Modeling of an ultra-supercritical boiler-turbine system with stacked denoising auto-encoder and long short-term memory network

Abstract

The ultra-supercritical (USC) coal fired boiler-turbine unit is an advanced power generation system with low emissions and high efficiency. It is also a typical multivariable nonlinear system with great inertia. Generally, building an accurate analytic model using the conventional system identification methods are quite difficult. However, the big data generated by the monitoring system can reflect the USC unit's operation status and reveal the internal mechanism, if appropriate data analysis methods are developed. A deep neural network (DNN) is proposed in this paper to model a 1000 MW USC unit. In this DNN, stacked denoising auto-encoder is adopted to obtain the intrinsic features from the input data, while the long short-term memory network is in charge of outputting the expected normal behaviors of USC system along the time axis. Furthermore, to guarantee the convergence of this network, a reasonable intensity of added noise is identified via Lyapunov stability method. The DNN model is compared with the traditional multi-layer perception network, the stacked denoising auto-encoder, and two other random neural networks, to show the advantages in forecasting the dynamic behavior of USC unit.