Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbour

Nuclear power plants (NPPs) are complex dynamic systems with multiple sensors and actuators. The presence of faults in the actuators and sensors can deteriorate the system’s performance and cause serious safety issues. This calls for the development of fault detection and diagnosis systems for detection and isolation of such faults. In this study, fault detection and diagnosis (FDD) based on neural networks (NN) and K-nearest neighbour (KNN) algorithm is applied to a pressurized water reactor (PWR). Fault detection is first determined based on the NN. Second, the KNN algorithm is used to classify the faults. The proposed approach is capable of classifying a variety of actuator faults, sensor faults, and multiple simultaneous actuator and sensor faults. A set of simulation results is provided to demonstrate the accuracy of the FDD method. The classifier performance is further compared with other machine learning techniques

Dynamic Neural Network-based Feedback Linearization Control of a Pressurized Water Reactor

This note presents a nonlinear control approach using dynamic neural network (DNN)-based feedback linearization (FBL) for nuclear reactor power control. The reactor model adopted in this study is based on neutronic dynamic and thermal-hydraulic models. The nonlinear plant is identified by a single-layer DNN trained using Quasi-Newton and Interior-Point methods. The feedback linearization scheme is combined with a Proportional-Integral (P-I) controller and simulations show good performance of the proposed controller. The efficacy of the controller is evaluated in the load-following mode of operation. Moreover, the fault-tolerance performance of the proposed approach is tested

Nonlinear model predictive control using feedback linearization for a pressurized water nuclear power plant

The present work aims to introduce a nonlinear control scheme that combines intelligent feedback linearization (FBL) and a model predictive control (MPC) for a pressurized water reactor (PWR). The nonlinear plant model that is considered in this study is described by the first-principles approach, and it consists of 38 state variables. First, system identification using a dynamic neural network (DNN) structure is performed to obtain a standard affine nonlinear system. The quasi-Newton algorithm is employed to find the best DNN model. Then, an FBL is formulated to address the nonlinearity of the DNN model. An MPC controller is developed based on the FBL system to improve the system performance. The designed controller is compared with a linear MPC controller that is based on state-space models to evaluate the performance of the proposed controller. The proposed approach improves the load-following operation and offers better disturbance rejection capability than the conventional MPC. In addition, numerical measures are employed to compare and analyse the performances of the two control strategies

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An {L_{1}} -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust {L_{1}} -adaptive control technique is also proposed by combining the {L_{1}} -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques

Dynamic modelling, simulation, and control design of a pressurized water-type nuclear power plant

This article 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. The model uses the first-principles approach to represent various components of the plant. The model considers the dynamics of the reactor core, thermal hydraulics, piping and plenum, pressurizer, steam generator, condenser, and turbine-governor system, in addition to various actuators and sensors. The response of the proposed model is tested using perturbations in different input variables. Various control loops implementing low-level PI control strategies are designed and implemented in the model to simulate the closed-loop behaviour of the plant. These include control loops for reactor power, steam generator pressure, pressurizer pressure and level, and turbine speed. Linear quadratic Gaussian-based optimal control strategies are further developed and implemented. Unique contributions of the work include the set of plant sections that are 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

Machine Learning-Based Fault Diagnosis for a PWR Nuclear Power Plant

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. It is therefore imperative to design a fault detection and diagnosis (FDD) system that achieves the highest standards of safety. This paper presents a machine learning-based fault detection and diagnosis (FDD) technique for actuators and sensors in a pressurized water reactor (PWR). 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, including support vector machine (SVM), K-nearest neighbor (KNN), and ensemble. Several classifiers were found to provide superior classification performance, including medium KNN, cubic KNN, cosine KNN, weighted KNN, fine Gaussian SVM, quadratic SVM, medium Gaussian SVM, coarse Gaussian, bagged trees, and subspace KNN. The accuracy of the FDD approach was demonstrated using a set of simulation results

Gain Scheduled Subspace Predictive Control of a Pressurized Water-type Nuclear Reactor

This work presents a methodology for designing subspace-based gain scheduled predictive controller for nuclear reactor power control. The main idea is to design a family of predictive controllers directly from measurements and integrate them without employing any explicit process model. 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. The efficacy of the proposed controller is demonstrated for load-following transients using a simulated model of a PWR-type nuclear reactor. Simulation results show that the proposed strategy is effective in addressing the load-following control problem of a non-linear parameter-varying PWR nuclear reactor system

Robust Subspace Predictive Control based on Integral Sliding Mode for a Pressurized Water Reactor

This work combines the subspace predictive control technique with the integral sliding mode control strategy to formulate a novel robust subspace predictive control scheme. The subspace predictive controller provides the nominal control whereas the integral sliding mode controller gives the discontinuous control action. The aim is to improve the capability of subspace predictive controller in handling uncertainties and external disturbances. The proposed control scheme is evaluated with a simulated pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations in the presence of uncertainties

ANN Based Sensor and Actuator Fault Detection in Nuclear Reactors

In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data-driven methods, and signal-based methods. In practical applications, model-based methods are very difficult to achieve. Thus, various data-driven methods and signal- based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation

Disturbance Observer-based Subspace Predictive Control of a Pressurized Water-type Nuclear Reactor

This work presents a disturbance observer-based predictive control strategy using a subspace matrix structure. The aim is to improve the capability of classical predictive controllers in handling external disturbances. A subspace-based predictive 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. The proposed scheme is tested with a simulated model of a pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations. Further, a quantitative analysis is performed to analyse the control performance of the proposed approach