Steam Generator Water Level Research Articles

Fixed-time fractional-order sliding mode controller with disturbance observer for U-tube steam generator

Water level control of a U-tube steam generator plays a critical and challenging role in ensuring safe and reliable operation for a pressurized-water reactor-type nuclear power plant, as both excessively high and low water level in the U-tube steam generator would affect the normal operation of the nuclear power plant and even lead to unplanned shutdown events. This work presents a fixed-time fractional-order sliding mode water level controller coupled with a fixed-time disturbance observer, aiming to further improve the water level control performance under full operating conditions, considering uncertainties and actuator saturation. The proposed scheme is formulatedbased on the popular Irving water level model with uncertainties and the pre-existing actual water level control framework, while at the same time incorporating the benefits of the state-of-the-art anti-windup techniques, disturbance observer-based feedforward compensation techniques, fractional-order calculus, sliding mode control techniques, and fixed-time stability theorem, such that the transient water level response and steady-state control accuracy can be further enhanced even in the presence of uncertainties and actuator saturation phenomenon. It is theoretically proved that under the proposed scheme, both the estimation error of the uncertainties and the water level control error can be driven to zero within a fixed time regardless of their initial values by Lyapunov’s theorem. Meanwhile, simulation studies, involving the comparisons with a real-world adopted water level controller and a pre-existing integer-order sliding mode water level controller coupled with an uncertainty estimator, reveal the feasibility and superiority of the proposed approach.

Self-Healing Control of Nuclear Power Plants Under False Data Injection Attacks

The transition from analog to digital instrumentation and control (I&C) systems introduces new threats caused by cyberattacks in the nuclear industry. This paper proposes a self-healing strategy to respond to a false data injection attack that targets digital I&C systems, which is a type of cyberattack commonly targeting nuclear power plants with the potential to cause serious physical impacts. This resilience strategy for self-healing control contains three components: (1) an anomaly detection model that can detect false data injection attacks, (2) a device-level control that utilizes inferred values to perform control under a detected false data injection, and (3) a system-level control that leverages another controller that is not under attack to lead the system back to a safe operation state when the device-level control is unavailable. Anomaly detection and device-level control use an autoencoder while system-level control utilizes reinforcement learning. The proposed self-healing resilience strategy is demonstrated with a generic pressurized water reactor (GPWR) simulator under false data injections, targeting the steam generator water level. The results show that the proposed strategy effectively leads the system back to a normal operation state under various false data injection cases.

Coordinated discrete-time super-twisting sliding mode controller coupled with time-delay estimator for PWR-based nuclear steam supply system

The coordination control problem for the core thermal power of the nuclear reactor (NR) and the water level of the nuclear steam generator (SG) is a crucial challenge for real-time model-based control of a pressurized-water reactor (PWR)-based nuclear steam supply system (NSSS). In this paper, a new coordinated discrete-time super-twisting sliding mode controller (CDTSTSMC) coupled with time-delay estimators (TDEs) is proposed, tackling the coordination control problem for the core thermal power of the NR and the water level of the nuclear SG in the presence of unknown dynamics, such as unmeasured system states, model uncertainties, parameter perturbations, and disturbances. First of all, the continuous-time dynamical models of both the NR and the SG in the PWR-based NSSS are described with consideration of unknown dynamics and then are transformed into discrete-time forms using the Euler approximation method. Two TDEs capable of estimating the unknown dynamics existing in the NR and the SG, respectively, are developed, which rely merely on the measurements of the PWR-based NSSS without the upper bounds of the uncertain dynamics being known. After that, a CDTSTSMC strategy, based on the discrete-time dynamical models describing both the NR and the SG, is proposed, allowing the core thermal power to be driven to a desired value while at the same time maintaining the desired water level in the SG with the help of TDEs. It is proved from stability analysis using a Lyapunov approach that under the proposed CDTSTSMC strategy coupled with the TDEs, the stability of the controlled PWR-based NSSS can be ensured in a wide range of load, where the control errors of both the NR and the SG are driven to a neighbor of zero. Finally, the proposed CDTSTSMC strategy coupled with the TDEs is compared with the previous discrete-time optimized proportional–integral–derivative controller (DTOPIDC) and fuzzy weighting controller (FWC). The simulation results reveal that the proposed CDTSTSMC strategy coupled with the TDEs outperforms the DTOPIDC and the FWC, particularly in rejecting unknown dynamics while maintaining both the core thermal power in the NR and the water level in the SG in their desired ranges.

