Main Stream Temperature Control For Ultra-supercritical Units Based On Genetic Algorithm

Ultra-Supercritical main steam temperature is a control object with large inertia, large delay, nonlinear, time-variability and other complex features. In order to make main steam temperature control achieve satisfactory results, we design main steam temperature cascade control system structure, the main loop using PID control combined with Smith predictor, the vice loop using PD control. Then we identify the model of main steam temperature with the genetic algorithm, and adjust the parameters of the Smith predictor according to the estimated model, while optimizing the PID parameters with the genetic algorithm. Thus we achieve the adaptive control of main steam temperature.When identifying the object model and optimizing the PID parameters with the genetic algorithm, we first establish the objective functions and constraints, then design a computing process for genetic algorithm. For the defects that the convergence precision of the traditional genetic algorithm is low and the computation is slow, we improve the method of the population initialization, selection operator, crossover operator and mutation operator. Finally, choosing a first order inertia model with the pure delay as an example, we use the improved genetic algorithm achieve the main steam temperature control object model identification, and use the MATLAB simulation get the model. Then based on the estimated model, we use the improved genetic algorithm achieve the controller parameter optimization. Finally we use the MATLAB simulation get the controller parameters. Simulation results prove the method used in this paper.