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Steam power turbine-based power plant approximately contributes 90% of thetotal electricity produced in the United States. Mainly steam turbine consistsof multiple types of turbine, boiler, attemperator, reheater, etc. Power isproduced through the steam with high pressure and temperature that is conductedby the turbines. The total power generation of the power plant is highlynonlinear considering all these elements in the model. We perform a predictivemodeling approach to detect the power generation from these turbines by theGaussian process (GP) model. As there are multiple interconnected turbines, weconsider a multivariate Gaussian process (MGP) modeling to predict the powergeneration from these turbines which can capture the cross-correlations betweenthe turbines. Also, the sensitivity analysis of the input parameters isconstructed for each turbine to find out the most important parameters.

Multivariate Gaussian Process Incorporated Predictive Model for Stream Turbine Power Plant