Gain scheduled internal model control based on the dynamic sliding mode method for the water level of nuclear steam generators

Abstract The Steam Generator (SG) is a crucial component of a nuclear power plant. Proper water level control in a nuclear steam generator is of great importance to ensure a sufficient cooling source for the nuclear reactor and to prevent damage to turbine blades. The water level control problem of steam generators has been a leading cause of unexpected shutdowns in nuclear power plants, which must be addressed for plant safety and availability. The control problem is challenging, particularly at low power levels due to shrink and swell phenomena and flow measurement errors. Furthermore, the dynamics of the steam generator vary as the power level changes. Therefore, there is a need to enhance the water level control system of the SG. In this paper, a Gain Scheduled Internal Model Control (IMC) based on the Dynamic Sliding Mode (DSMC) method is developed for the level control problem. The proposed method exhibits the desired dynamic properties throughout the entire output tracking process, independent of perturbations. Simulation results are presented to demonstrate the effectiveness of the proposed controller in terms of performance, robustness, and stability. The simulation results confirm the improvement in transient response achieved by using the proposed controller.

Parameter Optimization of Steam Generator Water Level Control System Based on Piecewise ARX Modeling

Parameter Optimization of Steam Generator Water Level Control System Based on Piecewise ARX Modeling

Visualization and monitoring dynamic water levels of steam generators based on deep learning

In a nuclear power system, the steam generator (SG) is the most important part of the second loop, and accurate monitoring of the internal water level of the SG has always been a challenge. In this study, a two-loop steam generator is mounted on a six-degrees-of-freedom (6-DoF) platform to fully imitate the marine condition of the SG, and the internal water level is visualized by opening a window on the SG. The heating power, period and angle of the 6-DoF platform are considered. By calibrating the relationship between vertical pixels and actual height of the water level, the recognition of water level position in the image is mapped to the actual water level height. 700 water level images were obtained for constructing the dataset. An enhanced deep learning recognition network (YOLO-v7-SE) is proposed to focus on the water level of SG, which is added a channel attention mechanism to the backbone network for enhancing the extraction of target features. Compared with the original YOLO-v7 network, the enhanced YOLO-v7-SE network enhanced the detection accuracy of water level recognition by 12.5% and the mean average accuracy (mAP) by 13.3%. In addition, as the heating power increases, the water level without feeding water drops faster, and that with feeding water keeps constant. The swing periods and angles have complex effects on the water level. In contrast, the traditional magnetic flap level gauge could not obtain the variation of the water level. This shows that the enhanced YOLO-v7-SE network can accuracy recognize the real-time water level change, and achieve fast dynamic response to the water level change, which is a reliable and powerful guarantee for the safety of the SG.

Possibility of misleading readings of water level in VVER steam generator during severe accidents with account for the Fukushima lessons

The severe accident at Fukushima Daiichi in 2011 has emphasized the importance of keeping instrumentation reliable in the course of a severe accident, for successful diagnosis of current state of the reactor and adequate decision making. After the electrical current was partially restored several hours into the accident at Unit 1, the readings of the water level in the core have been for a long time misleading. Given the conditions at the Unit, they were clearly contradicting the analytical estimates of coolability and degradation level of the reactor core. In the post-accident investigations this contradiction was explained by TEPCO by the effect of water evaporation in the reference leg due to heat up of the drywell atmosphere around. The water level instrumentation in different vessels of the VVER NPPs like steam generator, pressurizer is very similar to that at Fukushima BWR. Although the VVER and BWR designs are different in many aspects, it is important to verify if the lessons learnt from the Fukushima accident are applicable in case of severe accidents at VVER.This paper presents the results of numerical study for the possibility of misleading readings of water level in VVER steam generator during hypothetical severe accidents. These results demonstrate the importance of learning the lessons from the accidents that have happened at structurally similar NPPs, and their consideration in safety analyses of NPP units in operation and under design process. Accumulation of lessons learnt from previous operation and emergency experience is a very important advantage of nuclear fission technology that allows enhancing the safety of new designs of nuclear reactors.

Noise resistant steam generator water level reconstruction for nuclear power plant based on deep residual shrinkage network

Nuclear power plant (NPP) steam generator (SG) water level has strong nonlinear pattern and lagging characteristic under control system and manual adjustment. SG water level signal accidental missing and frequent abnormity may lead to incorrect operational intervention or even emergency shutdown. Deep learning SG water level signal reconstruction method trained with historical sensor data can provide supportive information for operators’ decision-making to decrease the financial cost of maintenance and improve the safety of NPP assets. However, the sensor data transferred by electronic devices can be mixed up with noise in transients or complex conditions. This study proposed a noise resistant SG water level signal reconstruction method based on deep residual shrinkage network (DRSN). The dataset in this study is collected from a power-down process collected from a NPP in China. Results from the experiments under different levels of noise are conducted to illustrate the efficacy and robustness of the proposed approaches compared with other deep learning signal reconstruction methods.

