Control of a PWR nuclear reactor core power using scheduled PID controller with GA, based on two-point kinetics model and adaptive disturbance rejection system

Mousakazemi, Seyed Mohammad Hossein

doi:10.1016/j.anucene.2019.02.019

Cited by 36 publications

(8 citation statements)

References 29 publications

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“…Various methods have been presented for applying AI to the tuning of proportional-integral-derivative (PID) controllers, which are widely used in NPPs [28]- [30]. Upadhyaya et al designed an autonomous control system for a space reactor using a PID controller.…”

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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“…In the past, load following capabilities of nuclear reactors have been studied in numerous references, which are reviewed in the main part of this paper. They mostly rely on single point kinetics (Aboanber et al, 2014;Arab-Alibeik and Setayeshi, 2005;Ben-Abdennour et al, 1992;Edwards et al, 1990;Elsisi and Abdelfattah, 2020;Hui et al, 2020;Hui and Yuan, 2021;2022c;d;Khajavi et al, 2002;Khorramabadi et al, 2008;Li and Zhao, 2013a;b, 2014;Liu and Wang, 2014;Mousakazemi, 2019;Nair and Gopal, 1987;Park and Cho, 1992;Torabi et al, 2011;Wang et al, 2019;Zarei et al, 2016) or multi-point kinetics (Ansarifar and Saadatzi, 2015;Eliasi et al, 2011;Hui and Yuan, 2022a;b;Kobayashi and Yoshikuni, 1982;Li, 2014a;b;Li et al, 2014b;Marseguerra et al, 2003;Na et al, 1998b;a;Na, 2001;Onega and Kisner, 1978;Parhizkari et al, 2015;Pradhan et al, 2016;Saadatzi and Ansarifar, 2017;Shimazu, 1995;Winokur and Tepper, 1984;Yadav et al, 2018) or on 1D time-dependent diffusion codes (Christie and Poncelet, 1973;…”

Section: Introductionmentioning

confidence: 99%

“…A wide variety of control methods is used, including linear methods like proportional-integral-derivative (PID) control (Mousakazemi, 2019), linear-quadratic-Gaussian control (Edwards et al, 1990;Ben-Abdennour et al, 1992;Li et al, 2014a), minimum-variance control (Na et al, 1998b), model predictive control (receding horizon control) (Na, 2001;Wang et al, 2017), robust control (Torabi et al, 2011), non-linear methods like global linearising control (Park and Cho, 1992), feedback linearization control (Zaidabadi nejad and Ansarifar, 2018;Naimi et al, 2022b), non-linear dynamic inversion (Yadav et al, 2018), non-linear model predictive control (Eliasi et al, 2011;Liu et al, 2016;Ejigu and Liu, 2022), sliding mode control (variable structure control) (Ansarifar and Saadatzi, 2015;Wang et al, 2019;Elsisi and Abdelfattah, 2020;Hui and Yuan, 2022b), control based on artificial neural networks (Na et al, 1998a;Lin and Shen, 2000;Khajavi et al, 2002;Boroushaki et al, 2004;Arab-Alibeik and Setayeshi, 2005;Khorramabadi et al, 2008;Naimi et al, 2022b;Hui and Yuan, 2022d) and/or fuzzy logic (Na et al, 1998a;Khorramabadi et al, 2008;Liu et al, 2016;Zeng et al, 2021), deep learning and artificial intelligence (Lee et al, 2020;Ejigu and Liu, 2022;Park et al, 2022), fault-tolerant control (Hui and Yuan, 2022d), control based on models with distribut...…”

Section: Introductionmentioning

confidence: 99%

An overview of power reactor kinetics and control in load-following operation modes

et al. 2023

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Previous work done on reactor kinetics and control in load-following operation modes available in open literature is reviewed. The analysis is focused on, however not limited to pressurized water reactors. Different approximations of the time-dependent neutron transport problem as well as different control algorithms are described in detail and compared. Due to lack of published information the majority of the comparisons was done on qualitative level. In order to facilitate future testing and intercomparisons of models and algorithms, two so-called reference scenarios with time-dependent power demand are defined: a scenario to test the limitations of the load-following capabilities of the nuclear facilities and a second, quasi-realistic scenario.

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“…The PID controller performance depends on the gain values used; hence, to achieve better performance, it is important that the PID gains are tuned in an optimal manner. In recent works, intelligent algorithms have been used for the gain tuning of PID controllers [4], [5]. Linear robust controllers have also been employed for the temperature and power control of NPPs.…”

Section: Introductionmentioning

confidence: 99%

Input-Output Feedback Linearization Control for a PWR Nuclear Power Plant

Naimi

Deng

Sheikh-Akbari

et al. 2022

2022 European Control Conference (ECC)

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This study proposes a feedback linearizationbased control using a dynamic neural network to control a pressurized water-type nuclear power plant. The nonlinear plant model adopted in this study is characterized by five inputs, five outputs and, 38 state variables. The model is linearized through dynamic neural network-based system identification and feedback linearization. The proportional-integral-derivative (PID) controller is subsequently applied to the linearized process. The effectiveness of the proposed approach is demonstrated by simulations on different subsystems of a pressurized water reactor nuclear power plant model. Simulation results show that the proposed strategy offers good performance and is capable of effectively tracking the reference under disturbances.

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Control of a PWR nuclear reactor core power using scheduled PID controller with GA, based on two-point kinetics model and adaptive disturbance rejection system

Cited by 36 publications

References 29 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

An overview of power reactor kinetics and control in load-following operation modes

Input-Output Feedback Linearization Control for a PWR Nuclear Power Plant

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