Multivariate Gaussian Process Incorporated Predictive Model for Stream Turbine Power Plant

TL;DR

This paper introduces a multivariate Gaussian process model to accurately predict power output in steam turbine power plants, capturing inter-turbine correlations and analyzing input sensitivities.

Contribution

It proposes a novel multivariate Gaussian process approach for modeling interconnected turbines, improving prediction accuracy and understanding parameter importance.

Findings

Effective prediction of turbine power output using MGP.

Identification of key input parameters influencing power generation.

Enhanced understanding of turbine interdependencies.

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 total power generation of the power plant is highly nonlinear considering all these elements in the model. We perform a predictive modeling approach to detect the power generation from these turbines by the Gaussian process (GP) model. As there are multiple interconnected turbines, we consider a multivariate Gaussian process (MGP) modeling 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 parameters.