Control of small pressurized water reactors under single-loop operation based on MFAPC theory

Small pressurized water reactors can supply power to ships, and single-loop operation is adopted to improve maneuverability under some abnormal conditions. Large key parameter fluctuations and the potential risk of human errors occur in conventional single-loop operation. Therefore, an automatic rapid power reduction method is proposed to improve the control performance of single-loop operation. An automatic rapid power-reduction method based on conventional control systems was tested. Poor power control performance and large fluctuations in steam generator water level were observed. Reactor power and steam generator water level control systems improved by the model-free adaptive predictive control (MFAPC) theory were developed, and the parameters of the MFAPC controller were optimized by the differential evolution method. The simulation results under single-loop operation show that the improved control systems reduce the overshoot of the reactor power and effectively suppress the water level fluctuation of the steam generator.

Coupling of adjoint-based Markov/CCMT predictive analytics with data assimilation for real-time risk scenario forecasting of industrial digital process control systems

Risk early detection and controls are critical to avoid unforeseen accidents or incidents across safety-critical industries especially in a digitalization context. Using the sequential Monte Carlo particle filter method, an adjoint-based Markov/Cell-to-Cell Mapping Technique (Markov/CCMT) predictive analytics coupling with data assimilation is proposed in the paper for dynamic risk and reliability scenario modeling of industrial digital process control systems. The particle filter-based data assimilation method is developed with a sequential importance sampling with resampling scheme for extended system state estimation and dynamic reshaping of system state-space model. Then the Markov/CCMT predictive analytics models are dynamically constructed using an equal-weight quadrature scheme for approximating the probabilistic cell-to-cell mapping relations that reflect the possible system state transitions on the discretized state-space model in real time. The adjoint-based Markov/CCMT modeling for risk scenario forecasting is demonstrated on a digital U-shaped Tube Steam Generator (UTSG) water level control system in nuclear power plants. The demonstration results show that super real-time and consistent predictions can be obtained within the adjoint-based Markov/CCMT predictive analytics framework. Multi-step time-series forecasting towards the identification of system-level degradation states and risk-significant scenarios evolution can also be envisioned when a blended state merging and pruning strategy is adopted in deep model search. The proposed Markov/CCMT predictive analytics is able to deliver proactive insights to intelligent operation and maintenance of complex safety-critical engineering systems.

Development and application of unplanned automatic shutdown fault tree model of nuclear power plant

In order to improve the operation performance of nuclear power plants, this paper introduces a modeling method of unplanned automatic shutdown of nuclear power units based on Fault Tree Analysis, and takes the shutdown signal of steam generator water level low-low as an example, analyzes the development process and application scenarios of unplanned automatic shutdown fault tree in detail. The results show that this method solves the huge and complex problems in the fault tree model of unplanned automatic shutdown in nuclear power units, and can be used to comprehensively and systematically identify the components that affect unit power generation. At the same time, combined with quantitative data, it can monitor the risk of unit power generation, and achieve targeted and efficient improvement of equipment reliability management level and power generation performance level.

Estimation of the time for steam generator trip due to cyber intrusions

The time required to trip a pressurized water reactor (PWR) by inserting malicious signals into its steam generator (SG) control system has been studied using the Generic PWR (GPWR) Simulator. A semi-analytical model is developed to approximately reproduce the simulator response and understand the dynamics of the control unit. A series of two proportional-integral controllers determines control action according to preset constants, the readings from the feedwater level sensor, and those from feedwater and steam flowrate transmitters. It is observed that the most important factor that determines whether a trip will occur is how much additional water is added to or withheld from the SG over time compared to normal operating conditions. In order to determine the effects of control action on the SG, changes in mass inventory are considered. This approach models the SG water level as a function of mass inventory and has a backward temporal memory. A Python interface is developed for the GPWR framework to automatically simulate different spoofing scenarios and post-process the related data. We observe that the trip times predominantly depend on flow mismatch and/or level errors. Controller parameters, including the integral time and gain constants, either speed up or slow down the rate of progression to a trip setpoint but do not cause a trip by themselves. The reactor can trip on a high-level signal when the reading crosses above 78%, increased from its reference level of 57%, or a low-level reading when it is below 25%. The present results show roughly how long the operators would have to respond to an attack, given a specific set of spoofing signals within the issue space analyzed. We have generated a simple surface by fitting a combination of exponential functions to the data obtained from the GPWR Simulator. In general, trips on a low level have been observed to occur faster than those on a high level.

