Nuclear Power Plant Control Systems Research Articles

Digitalization of Pulse Signal Processing for Ex-Core Instrumentation System in Nuclear Power Plants

Nuclear power plants (NPPs) are globally important sources of clean energy. However, high operation and maintenance (O&M) costs are one of the factors hindering the development of NPPs. In this study, we focused on the development of a digital algorithm for ex-core instrumentation systems (EISs) to reduce O&M costs, given that the digitalization of EISs is not progressing compared with other instrumentation and control systems in NPPs. Specifically, we developed a digital algorithm for pulse signal processing in EISs, which traditionally require a significant amount of hardware and maintenance efforts. Our purposes were to simplify the configuration and reduce the O&M costs associated with pulse signal processing. To validate the algorithm, we used a detector emulator and a research reactor. Additionally, we explored a new approach to reduce the workload associated with a discrimination characteristics test (DCT) in EISs. The results of this study demonstrated that the proposed algorithm is effective in achieving good linearity within ±5% of the span and response performance with a delay time of 5 ms, which are required for EISs. Furthermore, the proposed method for the DCT shows promising results in reducing the time required for the test compared with conventional methods.

Research on anomaly detection method of nuclear power plant operation state based on unsupervised deep generative model

In the field of traditional industrial control, anomaly detection method is mainly used to identify data items that do not match the normal operation state of the system. The traditional machine learning algorithm needs the transient operation data of normal and accident conditions to identify the abnormal state of nuclear power plant. The transient operation data of nuclear power plant during normal condition is sufficient, but it is lacks of transient operation data in accident conditions. To solve the above problems, an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model is established by using Variational Auto Encoders (VAE) and Isolation Forest (iForest) in this paper. The biggest advantage of this method is that it can only make use of the normal operation data of the nuclear power plant to make the nuclear power plant control system effectively identify whether the current state of nuclear power plant is normal operation or in accident condition. In the unsupervised deep generative model, VAE is used for data preprocessing, and iForest is used to identify abnormal operation data. Then, the method is verified in seven variable and accident conditions, such as power reduction condition, steam generator tube rupture accident and so on. The verification results show that the anomaly detection method can recognize the current abnormal condition immediately when the accident happens. And the time consumed to identify a group of operation parameters corresponding to the operation state of the nuclear power plant is about 3 ms, which can satisfy the real-time requirements of the control system. Therefore, the anomaly detection method based on unsupervised deep generative model can distinguish the normal or abnormal operation state of the nuclear power plant in real time and effectively, and provide judgment basis for accident classification and subsequent rescue.

Reliability analysis and safety model checking of Safety-Critical and control Systems: A case study of NPP control system

Non-functional requirements (NFR) play a crucial role in designing safety–critical and control systems. The failure of such systems leads to substantial financial loss, threats to human life, and adverse impact on the environment. Therefore, we develop a framework using a state-space model to quantify reliability in the design phase of the development life cycle to minimize the losses due to the system's failure after installation. State-space modeling is a powerful technique to develop such a system for behavioral and structural analysis. Since, a safety–critical and control system may comprises with hardware, software, and sometimes human aspects involved to operate it. Therefore, when we model such complex system using Petri Net then 'the state explosion problem arises. So, to get rid of this problem, we use an algorithmic-based reduction approach to reduce the states of the modeled system and optimize the state-space model. Further, we verify the safety properties of the system during the design phase of the development life cycle that gives confidence to the clients for the use of the system. In this work, a digital feedwatercontrolsystem of a nuclear power plant (NPP) is taken as control system to illustrate our methodology.

Power Level Control of Nuclear Power Plant Based on Asymptotical State Observer under Neutron Sensor Fault

Power level control is one of the critical functions in the instrument and control system of nuclear power plants (NPPs). In most power level control systems of NPPs, the power level or average neutron flux in reactor cores provided by out-of-core neutron sensors are usually measured as feedback of power control systems, while, as critical measuring devices, there is a risk of damage to out-of-core neutron sensors. For improving the operation reliability of NPPs under the neutron sensors’ failure, a power control system based on power observer is developed in this work. The simulation based on NPP simulator shows the power control system based on the observer is effective when neutron sensors fail.

