Fault tolerant control for increased safety and security of nuclear power plants
Lead Research Organisation:
Leeds Beckett University
Department Name: Computing, Creative Tech & Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Banerjee S
(2020)
ANN Based Sensor and Actuator Fault Detection in Nuclear Reactors
Naimi A
(2022)
Nonlinear Model Predictive Control Using Feedback Linearization for a Pressurized Water Nuclear Power Plant
in IEEE Access
Naimi A
(2022)
Machine Learning-Based Fault Diagnosis for a PWR Nuclear Power Plant
in IEEE Access
Naimi A
(2022)
Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor
in IEEE Access
| Description | During this project ,a single robust PID controller has been designed which is capable to track the reactor power during the load following over a wide range; An H-infinity controller has been designed which could stabilise the system in wide power regime and handle the thermal hydraulic disturbances; LQGI/LTR robust control system is a new hybrid control strategy for a pressurized water type nuclear power plant by integrating linear quadratic integrator (LQI), linear quadratic Gaussian (LQG), and loop transfer recovery (LTR) approaches . The control architecture offers robust performance and tracks the reference set-point with zero steady state error in the presence of uncertainties and disturbance; Dynamic modelling presents an integrated non-linear dynamic model of a pressurized water-type nuclear reactor (PWR) and associated plant components for control design and evaluation purposes including the set of plant sections considered, the implementation of carefully tuned control strategies, the completeness of the model equations, and the availability of parameter values so that the model is readily implementable and has the potential to become a benchmark for control design studies in PWR nuclear power plants; Robust-optimal integrated control design technique is formulated for a pressurized water type nuclear power plant by integrating the optimal linear quadratic Gaussian (LQG) control with the robust integral sliding mode (ISM) technique. A novel robust-optimal hybrid control scheme is further proposed by integrating the LQG-ISM technique with the loop transfer recovery approach to enhance the effectiveness and robustness capability. The control architecture offers robust performance with minimum control efforts and tracks the reference set-point effectively in the presence of disturbances ; LMI based H-infinity Controller uses a state space model as the point kinetics equations of PWR coupled with the Mann's thermal-hydraulic equations. The system matrices have been evaluated at different power levels with uncertainty in parameters to produce an interval state space model. A H-infinity based Full State Feedback Controller has been designed for this interval plant and then used for establishing a stability criterion in presence of disturbances; ANN based sensor and actuator fault detection technique in nuclear reactors use data generated in simulation to train the ANNs as per requirement and to compare with the plant signal during fault. A technique has been proposed analysing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in closed loop in presence of robust (Proportional-Integral-Derivative) PID controller; A PID controller based on dynamic neural network and feedback linearization control has been designed for a Pressurized Water Reactor; A dynamic neural network has been able to find to identify a pressurized water reactor; A gain scheduled subspace predictive controller has been designed for nuclear reactor power control. The developed controller incorporates the robustness feature of subspace identification with the adaptive capability of gain scheduling in a predictive control set-up. The controller is designed to handle process variations effectively; A disturbance observer-based predictive control strategy for a pressurized water-type nuclear reactor uses a subspace matrix structure to improve the capability of classical predictive controllers in handling external disturbances. The controller is designed directly from measurements. Then, a disturbance observer is designed using subspace matrices to estimate the external disturbance. Both of the designs are integrated using a feed-forward plus feed-back strategy to form the proposed control strategy; A robust subspace predictive controller based on integral sliding mode is designed for a pressurized water reactor, which could improve the capability of subspace predictive controller in handling uncertainties and external disturbances; L1-Adaptive Robust Control has been designed for a Pressurized Water-Type Nuclear Power Plant; An LQGI/LTR based Robust Controller has been designed for a Pressurized Water Nuclear Power Plant; A nonlinear model predictive controller based on feedback linearisation and dynamic neural networks has been designed for a Pressurized Water-Type Nuclear Power Plant; Fault detection and isolation approaches based on neural networks and K-nearest neighbour have been realized for a pressurized water reactor. These works have been published in the journals "IEEE Access", "IEEE Transactions on Nuclear Science", "Progress in Nuclear Energy", "Nuclear Engineering and Design", and "Annals of Nuclear Energy" or have been presented in international conferences: The 7th International Conference on Control, Mechatronics and Automation (ICCMA), 15th European Workshop on Advanced Control and Diagnosis, ACD 2019, 2020 28th Mediterranean Conference on Control and Automation (MED), 13th International Conference on Developments in eSystems Engineering, (DeSE 2020), 7th International Conference on Control, Decision and Information Technologies (CoDIT'2020), 8th International Conference on Control, Mechatronics and Automation, (ICCMA 2020). In the nuclear power industry, safety and reliability are of the utmost importance. Sensors and actuators are integral components in such systems, and potential faults may adversely impact system performance. We developed s a machine learning-based fault detection and diagnosis(FDD) technique for actuators and sensors in a pressurized water reactor (PWR) nuclear power plant. In the proposed FDD framework, faults are first detected using a shallow neural network. Second, fault diagnosis is performed using 15 different classifiers provided in the MATLAB Classification Learner toolbox. Good accuracy has been achieved. We also developed an arbitrary order continuous-time sliding mode controller based on the super-twisting algorithm for a nonlinear pressurized water nuclear power plant. A proportional-derivative terminal sliding surface is designed to achieve the finite time convergence and to enhanced the tracking performance. The proposed controller is chattering free, which is always preferable in most of the practical applications and, it is robust against Lipschitz in time uncertainties. Superiority of the proposed controller over some conventional control techniques in the presence of uncertainties is shown with the help of simulation results in the MATLAB/Simulink environment. We design an input-output based control for a PWR nuclear power plant. We designed a fractional order integral sliding mode control for a PWR nuclear power plant. These works have been published in the journals "IEEE Access", "Progress in Nuclear Energy", or have been presented in the European Control Conference, 2022. |
| Exploitation Route | The findings could be used by other control and modelling engineer as these have been published. The work also has been discussed with Bhabha Atomic Research Centre (BARC) and Indira Gandhi Centre for Atomic Research(GCAR). BARC and IGCAR also could use these findings. |
| Sectors | Aerospace, Defence and Marine,Chemicals,Energy,Environment,Financial Services, and Management Consultancy,Manufacturing, including Industrial Biotechology |
| Description | The control methods, and fault detection and isolation methods could be used at least by Bhabha Atomic Research Centre (BARC) and Indira Gandhi Centre for Atomic Research(IGCAR). |
| First Year Of Impact | 2021 |
| Sector | Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Energy,Financial Services, and Management Consultancy,Healthcare |
| Impact Types | Societal,Economic |
| Description | Model predictive control system design for nuclear power plants |
| Amount | £60,000 (GBP) |
| Organisation | Leeds Beckett University |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 10/2019 |
| End | 09/2022 |
| Title | Various actuator and sensor faults for nuclear power plants |
| Description | The datasets include the bias and drift faults in sensors, and offset and saturation faults in actuators in five different loops of a PWR plant. In total, there are 20 faults. A description of the faults is provided below: F1 Offset in pressurizer level actuator . F2 Saturation in pressurizer level actuator. F3 Bias in pressurizer level sensor. F4 Drift in pressurizer level sensor. F5 Offset added to the turbine-governor valve (steam pressureloop) signal. F6 Saturation on turbine-governor valve (steam pressure loop) . F7 Bias on steam generator sensor . F8 Drift on steam generator sensor. F9 Offset on pressurizer heater F10 Saturation on pressurizer heater. F11 Bias on pressurizer pressure sensor. F12 Drift on pressurizer pressure sensor. F13 Offset turbine governor-valve (turbine speed control). F14 Saturation on turbine-governor valve (turbine speed control) . F15 Bias on turbine speed sensor. F16 Drift on turbine speed sensor. F17 Offset on control rod actuator. F18 Saturation on control rod actuator. F19 Bias on power sensor. F20 Drift on power sensor |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | No |
| Impact | This is just developed in Late 2022. |
| URL | https://ieeexplore.ieee.org/abstract/document/9967986 |
| Title | Various faults of sensors and actuators in a reactor core |
