Hybrid Modeling of Heavy Duty Gas Turbines for On-Line Performance Monitoring

Abstract

Due to their complex physics, accurate modeling of modern heavy duty gas turbines can be both challenging and time consuming. For online performance monitoring, the purpose of modeling is to predict operational parameters to assess the current performance and identify any possible deviation between the model’s expected performance parameters and the actual performance. In this paper, a method is presented to tune a physical model to a specific gas turbine by applying a data-driven approach to correct for the differences between the real gas turbine operation and the performance model prediction of the same. The first step in this process is to generate a surrogate model of the 1st principle performance model through the use of a neural network. A second “correction model” is then developed from selected operational data to correct the differences between the surrogate model and the real gas turbine. This corrects for the inaccuracies between the performance model and the real operation. The methodology is described and the results from its application to a heavy duty gas turbine are presented in this paper.