A review on the research status of reliability analysis of the digital instrument and control system in NPPs

The widely use of digital instrument and control (I&C) systems in nuclear power plants (NPPs) and their important role in plant safety put forward the requirement for developing reliability modeling for digital systems with the capacity to be integrated into the existing probabilistic safety analysis (PSA) framework. However, challenges arise in the reliability analysis which mainly come from the modeling of complex interactions among the system hardware, software, controlled process physics, and operator. Generally, these factors are analyzed separately since at present there isn’t universal methods for modeling them in a uniform framework. In this paper, the research status of reliability modeling methodologies, software reliability assessment methods and human reliability analysis for digital instrument and control systems in NPPs were introduced. The survey results show that ET/FT is still the mainstream method in reliability studies of digital systems in NPPs, and DFM is a dynamic method with great potential. At present, there is no generally accepted software reliability analysis method. The researches about human errors under digital environment and the theoretical basis of corresponding human reliability analysis method is being developed.

CPN simulation-based test case generation from controlled natural-language requirements

We propose a test generation strategy from natural language (NL) requirements via translation into Coloured Petri Nets (CPN), an extension of Petri Nets that supports model structuring and provides a mature theory and powerful tool support. This approach extends our previous work on the NAT2TEST framework, which involves syntactic and semantic analyses of NL requirements and the generation of Data-Flow Reactive Systems (DFRS) as an intermediate representation, from which target formal models can be obtained for the purpose of test case generation. Our contributions include automating a systematic translation of DFRSs into CPN models, an extension to deal with time aspects, besides an empirical analysis of the CPN-based test generation strategy. The analyses considered examples both from the literature (a vending machine and a nuclear power plant control system), and from the aerospace and the automotive domain (a priority command control system and a turn indicator control system, respectively). We analysed performance and the ability to detect defects generated via mutation. The results provide evidence that the contribution proposed here is more efficient, besides being able to detect at least as many defects as our previous efforts. • Test case generation for timed reactive systems from natural-language requirements. • Coloured Petri Nets (CPN) are used as a hidden formalism in the generation process. • Generation of CPN models is systematised with the aid of the Spoofax framework. • Test generation is performed via simulation of CPN models. • The strategy is evaluated (performance and test strength) using four applications.

Implication of LOCA characteristics of large PWR and SMR for future development of intelligent nuclear power plant control system

Operation with reduced number of operators is considered as an important operational mode to improve economics of a nuclear power plant (NPP) in the future and it will be more important for the small modular reactors (SMRs). To control NPPs efficiently and safely with a reduced number of operators, an intelligent operation system can be very useful. However, reduced number of operators will result in larger consequence in human error due to simultaneous multi-unit failures are now more probable than the conventional operational mode. Thus, to decrease the probability of human error occurrence for the reduced number of operators, the development of an autonomous operation system with certain level of intelligence is inevitable. The prediction capability of system’s behavior as well as understanding the root cause for the current situation should be an essential function of the designed autonomous operation system. The authors propose to use the current system thermal–hydraulic (STH) analysis code platform to develop such intelligence. As a demonstration of the suggested method, the authors used the STH code to track the accident initiator of both large pressurized water reactor (PWR) and SMR. The discussions on how this method can be useful for future NPP operation and how generic differences in system design actually influence the system behavior are presented.

Measuring Situation Awareness of Operating Team in Different Main Control Room Environments of Nuclear Power Plants

Abstract Environments in nuclear power plants (NPPs) are changing as the design of instrumentation and control systems for NPPs is rapidly moving toward fully digital instrumentation and control, and modern computer techniques are gradually introduced into main control rooms (MCRs). Within the context of these environmental changes, the level of performance of operators in a digital MCR is a major concern. Situation awareness (SA), which is used within human factors research to explain to what extent operators of safety-critical systems know what is transpiring in the system and the environment, is considered a prerequisite factor to guarantee the safe operation of NPPs. However, the safe operation of NPPs can be guaranteed through a team effort. In this regard, the operating team's SA in a conventional and digital MCR should be measured in order to assess whether the new design features implemented in a digital MCR affect this parameter. This paper explains the team SA measurement method used in this study and the results of applying this measurement method to operating teams in different MCR environments. The paper also discusses several empirical lessons learned from the results.

