Modeling, parameter estimation and state-space control of a steam turbine
Rodrigo Trentini1; Rüdiger Kutzner1; Lutz Hofmann2; Alexandre Campos3; Clodoaldo Schutel Furtado Neto3
1 Hochschule Hannover, Germany; 2 Leibniz Universität Hannover, Germany; 3 UDESC
doi:10.20906/CPS/COB-2015-0810
Resumo
The common analysis of steam turbines to be used in Power Systems Stability studies utilizes a linear third order single-input single-output (SISO) model derived from the Law of Conservation of Mass, where some non-realistic assumptions are considered, such as constant temperature and no pressure dynamics and heat transfer. However, despite of the cited assumptions, the simplified model represents the real turbine behaviour over an equilibrium point with enough accuracy when one aims the stability study of the overall generating unit. Nevertheless, due to the extremely reduced model only classical control theory, such as PI or PID control, is normally applied, since there is no access to the internal states of the machine, limiting the control possibilities of the system. For this reason, this paper intends to present a methodology for using optimum state-space control whenever only few information from the real steam turbine is available. Interesting to notice that this approach can, in principle, be used along with any dynamic system. At first an innovative mathematical modelling for the steam turbine is presented. It is fully based on thermodynamic laws where the main aim is to have access to the states, namely pressure and temperature, of each turbine section. The equating corresponds to a 6th-order strongly coupled nonlinear model which can be used further in system simulations, since it depicts the turbine behaviour also outside its common equilibrium point. The model is then linearized and the problem of the unknown parameters is addressed using the data of the simulated simplified system along with the nonlinear system identification techniques named Particle Swarm Optimization (PSO). Further, the identified parameters feed a Kalman Filter where turbine's states are estimated, allowing its control through modern techniques which are capable of dealing with Multiple-Input Multiple-Output (MIMO) systems, such as Linear Quadratic Gaussian control (LQG). Finally, the whole system composed b
Palavras-chave: Steam turbine; Particle Swarm Optimization; Optimal Control