Information entropy theory for steam turbine system monitoring study

Abstract

A steam turbine is one of the components in a power plant. Considered as a strong coupling system, steam turbines may possess correlation information among the operation parameters. In this article, a correlation extraction method based on mutual information theory is proposed for performance monitoring. First, five functions between two variables including linear and nonlinear relationships are tested to validate the proposed method. In the first industrial case study, a sensor fault detection based on the governing stage pressure is conducted on the abnormal fluctuation mutual entropy values calculated by operation data. The results show the proposed method may effectively be used to detect sensor errors and locate the first error sample. Furthermore, uncertainty analysis on the heat rate is studied to sort the importance degree of the relative parameters. The mutual information entropy difference values are then calculated to quantify the sensitivity degree of the operation parameters to the heat rate index. The results in this case study are in accordance with the sequences by the Guide to the Expression of Uncertainty Measurement (GUM) and Monte Carlo (MC) methods. In conclusion, mutual information entropy may hence be used as a new solution for turbine monitoring, combined with information theory.