Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework

Lee, Daeil; Seong, Poong Hyun; Kim, Jong-Hyun

doi:10.1016/j.anucene.2018.05.020

Cited by 60 publications

(19 citation statements)

References 8 publications

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“…Moreover, LSTM not only stores the values that are calculated from the previous time data in the LSTM cell but also considers previously saved values when calculating the next time data. The author's previous studies showed that the LSTM can support well the operation of nuclear systems [3], [5] as well as the diagnosis of events [47,48]. Moerover, to better support the selection of the LSTM neural network, this study added Appendix A to compare the performance of other neural networks such as deep neural network (DNN), convolutional neural network (CNN), LSTM, and C-LSTM(CNN + LSTM).…”

Section: Development Of An Algorithm For Power-increase Controlmentioning

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: Development Of An Algorithm For Power-increase Controlmentioning

confidence: 99%

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

2020

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“…Such methods include the prediction of plant parameters, the diagnosis of accidents, and autonomous operation during an accident [52][53][54]. The monitoring of sensor soundness can support the practical application of these incoming technologies.…”

Section: Discussionmentioning

confidence: 99%

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Choi

Lee

2020

Sensors

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A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states.

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“…Content may change prior to final publication. [160] Colored Petri Nets [193], [197], [198] Tree Methods [50], [200] Unsupervised Learning [161], [181] Fuzzy Methods [159], [182]- [189], [192], [195], [201] Other Models/Algorithms Physical Plant-centered [39], [53], [86]- [88], [123], [127]- [129], [150], [175], [193] Cyber System Layer & Physical System Layer ANN [105], [160] Fuzzy Colored Petri Nets (FCPN) [106] Reinforcement Learning (RL) [107], [108], [134]- [137] Recurrent Neural Network (RNN) [95], [97], [138], [138], [139], [146] Convolutional Neural Network (CNN) [140]- [142] Deep Belief Network [90], [121] SVM [118], [119], [122] Tree Methods [124]-…”

Section: Discussionmentioning

confidence: 99%

“…In another trial, Kim and Lee [107] tried to design the control functions for startup/shutdown procedures in NPPs with LSTM and operator operation-based control module framework which provides a good idea for realizing a fully autonomous control system. They also suggest using a function-based hierarchical framework along with LSTM to model and automatically control the safety systems of NPPs [108]. Furthermore, they designed an AI-based prototype, which also integrated their previous work via a nuclear simulator [109].…”

Section: ) Autonomous Control For Fixed Proceduresmentioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework

Cited by 60 publications

References 8 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

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

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