Model identification for adaptive control of nuclear power plants

Abstract

The problem being considered is the identification and adaptation of a stochastic model of a nuclear power plant, and the uses of the stochastic model in the control of large and fast load changes. In order to make such load changes, a control strategy is designed to coordinate the major plant inputs. However, to be viable the control must overcome several kinds of variation and uncertainty in plant behavior. The control developed in this study makes use of a stochastic model as a reduced order representation of the plant. The parameters of this model are identified from measurement data records, using a maximum likelihood identification technique. The stochastic model is then used in a state estimator which gives estimates of important unmeasured plant variables. The stochastic model is also used to adjust control constraints and to predict the plant performance during the load change for display and monitoring purposes. If the plant behavior deviates from the model predictions, the model is adapted to be consistent with the plant performance. These techniques together overcome the difficulties of variation and uncertainty in the nuclear plant behavior, thus permitting large, fast load changes to be made without upsetting the plant operations.