Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Model-Based Diagnostic Frameworks for Fault Detection and System Monitoring in Nuclear Engineering Systems

The high operations and maintenance (O&M) cost for nuclear plants is one of the most significant challenges facing the industry today. The research in this thesis is motivated by the ongoing effort to utilize automation and improved operator support technologies to reduce O&M costs in nuclear power plants. A diagnostic framework is first developed for the problem of monitoring equipment health and sensor calibration status in nuclear engineering systems. This is achieved by utilizing real-time data from sensors that are already in place for system monitoring to perform automated diagnostics of equipment degradation. Given the long-time scale over which component degradation typically proceeds, some of the sensors may also inevitably degrade and become unreliable. The need to simultaneously consider equipment and instrument faults is both a technical necessity and a desired capability. The automation of these monitoring tasks contributes to the reduction of the overall O&M cost by reducing the required human resources and by providing better maintenance scheduling. Early detection of slow degradation over the course of plant operation requires sufficient detection sensitivity from the diagnostic framework. The problem is more complicated in the presence of various sources of uncertainty and possible changes of operating conditions due to plant drifts. To resolve these difficulties and provide the desired capability, the proposed framework is a hybrid integration of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. Physics-based models are developed to describe the fault-free behavior of system components. Quantitative residuals are generated from the analytical redundancy in each model and serve as fault symptoms for model-based diagnosis. Statistical change detection methods are used to detect changes in the residuals in the presence of uncertainty. Measurement and modelling uncertainty are robustly treated by methods of statistical change detection and probabilistic reasoning. A system level diagnosis framework is proposed to deal with the lack of local sensors to each component. The overall framework has been implemented and demonstrated with a high-pressure feedwater system whose available sensor set is insufficient for the construction of standalone models for most major components. Results from the demonstration showed that the system level approach can be used to construct models and perform diagnostics for systems with limited instrumentation. Both component faults and sensor faults can be detected, and the effects of uncertainty can be mitigated by the proposed probabilistic reasoning framework. Areas for future work were identified and include the investigation of a dynamic Bayesian network to treat the effects of uncertainty in the diagnosis as well as the investigation of using high fidelity simulation codes to construct simulation-based surrogate models of the basic plant components.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155193/1/nghiant_1.pd

Inverse Dynamics and Control for Nuclear Power Plants

A new nonlinear control technique was developed by reformulating one of the “inverse Problems” techniques in mathematics, namely the reconstruction problem. The theory identifies an important concept called inverse dynamics which is always a known property for systems already developed or designed. Accordingly, the paradigm is called “reconstructive inverse dynamics” (RID) control. The standard state-space representation of dynamic systems constitutes a sufficient foundation to derive an algebraic RID control law that provides solutions in one step computation. The existence of an inverse solution is guaranteed for a limited dynamic space. Outside the guaranteed range, existence depends on the nature of the system under consideration. Derivations include adaptive features to minimize the effects of modeling errors and measurement degradation on control performance. A comparative study is included to illustrate the relationship between the RID control and optimal control strategies. A set of performance factors were used to investigate the robustness against various uncertainties and the suitability for digital implementation in large scale-systems. All of the illustrations are based on computer simulations using nonlinear models. The simulation results indicate a significant improvement in robust control strategies. The control strategy can be implemented on-line by exploiting its algebraic design property. Three applications to nuclear reactor systems are presented. The objective is to investigate the merit of the RID control technique to improve nuclear reactor operations and increase plant availability. The first two applications include xenon induced power oscillations and feed water control in conventional light water reactors. The third application consists of an automatic control system design for the startup of the Experimental Breeder Reactor-II (EBR-II). The nonlinear dynamic models used in this analysis were previously validated against available plant data. The simulation results show that the RID technique has the potential to improve reactor control strategies significantly. Some of the observations include accurate xenon control, and rapid feed water maneuvers in pressurized water reactors, and successful automated startup of the EBR-II. The scope of the inverse dynamics approach is extended to incorporate artificial intelligence methods within a systematic strategy design procedure. Since the RID control law includes the dynamics of the system, its implementation may influence plant component and measurement design. The inverse dynamics concept is further studied in conjunction with artificial neural networks and expert systems to develop practical control tools

The development of boiler control models for the optimization of boiler efficiency

With Eskom’s fleet rapidly reaching end of life and maintenance outages becoming more frequent, it has become more critical to be able to determine transient effects of auxiliary losses and system responses due to instabilities. A low mono-nitrogen oxide (low-NOx) burner replacement project has recently been completed at Camden power station in Ermelo. It has thus deemed itself as a perfect candidate for a model which can be used to understand the new systems’ response during transient scenarios. The aim is to develop a boiler control model to be used for simulation of various process conditions and failure scenarios in order to predict the boiler plants’ behaviour and improve its availability. Research was done on common boiler control practices and modelling of boiler control. A theoretical boiler control model was developed based on the Camden power station’s control system specification. The computational model of the boiler control was implemented in Flownex® simulation environment, which was found to be particular useful for modelling industrial applications. A number of simulations with the computational model were performed and the results were compared against the historic plant data showing good correlation. In parallel, a thermo-fluid model of the boiler was developed using Flownex® by a Masters student at the University of Cape Town, which was then integrated with the control model. The combined Flownex model was used for simulation of the following important cases: a mill trip, a Forced Draught fan trip and load changes. The obtained results show good correlation with the real plant data, indicating that the developed computational model can be considered accurate for Camden’s particular type of boiler and its control. Hence, it is envisaged that the developed combined Flownex model can be applied for simulation of the boilers of the Camden power station

