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Nuclear Power Instrumentation and Control
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Nuclear Power Instrumentation and Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 22403

Special Issue Editors

Dr. Zhe Dong

E-Mail Website
Guest Editor
Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Beijing 100084, China
Interests: nuclear power instrumentation, control and operation
Dr. Xiaojin Huang

E-Mail Website
Guest Editor
Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Beijing 100084, China
Interests: nuclear power instrumentation, control and operation
Dr. Xinyu Wei

E-Mail Website
Guest Editor
School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: nuclear power dynamics, control, optimization and simulation

Special Issue Information

Dear Colleagues,

Nuclear power plants are clean, baseload power sources. While intermittent renewable energies, such as wind and solar energy, are strongly dependent on geographical location, climatic conditions and a large land footprint, nuclear plants are able to provide a consistent, clean power supply with a small land footprint. Nuclear power practices have been adapted over the years to balance the supply and demand of power, and are able to increase nuclear renewability, offering significant support for the realization of worldwide carbon neutrality. Instrumentation and control (I&C) is a key technology that is used to enhance the stability, reliability and efficiency of nuclear plants by providing advanced functions, such as plant coordinated control, smart measurement, reliable protection, control optimization, as well as sufficient monitoring, diagnosis and prognosis. By developing advanced I&C techniques, the operating cost of nuclear power plants can be further reduced, which is significant for strengthening the economic competitiveness of nuclear power. Nowadays, due to the fast development of small modular reactors (SMR) and artificial intelligence, there will be a great expansion of study in the field of nuclear power I&C technology. The topics of this Special Issue on “Nuclear Power Instrumentation and Control” include, but are not limited to:

  • Advanced control of nuclear power reactors;
  • Coordinated control of nuclear power/cogeneration plants;
  • Control optimization based on model-predictive control (MPC), reinforcement learning, extremum seeking, etc.;
  • Process monitoring and online reliability assessment;
  • Residual life estimation for instrumentation and control equipment;
  • Anomaly diagnosis and prognostics
  • Dynamical modeling for controlling the design and operation analysis;
  • Reliability enhancement of nuclear power digital I&C systems.

Dr. Zhe Dong
Dr. Xiaojin Huang
Dr. Xinyu Wei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nuclear plant
  • instrumentation
  • control
  • operation
  • optimization
  • monitoring
  • diagnosis
  • prognosis
  • reliability

Published Papers (6 papers)

