Data-driven modeling of 600 MW supercritical unit under full operating conditions based on Transformer-XL.

Abstract

Improving the flexible and deep peak shaving capability of supercritical (SC) unit under full operating conditions to adapt a larger-scale renewable energy integrated into the power grid is the main choice of novel power system. However, it is particularly challenging to establish an accurate SC unit model under large-scale variable loads and deep peak shaving. To this end, a data-driven modeling strategy combining Transformer-Extra Long (Transformer-XL) and quantum chaotic nutcracker optimization algorithm is proposed. Firstly, three models of the SC unit under once-through/recirculation/shut-down are built via analyzing its mechanism of the operation process, respectively. Secondly, the superior performance of Transformer-XL in obtaining global feature information is employed to effectively solve the problem of high information dependence caused by the strong coupling and nonlinearity of SC unit. Then, the improved quantum chaotic nutcracker optimization algorithm with higher search accuracy is proposed to obtain the optimal parameters of Transformer-XL based on the logistic chaotic mapping and quantum thinking. Feature information dependencies and optimal parameter settings are fully considered in the proposed modeling scheme, which results in an accurate model of SC unit under full operating conditions. Finally, various simulations and comparisons are conducted based on the on-site data of 600 MW SC unit to demonstrate the superiority of the proposed data-driven modeling strategy. According to the improved Transformer-XL, the mean square errors of the proposed SC unit model under once-through/recirculation/shut-down modes are less than 2.500E-03, which verifies the high accuracy of the model. Consequently, the developed model is suitable for application in the controller designing and the operating efficiency and flexibility improvement of SC unit.