Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service Fault Detection and Isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored; occasional high-frequency periodical oscillations due to disturbances and faults; and sensor faults. Techniques like Principal Component Analysis (PCA) can be used for FDI, however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multi-scale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring has been proposed in this paper for online FDI of Advanced Heavy Water Reactor (AHWR). A new mathematical formulation of the Generalized Likelihood Ratio Test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor.
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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