Experimental Fuzzy Modelling and Control of a Steam Power Plant Boiler

Abstract

Increasing the use of electricity and the need for more and safer power generation has motivated investigation into new control methods resulting in better performance. Better system performance means increase in power generation efficiency, also decrease in the maintenance costs. To design suitable controllers, adequate information about the system dynamics is required, which in turn has motivated the methods of system identification and simulation studies of power plants. In this paper, simple first-order models are developed for the subsystems of a subcritical once-through boiler, based on the principles of thermodynamics and energy—mass balance, together with parameter estimation routines. These routines are applied on the experimental data obtained from a complete set of field experiments. However, since most processes in a boiler are categorized as multi-input and multi-output systems, mathematical boiler models, which are derived from physical structure and parameters estimation routines, lead to a time-consuming procedure, and employing such models in control algorithms becomes very complex. Therefore, to improve the dynamics modelling, a concise multilayer neuro fuzzy model of the boiler is developed. Next, these two models are compared based on the performance of the real system. This comparison validates the accuracy of both original and neuro fuzzy models, while the latter can be successfully employed in simulation studies, and to design modern model-based control systems. Finally, a new Fuzzy P2 ID controller is developed to use for superheaters temperature control. Simulation results show very good performance of this controller in terms of more accurate and less fluctuation in the temperature of corresponding subsystems, compared to the existing classic controllers.