Model based predictive control for load following of a pressurised water reactor

Abstract

By September 2009 the International Atomic Energy Agency reported that the number of commercially operated nuclear reactors in 30 countries across the world is 436, around 50 reactors are currently being constructed, 137 reactors have been ordered or is already planned, and there are around 295 proposed reactors. Pressurised water reactors (PWRs) make up the majority of these numbers. The growing number of carbon emissions and the ongoing fight against fossil fuel power stations might see the number of planned nuclear reactors increase even more to be able to satisfy the world’s need for cleaner energy. To ensure that technology keeps pace with this growing demand, ongoing research is essential. Not only is the research of new reactor technologies (i.e. High Temperature Reactors) important, but improving the current technologies (i.e. PWRs) is critical. With the increased contribution of nuclear generated electricity to our grids, it is becoming more common for nuclear reactors to be operated as load following units, and not base load units as they are more commonly being operated. Therefore a need exists to study and develop new strategies and technologies to improve the automatic load following capabilities of reactors. PWR power plants are multivariable systems. In this study a multivariable, more specifically, a model predictive controller (MPC) is developed for controlling the load following of a nuclear power plant, more specifically a PWR plant. In developing this controller system identification is employed to develop a model of the PWR plant. For the identification of the model, measured data from a computer based PWR simulator is used as the input. The identified plant model is used to develop the MPC controller. The controller is developed and tested on the plant model. The MPC controller is also evaluated against another set of measured data from the simulator. To compare the performance of the MPC controller to that of the conventional controller the ITAE performance index is employed. During the process Matlab®, the System Identification Toolbox™, the MPC Toolbox™ and Simulink® are used. The results reveal that MPC is practicable to be used in the control of non-linear systems such as PWR plants. The MPC controller showed good results for controlling the system and also outperformed the conventional controllers. A further result from the dissertation is that system identification can successfully be used to develop models for use in model based controllers like MPC controllers. The results of the research show that a need exists for future research to improve the methods to eventually have a controller that can be applied on a commercial plant.