Application of Fuzzy Control in Ocean Nuclear Power Plant Control

In marine nuclear power plants based on molten salt reactors, the complexity of core nuclear reactions, fuel fluidity, and the “false” water level characteristics of the steam generator water level make it unrealistic to establish an accurate mathematical model, so it is difficult to implement traditional PID control methods. This has increased substantially. The fuzzy control has a good solution to this feature. Therefore, combined with the fuzzy control that does not depend on the precise mathematical model of the controlled object, the fuzzy controller of the nuclear power plant is designed, and the control research of the core power is obtained respectively through MATLAB/Simulink simulation. It shows that the designed fuzzy controller can achieve good control of nuclear power plants.

Nuclear power plant sensor signal reconstruction based on deep learning methods

In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.

Full-range steam generator's water level model and analysis method based on cross-calculation

The steam generator is a vertical natural circulation device that produces saturated steam. It is difficult to accurately measure the water level of the steam generator because there are different forms of boiling states and dynamics under different operating conditions. The existing measuring methods for the water level of the steam generator are calibrated under specific operating conditions, and deviations from the calibration conditions may result in large deviations in the measurements. Based on the design and installation characteristics of the existing wide/narrow range water level transmitter, this paper proposes an accurate steam generator water level computational model. The model calculates the water density of full-range steam generator. Moreover, the model can perform the real-time calculations of the steam generator water level under various operating conditions. According to the statistical data of the CPR1000 nuclear power plant, the model and accurate analysis method based on the cross-calculation (A method of comparing and calculating parameters measured by different measurement elements of the same measurement object.) are simple and can provide effective solutions for the water levels of the full-range steam generator.

Robust localized cyber-attack detection for key equipment in nuclear power plants

Most nuclear power plants (NPPs) are looking deploying digital instrumentation and control (I&C) systems, which allow for more precise control and more economical operation. However, both the quantity and capability of industrial control system (ICS)-targeted cyber-attacks have grown dramatically over recent years. Therefore, one of the most significant challenges that digital I&C systems bring is the issue of cybersecurity, which should be enhanced before their deployment.Several different types of cyber-attacks can be introduced to NPPs; a false data injection attack on key equipment is the focus of this research due to the potential severe consequences associated with such an attack. In false data injection, the attackers may alter the reading of control sensors or commands to change the operation of an NPP. Current cybersecurity efforts focus on intrusion prevention by firewalls or data-flow direction control and use commercial intrusion detection systems, which usually focus on monitoring Internet Protocol (IP) addresses, ports, and payload length. However, attention should be given to conditions where these approaches can fail, such as an insider attack. Previous research based on process data shows the potential of a last defenseâ, line using online monitoring of the process data in concern with cyber data analysis. However, existing models involve different subsystems across the whole NPP, which has a wide attack surface and may require high computing cost. This holistic approach may not meet the time-sensitive requirements imposed upon I&C systems. This paper proposes a localized kit for key equipment in a process as a complementary detection method to improve the robustness of key equipment under cyber-attacks. Compared to existing models, this reduces the number of variables used in the model and significantly improves the computational speed. It also reduces the attack surface by limiting the data acquisition locally. This localized kit includes a cyber-attack detection model to detect anomalies within key components, such as the control system actuator, and an inference model to potentially reconstruct a compromised signal to allow the safe shut down.To develop and demonstrate the localized cybersecurity kit, a hardware-in-the-loop (HIL) testbed was built with a pressurized water reactor (PWR) simulator and a programmable logical controller (PLC). The PLC was programmed to control the steam generator (SG) water level at a specified set point, and the PWR simulator was utilized to simulate the nuclear system and response for parameters outside of the SG. Three false data injection attacks were conducted towards the testbed to generate the data needed for the localized kit development and evaluation. The results show the cyber-attack detection model is effective under false data injection scenarios and the inference model is promising as a signal reconstruction method.

