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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 turbinegovernor 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.

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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. Although concerns about faults in the sensors and actuators in NPPs is a similarly important topic, only a few papers have discussed it. In this study, fault detection and diagnosis (FDD) based on neural networks (NN) and K-nearest neighbour (KNN) is addressed for 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.INDEX TERMS Fault classification, fault detection, K-nearest neighbor (KNN), neural networks (NNs), nuclear power plants (NPPs), pressurized water reactor (PWR).