Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit.

In order to satisfy the growing demands of control performance and energy conservation in power generation process, a novel T-S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed and applied to the coordinated control system (CCS) of ultra-supercritical unit in 1000MW power plant. The T-S fuzzy modeling consists of the identifications of premise part and consequence part. In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method. Secondly, the initial cluster centers are modified through QABC algorithm to guarantee the integral of data and avoid possible marginalization. Then, the consequence part is identified through exponentially-weighted least squares. Furthermore, on account of the obtained fuzzy model, an energy-saving predictive control (ESPC) algorithm based on the generalized predictive control is introduced. In the rolling optimization process of ESPC, the values of manipulated variables taken as energy consumption indicator are introduced into objective function to decrease the consumption of energy and improve the performance of control process. Meanwhile, the addition of manipulated variables constraints can obtain further improvements of energy-saving efficiency and control performance. The simulation results demonstrate the high precision of identified model and ideal performance along with energy-saving ability of ESPC.