Fault Tolerant Control of a Nuclear Steam Generator in the presence of Sensor Biases

Steam Generator (SG) is one of the crucial components in a nuclear power plant where nuclear reactors like pressurized water reactors (PWR) and pressurized heavy water reactors (PHWR) are used. For safe operation of SG in power plants, the control of water level becomes very crucial. Poor control of the water level in SG can lead to frequent reactor shutdowns. The occurrence of biases in the measurement of level and pressure sensors used in the SG control system can result in significant degradation of the closed-loop performance. This work aims to introduce a fault tolerant control framework to arrest the performance degradation of conventional controllers in the presence of sensor bias. Initially, the bias present in the measurements is modeled as a time varying parameter and estimated using a suitably modified version of RNK based state and parameter estimator approach. Using the sensor bias estimates, the measurements are corrected and further used in the controller. The effectiveness of the proposed framework of FTC in the presence of sensor bias is demonstrated by carrying out simulation studies on a benchmark SG system. Analysis of simulation results reveals that the proposed FTC framework gives similar results to that of fault free case.

SUBSTANTIATION OF MODERNIZED BLACKOUT & LOSS-OF-COOLANT ACCIDENT MANAGEMENT STRATEGY AT NUCLEAR POWER PLANTS WITH WWER

The analysis of the known results of RELAP5/V.3.2 simulation for loss of coolant & blackout accidents at WWER nuclear power plants showed that the design accident management strategies with design passive safety systems do not provide the necessary safety conditions for the maximum permissible temperature of fuel claddings, the minimum permissible level of coolant in the reactor and feed water in the steam generators. A conservative thermohydrodynamic model for a design and modernized blackout & loss-of-coolant accident management strategy at a nuclear power plant with WWER has been developed. Design passive safety systems carry out the design accident management strategy: pressurizer safety valves, secondary steam relief valves, and hydraulic reservoirs of the emergency core cooling system of the reactor. Promising afterheat removal passive systems and the reactor level and steam generator water level control systems carry out the modernized blackout & loss-of-coolant accident management strategy. The main conservative assumptions of the presented model of blackout & loss-of-coolant accidents: complete long-term failure of all electric pumps of active safety systems, the temperature of nuclear fuel in the central part of the fuel matrix is assumed as the maximum allowable one, effect of “run down” flow of a turbine feed pump and the coolant level in pressurizer on accident process is not considered. Computational modelling has found that violations of the safety conditions are over the entire range of leak sizes for the design blackout & loss-of-coolant accident management strategy. For the modernized blackout & loss-of-coolant accident management strategy, safety conditions are provided for 72 hours of the accident and more. The presented results of computational modelling of blackout accident management strategies for nuclear power plants can be used to modernize and improve symptom-informed emergency instructions and guidelines for the severe accident management at nuclear power plants with WWER. Application of the results of computational modelling of blackout accident management strategies is generally not substantiated for other types of reactor facilities. In this case, it is necessary to develop calculated models for blackout accident management taking into account the specifics of the structural and technical characteristics and operating conditions for safety related systems of nuclear power plants.

Management of loss of offsite power avoiding reactor trip

A loss of offsite power event in a nuclear power plant resulting from the loss of connections to the electrical grid can be effectively managed without resulting in an unscheduled reactor trip. We have developed effective procedures for managing such house load operations that will maintain a stable steam generator water level using the auxiliary feedwater system. The procedures are based on a first-principle energy balance with the heat flux from the primary to the secondary side represented through an average primary system temperature. The reduced order model has been validated through actual simulations of steam generator dynamics with the MARS-KS code. With the help of the reduced order model, various scenarios or tracks involving judicious combinations of reactor power, reactor coolant flowrate and steam generator feedwater flowrate that could support stable house load operations are simulated and summarized as an operating procedure in a multi-path event tree structure.

Investigation on reverse flow phenomenon in UTSGs with abnormal secondary side water level under single-phase natural circulation

The effect of secondary side water level of steam generator (SG) on the single-phase reverse flow in inverted U-tube steam generator (UTSG) is analyzed based on the one-dimension flow model, applying the small perturbation theory. The pressure drop-mass flow rate curve of inverted U-tube in SG with abnormal secondary side water level is obtained according to the flow model. There is a negative slope on the curve where the single-phase reverse flow can occur. The characteristic mass flow rate and characteristic pressure drop of different secondary side water level of UTSG are compared, and the results show that the single-phase reverse flow will occur more easily when the secondary side water level of UTSG falls. Meanwhile, for the smaller UTSG, the secondary side water level of UTSG can influence the space distribution of the single-phase reverse flow, for the larger UTSG, the single-phase reverse flow always occurs firstly in the longest inverted U-tube. Furthermore, the analytical results are validated by the simulation results of RELAP5/MOD3.2 based on the experiment.