The current study is based on multiple machine learning algorithms to predict the normal behavior of operational parameters including power generated, turbine cross section temperature average their and variation etc. The predictions can be used to identify anomalies and probable failures in the gas turbine performance. The data used in the study is taken from multiple gas turbine units of combined cycled utility power plants which is known to contain operational failures. The predictors include operational parameters such as fuel flow, various air temperature, and pressure, etc.

In the first step, we cluster the observations into different working modes, assuming heterogenous behavior of the gas turbine parameters under various modes. Then we consider predicting the operational parameters under each mode respectively, via algorithms including random forest, generalized additive model, and neural network. The models are trained and parameters are selected based on the overall prediction performance on the validation set.

The comparative advantage based on prediction accuracy and applicability of the algorithms is discussed for real-time use and post processing. The advantage of data-driven methods over physics-based methods is that they provide insight into the behavior of some gas turbine variables, e.g.- turbine cross section temperature variation, which are not explicitly known to have any correlation with other thermodynamic variables.


Prediction of Gas Turbine Performance Using Machine Learning Methods

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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