Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm

Ultra-supercritical units with high steam temperature and pressure have been widely employed in thermal power plants due to their high efficiency and environmental friendliness. To ensure efficient unit operation, it is necessary to develop a model for the boiler-turbine coupled process of the unit. The present models may be complicated or non-transparent, preventing their practical application. In this work, the model structure is a transfer function matrix by dynamic analysis, and the model parameters are obtained by the proposed data-driven multivariable model parameters intelligent identification scheme with the cloud adaptive chaotic bird swarm algorithm combining sheep optimization and lion swarm optimization. By employing operational data from 1000 MW ultra-supercritical unit, extensive experimental results were given to show that the model identified was reasonable and accurate by comparison tests between identification outputs and process outputs around 800 MW operation condition. The developed model can provide reference for further control strategy synthesis and performance optimization. (C) 2020 Published by Elsevier Ltd.