Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

Abstract

Superheated steam temperature (SST) is one of the most critical parameters for the process safety, overall efficiency and pollution reduction of coal-fired power plants. However, SST control is challenging due to various disturbances and model uncertainties, especially in the face of the growing penetration of intermittent renewable energy into the power grid. To this end, a cascaded Disturbance Observer-PI (DOB-PI) control strategy is proposed to enhance control performance. The observer design and parameter tuning are carried out through mechanism analysis on the proposed structure. Furthermore, a robust loop shaping method is introduced as a hard constraint to balance the control performance and robustness. The controller parameters are optimized based on the multi-objective artificial bee colony optimization (MOABC) algorithm. Simulation results show that the proposed cascaded DOB-PI control strategy can significantly improve the disturbance rejection performance of both the inner- and outer-loops of the SST control system. This paper indicates promising prospects for the proposed method in future applications.