The control of nuclear power plants covers a wide range of problems and requires understanding of the many interactions and objectives involved in obtaining operational flexibility and safety. Improved solutions must be developed as nuclear plants will increasingly require operation in a load-following mode. The authors discuss applications, problems, trends, and their perspectives. Areas where researchers developing control methods can contribute toward improved control design are outlined.

Control of nuclear power plants

This paper presents the results of a feasibility study of a technique for identifying dynamic equivalents of portions of a power system from measurements made only within a restricted Study System without intentional perturbation of the system. The Maximum Likelihood technique is used to identify parameter values for a specified equivalent structure which is suitable for direct use in standard transient stability programs. The identifier was tested using measurements from simulations tuned to match field measurements. The field measurements, identification algorithm, and program test results are described. The expected benefit of this technique is in overcoming a major block in the development of an on-line technique for the evaluation of transient stability contingencies by power system operators.

Dynamic equivalents from on-line measurements

The authors report preliminary results on the development of practical, multivariate, nonlinear, model predictive control for fossil fuel power plants. The approach used involves the development of a first-principles, nonlinear reduced-order model which captures the dominant static and dynamic characteristics of a power plant. This model is used to predict the plant response to control inputs. Since the model will not exactly match the true plant structure, the parameters of the model must be estimated using prediction error methods or nonlinear least squares. This model is then used in a Kalman filter to estimate process states in real time. These estimated states are used for prediction, enabling the computation of the optimal control sequence. The results of full-scale boiler control simulation were encouraging, and conclusively demonstrate the feasibility of the approach. The results appear to be significantly better than those of most existing control systems. Although some additional problems remain to be solved, no serious problems with the technique have been identified. >

Application of nonlinear model-based predictive control to fossil power plants

Comparison of techniques for assessing effects of loading uncertainty upon a long term phosphorous model

This report demonstrates the feasibility of applying stochastic techniques to linear water quality models. The Monte Carlo, First Order, and Generation of Moment Equation techniques are applied to a long term phosphorus model of Lake Washington. The effect of uncertainty of the phosphorus loading term on simulated phosphorus levels is analyzed. All three stochastic techniques produced the same results. The simulated concentrations of phosphorus in the water column are very responsive to uncertainty in annual phosphorus loading, the sediment concentrations relatively insensitive. The Monte Carlo technique is shown to require the most computation time of the three stochastic techniques applied. The First Order and Generation of Moment Equation techniques are shown to be precise and efficient methods of stochastic analysis. In this application they required less than one thousandth the computation time of the Monte Carlo technique. The Generation of Moment Equations technique is also applied to a steady state salinity model of the Colorado River system. Two sources of uncertainty are considered: 1) the estimation of "steady state" values of salinity loading from a limi ted historic data base and 2) the estimation of salinity loading from irrigated land by a semi-empirical approach. Six stochastic simUlations of the Colorado River system are presented. Coefficients of variations of simulated salinities at Imperial Dam are shown to vary from 5.7 to 10.3 percent. The major source of uncertainty in all simulations is the estimation of the steady state salinity loading with the agricultural loading term becoming important in some simulated management alternatives.

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R. L. Moore

Papers

Model identification for adaptive control of nuclear power plants