Development of the ALFRED reactor full power mode control system

In this paper, the full power mode control scheme for the pool-type Advanced Lead Fast Reactor European Demonstrator (ALFRED) is presented. The design process of a nuclear power plant control system is a multi-step process, whose final result is the implementation of the feedback controllers. In particular, the control system configuration has been finalized after having considered different strategies to govern the variables of interest (e.g., power produced in the core, coolant temperature, pressure, etc.). In a previous work (Ponciroli et al., 2014b), the most relevant issues regarding both the controlled outputs (e.g., the lead temperature in the cold leg) and the control inputs (e.g., the feedwater inlet temperature and the lead mass flow rate) have been allowed for. Based on these preliminary analyses, the control strategy has been further developed, and the full power mode control scheme for the ALFRED reactor has been implemented. As a major outcome, it has been demonstrated how a decentralized control scheme based on a battery of PI controllers allows obtaining satisfactory performance. Indeed, starting from the linearization of the dedicated object-oriented model, the level of decoupling among the control loops has been verified, confirming the validity of the proposed solution based on the outcomes of the Relative Gain Array (RGA) method. This result has been achieved thanks to a proper tuning of the controller parameters, which allows damping the undesired influence due to the other control loops. Finally, as for the design of the actuators, a quantitative approach based on the outcomes of the system linear stability analysis has been proposed, in order to define the maximum safety value for the extraction speed of the control rods.

The feedwater control system and steam dump control system responses during large-load reduction transient for Maanshan PWR Plant

In this study, the development of TRACE (TRAC/RELAP Advanced Computational Engine) models for Maanshan nuclear power plant (NPP) important control systems, such as feedwater control system and steam dump control system, are performed in using SNAP (Symbolic Nuclear Analysis Program) / TRACE. The large-load reduction transient analysis of the Maanshan NPP control system TRACE models are also performed and the responses of the control systems of TRACE models compare with Maanshan NPP startup tests data to verify their accuracy. Analysis results of TRACE indicate that the responses of the Maanshan NPP control system TRACE models are consistent with the plant data for large-load reduction transient.

The Feedwater Control System and Steam Dump Control System Responses During Large-Load Reduction Transient for Maanshan PWR Plant

In this study, the development of TRACE (TRAC/RELAP Advanced Computational Engine) models for Maanshan nuclear power plant (NPP) important control systems, such as feedwater control system and steam dump control system, are performed in using SNAP (Symbolic Nuclear Analysis Program) / TRACE. The large-load reduction transient analysis of the Maanshan NPP control system TRACE models are also performed and the responses of the control systems of TRACE models compare with Maanshan NPP startup tests data to verify their accuracy. Analysis results of TRACE indicate that the responses of the Maanshan NPP control system TRACE models are consistent with the plant data for large-load reduction transient.

Micro incident analysis framework to assess safety and resilience in the operation of safe critical systems: A case study in a nuclear power plant

Safety in modern organizations, comprised of many nested levels with different types of coupling between them, must be managed by a control structure embedded in this adaptive sociotechnical system. The resilience of sociotechnical critical systems still relies on human ability to handle unexpected events adequately. In this study, based on a schematic view of a nuclear power plant control system composed of three structural system layers, planning, operation, and hardware, we present a framework to analyze micro incidents during nuclear power plant operation. The analysis show operators’ control actions used to solve small conflicts that arose at the operational layer, and how they marshaled the resources required for their action/cognition, i.e. the material, social, and cultural characteristics of the environment. The micro incident framework enables an anticipated view of operators’ control actions, providing processes for systemic analysis and critical thinking about the possibility that relatively small problematic situations in the loosely coupled system layers may lead to negative outcomes at some future time.

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (PEC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances or uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and nuclear power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrades.

Natural language interface for fault diagnosis system of nuclear power plant control systems.

A fault diagnosis system was developed to improve the availability and maintainability of control systems in nuclear power plants. To facilitate man-machine communications in the system a natural language interface was introduced. Features of the interface include two-step analysis of the meaning of input sentences, identification of the kind of input content using sentence patterns, and retrieval of the information required. For the information retrieval, when information is lacking supplementation is done by referring to the dialog history and knowledge of the control system composition. Dialog tests, including the required information inputs for diagnosis and questions about the validity of the diagnosis results, were performed by the control system simulator for an assumed component failure, such as a printed circuit board failure. Dialog inputs were made by voice and outputs were by voice or a CRT display. The tests confirmed that inputs required in the diagnosis process can be entered by voice witho...

The Development of a Programmable Controller System for use in Ontario Hydro Nuclear Power Plants

Ontario Hydro has successfully used telephone type relays in its CANDU nuclear power plant control systems. An alternative type of hardware using microprocessor technology has been developed possessing some advanced control features. Prototype systems have been built and have passed preliminary tests.