Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network

Ultra-supercritical units are widely employed in peak shaving and fluctuation suppression of power grid. To serve for efficient operation and fast power varying, it is necessary to develop a model for the coordinated control system (CCS) of the unit. The existing model may be limited by operating conditions and assumptions, reflecting partial dynamic characteristics of CCS. In this work, based on an improved transformer neural network and optimization algorithm, a novel data-driven modeling approach is proposed for the boiler-turbine coupled process in the CCS of the ultra-supercritical units. Firstly, the proposed model is composed of transformer network with convolution operation and residual network with self-attention mechanism. Relying on the ability of the network to extract long-term dependencies and local feature maps, an accurate identification model is obtained under the fast power fluctuation operation of ultra-supercritical units. Secondly, some hyper parameters in the network are identified by the proposed heap-based optimization algorithm fused with harris hawks optimization (HHBO). In this stage, the higher accuracy and rapidity are fulfilled with the assistance of two improvements. Then, the convergence of the proposed network is proved using the Lyapunov function. Finally, by employing operational data from 1000 MW ultra-supercritical unit, the superiority of the proposed approach is further validated by extensive simulations and comparative experiments. The mean square errors of the electrical power, the main steam pressure, and the separator temperature are 1.34E−04, 1.54E−04 and 1.71E−04, respectively. The proposed approach can provide reference for further control strategy design.