Incipient fault detection and isolation of sensors and field devices

The purpose of this research is to develop a robust fault detection and isolation method, for detecting faults in process sensors, actuators, controllers and other field devices. The approach to the solution to this problem is summarized below. A novel approach for the validation of control system components and sensors was developed in this research. The process is composed of detecting a system anomaly, isolating the faulty component (such as sensors, actuators, and controllers), computing its deviation from expected value for a given system\u27s normal condition, and finally reconstructing its output when applicable. A variant of the Group Method of Data Handling (GMDH) was developed in this research for generating analytical redundancy from relationships among different system components. A rational function approximation was used for the data-driven modeling scheme. This analytical redundancy is necessary for detecting system anomalies and isolating faulty components. A rule-base expert system was developed in order to isolate the faulty component. The rule-based was established from model-simulated data. A fuzzy-logic estimator was implemented to compute the magnitude of the loop component fault so that the operator or the controller might take corrective actions. This latter engine allows the system to be operated in a normal condition until the next scheduled shutdown, even if a critical component were detected as degrading. The effectiveness of the method developed in this research was demonstrated through simulation and by implementation to an experimental control loop. The test loop consisted of a level control system, flow, pressure, level and temperature measuring sensors, motor-operated valves, and a pump. Commonly observed device faults were imposed in different system components such as pressure transmitters, pumps, and motor-operated valves. This research has resulted in a framework for system component failure detection and isolation, allowing easy implementation of this method in any process control system (power plants, chemical industry, and other manufacturing industry). The technique would also aid the plant personnel in defining the minimal number of sensors to be installed in a process system, necessary for reliable component validation

Electrical power prediction through a combination of multilayer perceptron with water cycle ant lion and satin bowerbird searching optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented

Multi-Modular Integral Pressurized Water Reactor Control and Operational Reconfiguration for a Flow Control Loop

This dissertation focused on the IRIS design since this will likely be one of the designs of choice for future deployment in the U.S and developing countries. With a net 335 MWe output IRIS novel design falls in the “medium” size category and it is a potential candidate for the so called modular reactors, which may be appropriate for base load electricity generation, especially in regions with smaller electricity grids, but especially well suited for more specialized non-electrical energy applications such as district heating and process steam for desalination. The first objective of this dissertation is to evaluate and quantify the performance of a Nuclear Power Plant (NPP) comprised of two IRIS reactor modules operating simultaneously with a common steam header, which in turn is connected to a single turbine, resulting in a steam-mixing control problem with respect to “load-following” scenarios, such as varying load during the day or reduced consumption during the weekend. To solve this problem a single-module IRIS SIMULINK model previously developed by another researcher is modified to include a second module and was used to quantify the responses from both modules. In order to develop research related to instrumentation and control, and equipment and sensor monitoring, the second objective is to build a two-tank multivariate loop in the Nuclear Engineering Department at the University of Tennessee. This loop provides the framework necessary to investigate and test control strategies and fault detection in sensors, equipment and actuators. The third objective is to experimentally develop and demonstrate a fault-tolerant control strategy using this loop. Using six correlated variables in a single-tank configuration, five inferential models and one Auto-Associative Kernel Regression (AAKR) model were developed to detect faults in process sensors. Once detected the faulty measurements were successfully substituted with prediction values, which would provide the necessary flexibility and time to find the source of discrepancy and resolve it, such as in an operating power plant. Finally, using the same empirical models, an actuator failure was simulated and once detected the control was automatically transferred and reconfigured from one tank to another, providing survivability to the system

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Known as the fourth industrial revolution, digitization is an ongoing trend in all fields, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and to take actions more rapidly, in areas such as health and monitoring, radiation detection, and optimization. By acknowledging the practicality and popularity of these methods, it is imperative to understand the benefits and barriers of implementing such methodologies to create better research plans and identify project risks and opportunities. This dissertation discusses different technologies and their integration and challenges within the nuclear industry. It is recognized that concepts of complexity and emergent behavior, as well as the importance of such properties as a distinctive aspect of nuclear and radiological engineering problems in which holistic approaches are crucial to innovation. Overall, the development and application of learning-based methods can be promising in the nuclear industry and many related tasks as long as expert knowledge is considered in the desired application to ensure a robust application of such methods. Cross-discipline studies and the creation of benchmarks are highly suggested for future practices