioning

confidence: 99%

Development of an expert system for abnormal operating procedures in a main control room

Hsieh

Hwang

Liu

et al. 2012

Work

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The study was conducted from the perspective of human factors engineering in order to compare the process that operators originally used to diagnose potential and actual faults with a process that included an expert system for diagnosing faults. The results of the study indicated that the existence of an expert system for fault diagnosis makes the task of fault diagnosis easier and reduces errors by quickly suggesting likely Abnormal Operating Procedures (AOPs)

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Development of advanced I&C in nuclear power plants: ADIOS and ASICS

Cited by 23 publications

References 4 publications

Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

A Regional Sensitivity Analysis-based Expert System for safety margins control

Development of an expert system for abnormal operating procedures in a main control room

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