There is a continuing tendency to apply many of the powerful results of modern control theory to various industrial processes. Power systems have been indicated as one area where significant progress can be expected. Practically all results of modern control theory require that models of the processes in terms of state equations are available. The need to obtain such models has been a strong motivation for research in the area of modelling and identification. Some progress and on plant experiments is discussed and compared. Particular emphasis is given to parameter estimation techniques like the maximum likelihood method which offers a possibility of combining physical a priori knowledge with experimental investigations. The formulation of identification problems is discussed, including the choice of criteria and model structures. The techniques are illustrated by applications to data obtained from measurements on various components of a power system. The examples include an electric generator, a nuclear reactor and a drum boiler, and serve to illustrate the potentials and limitations of system identification and modelling techniques when they are applied to real data
