Multivariate gaussian process incorporated predictive model for stream turbine power plant

Abstract

Steam power turbine-based power plant approximately contributes 90% of the total electricity produced in the United States. Mainly steam turbine consists of multiple types of turbine, boiler, attemperator, reheater, etc. Power is produced through the steam with high pressure and temperature that is conducted by the turbines. The dynamics of the power plant are highly nonlinear considering all these elements in the model. We proposed to capture the dynamics of the power plant through Simulink modeling where data are generated, and a subsequent data-driven predictive modeling approach is used to detect the power generation from these turbines by the multivariate Gaussian process (MGP). The modeling approach is considered to predict the power generation from these turbines which can capture the cross-correlations between the turbines. Also, the sensitivity analysis of the input parameters is constructed for each turbine to find out the most important factors.