| Description | . A. TYPES OF FAULTS Six single faults and two simultaneous faults are generated. The types of faults considered included bias, drift, actuator offset, and actuator, saturation faults, which are described as follows: Bias fault. This is one of the most common faults in sensors, corresponding to a constant offset added to the sensor output, which may be caused by inappropriate calibration or physical changes in the sensor . Bias failures are common faults in reactor cores, and their maintenance can be costly . The bias fault is injected into the power and temperature sensors at a certain time. Drift fault. This consists of a time-varying offset. The drift fault is difficult to detect because the drifting amplitudes initially low , therefore it is important to have a sensor drift fault. Drift faults are common and can cause power reduction. As with the bias fault, the drift fault is injected into the power and temperature sensors at a certain time. Actuator saturation fault. This is when the actuator (control rod system) exceeds a set saturation value. This phenomenon inevitably must be considered because of physical limitations that, in practice, can led to important deterioration of the system. Actuator offset fault. This corresponds to an offset added to the control rod system at a certain time. This failure can occur because of design/ manufacturing defects in the actuator. These are eight faults in this datasets: Fault ID Process variable Type Fault 1 Reactivity Actuator offset fault Fault 2 Reactivity Actuator saturation fault Fault 3 Power Power sensor bias fault ( Fault 4 Power Power sensor drift fault Fault 5 Temperature Temperature sensor bias fault Fault 6 Temperature Temperature sensor drift fault Fault 7 Reactivity/ Power Actuator saturation fault + Power sensor bias fault Fault 8 Reactivity/ Power Actuator saturation fault + Power sensor drift fault |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | The paper has been cited in three papers. |
| URL | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9706445 |
| Description | Fault tolerant control for increased safety and security of nuclear power plants |
| Organisation | Bhabbha Atomic Research Centre |
| Country | India |
| Sector | Public |
| PI Contribution | Our team designs robust fault tolerant control system and provide fault detection and isolation models. |
| Collaborator Contribution | BARC provide knowledge of nuclear power plants and nuclear power plant control system design. IGCAR Provide knowledge of nuclear power plants. University of Portsmouth provide nuclear power plant models and design optimal fault tolerant control system. |
| Impact | 1. LMI Based Robust PID Controller Design for PWR with Bounded Uncertainty using Interval Approach 2. Wavelet-based Model Predictive Control of PWR Nuclear Reactor using Multi-Scale Subspace Identification 3. Disciplines involved are nuclear power plants, modelling, control system design, machine learning |
| Start Year | 2019 |
| Description | Fault tolerant control for increased safety and security of nuclear power plants |
| Organisation | Indira Gandhi Centre for Atomic Research (IGCAR) |
| Country | India |
| Sector | Academic/University |
| PI Contribution | Our team designs robust fault tolerant control system and provide fault detection and isolation models. |
| Collaborator Contribution | BARC provide knowledge of nuclear power plants and nuclear power plant control system design. IGCAR Provide knowledge of nuclear power plants. University of Portsmouth provide nuclear power plant models and design optimal fault tolerant control system. |
| Impact | 1. LMI Based Robust PID Controller Design for PWR with Bounded Uncertainty using Interval Approach 2. Wavelet-based Model Predictive Control of PWR Nuclear Reactor using Multi-Scale Subspace Identification 3. Disciplines involved are nuclear power plants, modelling, control system design, machine learning |
| Start Year | 2019 |
| Description | Fault tolerant control for increased safety and security of nuclear power plants |
| Organisation | University of Portsmouth |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Our team designs robust fault tolerant control system and provide fault detection and isolation models. |
| Collaborator Contribution | BARC provide knowledge of nuclear power plants and nuclear power plant control system design. IGCAR Provide knowledge of nuclear power plants. University of Portsmouth provide nuclear power plant models and design optimal fault tolerant control system. |
| Impact | 1. LMI Based Robust PID Controller Design for PWR with Bounded Uncertainty using Interval Approach 2. Wavelet-based Model Predictive Control of PWR Nuclear Reactor using Multi-Scale Subspace Identification 3. Disciplines involved are nuclear power plants, modelling, control system design, machine learning |
| Start Year | 2019 |
| Description | LMI based Robust PID controller for nuclear power plant |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Interaction with researchers and dissemination of the research results were the main purpose of the meeting. In interaction session, people from interdisciplinary domain asked questions and has a discussion. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Portsmouth University final project workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | 16 people attended the Portsmouth final project workshop,which sparked questions and discussion afterwards |
| Year(s) Of Engagement Activity | 2021 |