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Research

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13 pages, 4644 KiB  
Article
A Comprehensive Situation Awareness Measurement Method for Analyzing the Operators’ Situation Awareness of Multi-Module High Temperature Gas-Cooled Reactor Plants
by Runfa Miao, Qianqian Jia, Duo Li and Zhe Dong
Energies 2023, 16(15), 5601; https://0-doi-org.brum.beds.ac.uk/10.3390/en16155601 - 25 Jul 2023
Viewed by 732
Abstract
For multi-modular nuclear power plants under the scheme of multiple nuclear steam supply system (NSSS) modules driving a single steam turbine, the NSSS modules are coupled tightly with each other by the common turbine, giving more complex normal operation with respect to the [...] Read more.
For multi-modular nuclear power plants under the scheme of multiple nuclear steam supply system (NSSS) modules driving a single steam turbine, the NSSS modules are coupled tightly with each other by the common turbine, giving more complex normal operation with respect to the single-modular plants. To limit the operation cost of multi-modular plants, one operator is assigned to monitor and control two or more modules, whose feasibility should be verified. Combined with the characteristics of multi-module control rooms and multi-module running tasks, this paper designs a comprehensive situation awareness measurement method that combines SART, NASA-TLX (NASA Task Load Index), and eye movement tracking methods. The SART, NASA-TLX, and gaze entropy are adopted to measure the operators’ SA, and a series of accident handling experiments are performed on a full-scale simulator to gain enough data for analysis. The operators’ eye trajectories on the human–machine interface (HMI) during the experiments are all recorded for calculating the gaze entropy. Both the SART and NASA-TLX scales are filled by the operators after finishing the experiments. The experiment results show that the difference in operators’ workload and SA amongst all the experimental scenarios is limited, even between the toughest and tenderest scenarios, indicating the feasibility of one operator driving two NSSS modules simultaneously. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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19 pages, 6065 KiB  
Article
Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder
by Alexandra Akins, Derek Kultgen and Alexander Heifetz
Energies 2023, 16(13), 4965; https://0-doi-org.brum.beds.ac.uk/10.3390/en16134965 - 26 Jun 2023
Cited by 1 | Viewed by 15446
Abstract
Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold [...] Read more.
Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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25 pages, 4909 KiB  
Article
Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design
by Yao Tong, Duo Zhang, Zhijiang Shao and Xiaojin Huang
Energies 2023, 16(12), 4653; https://0-doi-org.brum.beds.ac.uk/10.3390/en16124653 - 12 Jun 2023
Viewed by 833
Abstract
The world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) nuclear power plant adopts an innovative reactor type and a modular structure design. Parameter estimation and model calibration are of great significance prior to the implementation of model-based control and optimization. This paper focuses [...] Read more.
The world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) nuclear power plant adopts an innovative reactor type and a modular structure design. Parameter estimation and model calibration are of great significance prior to the implementation of model-based control and optimization. This paper focuses on identifying the thermal hydraulic parameters of HTR-PM over the global operating domain. The process technology and model mechanism of HTR-PM are reviewed. A parameter submodel named global parameter mapping is presented to quantify the relationship between an unknown model parameter and different operating conditions in a data-driven manner. The ideal construction of such a mapping requires reliable estimates, a well-poised sample set and an appropriate global surrogate. An adaptive model calibration scheme is designed to tackle these three issues correspondingly. First, a systematic parameter estimation approach is developed to ensure reliable estimates via heuristic subset selection consisting of estimability analysis and reliability evaluation. To capture the parameter behavior among the multiple experimental conditions and meanwhile reduce the operating cost, an adaptive experimental design is employed to guide condition testing. Experimental conditions are sequentially determined by comprehensively considering the criteria of sampling density, local nonlinearity and parameter uncertainty. Support vector regression is introduced as the global surrogate due to its capability of small-sample learning. Finally, the effectiveness of the model calibration scheme and its application performance in HTR-PM are validated by the simulation results. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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20 pages, 8089 KiB  
Article
Study on IMC-PID Control of Once-Through Steam Generator for Small Fast Reactor
by Kai Xiao, Yiliang Li, Pengcheng Yang, Ying Zhang, Yang Zhao and Xiaofei Pu
Energies 2022, 15(20), 7475; https://0-doi-org.brum.beds.ac.uk/10.3390/en15207475 - 11 Oct 2022
Viewed by 898
Abstract
The simplification of simulation inevitably leads to model mismatch. In this paper, a once-through steam generator (OTSG) for a small lead bismuth fast reactor (SLBFR) is established and verified, and the OTSG model is simplified by three different methods. Based on the simplified [...] Read more.
The simplification of simulation inevitably leads to model mismatch. In this paper, a once-through steam generator (OTSG) for a small lead bismuth fast reactor (SLBFR) is established and verified, and the OTSG model is simplified by three different methods. Based on the simplified OTSG model, IMC and IMC-PID controllers are designed to verify the sensitivity of the controller to model mismatch. The results show that the sensitivity of the controller to model mismatch is related to the filter parameters. With the increase in λ, the IMC-PID controller becomes insensitive to model mismatch caused by model linearization, non-minimum phase characteristics, noise and time delay. However, the adaptability to model mismatch sacrifices the sensitivity of the system. When λ is too large, the inertia of the controller is too large, resulting in the deterioration of the fast power regulation. Through the research of this paper, the time domain response approximation method is recommended for OTSG model simplification, and λ is recommended to be between 5 and 10 for feedwater IMC-PID controller. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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15 pages, 2374 KiB  
Article
A Finite-Time Differentiator with Application to Nuclear Reactor Inverse Period Measurement
by Yunlong Zhu, Zhe Dong, Duo Li, Xiaojin Huang, Yujie Dong, Yajun Zhang and Zuoyi Zhang
Energies 2022, 15(12), 4487; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124487 - 20 Jun 2022
Cited by 1 | Viewed by 1165
Abstract
The measurement of the growth rate, or the so-called inverse period, of a nuclear reactor is crucial for safety monitoring and control purposes. Due to the inevitable statistical fluctuation of neutron flux at low power-levels, it is difficult to precisely estimate the inverse [...] Read more.
The measurement of the growth rate, or the so-called inverse period, of a nuclear reactor is crucial for safety monitoring and control purposes. Due to the inevitable statistical fluctuation of neutron flux at low power-levels, it is difficult to precisely estimate the inverse period from the pulse counting data in the source range. Motivated by the equivalence of the measurement of inverse period and the differentiation of the logarithm of pulse count, a new differentiator is proposed, which is finite-time convergent with a bounded steady estimation error. The feasibility of this newly-built finite-time differentiator is verified by numerical simulation. Then, based on the pulse count data recorded during the startup of a test reactor, the differentiator is used to estimate the inverse period and its derivative, as well as the period and the reactivity of the reactor. The results show that the differentiator is capable of providing a satisfactory estimation of signal derivatives under strong noise. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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Review

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19 pages, 1119 KiB  
Review
Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control
by Zhe Dong, Zhonghua Cheng, Yunlong Zhu, Xiaojin Huang, Yujie Dong and Zuoyi Zhang
Energies 2023, 16(3), 1443; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031443 - 01 Feb 2023
Cited by 4 | Viewed by 2070
Abstract
Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of [...] Read more.
Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plants, learning-based modeling and state-observers are typical hot topics. In addition, from the directions of intelligent control, nonlinear control, online control optimization and multimodular coordinated control, the advanced results in nuclear plant control methods are introduced, where the hot topics include fuzzy logic inference, neural-network control, reinforcement learning, sliding mode, feedback linearization, passivation and decoupling. Based upon the review of recent progress, the future directions in nuclear plant modeling and control are finally given. Full article
(This article belongs to the Special Issue Nuclear Power Instrumentation and Control)
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