Evaluating Gas Turbine Performance Using Machine-Generated Data: Quantifying Degradation and Impacts of Compressor Washing

Abstract

Gas turbine (GT) operators are often met with the challenge of utilizing and making meaning of the vast measurement data collected from machine sensors during operation. This can easily be about 576 × 106 data points of gas path measurements for one machine in a base load operation in a year, if the width of the data is 20 columns of measured and calculated parameters. This study focuses on the utilization of large data in the context of quantifying the degradation that is mostly related to compressor fouling, in addition to investigations on the impact of offline and online compressor washing. To achieve this, four GT engines operating for about 3.5 years with 51 offline washes and 1184 occasions of online washes were examined. This investigation includes different wash frequencies, liquid concentrations, and one engine operation without online washing (only offline). This study has involved correcting measurement data not only just with compressor inlet temperatures (CITs) and pressures but also with relative humidity (RH). turbomatch, an in-house GT performance simulation software has been implemented to obtain nondimensional factors for the corrections. All of the data visualization and analysis have been conducted using tableau analytics software, which facilitates the investigation of global and local events within an operation. The concept of using of handles and filters is proposed in this study, and it demonstrates the level of insight to the data and forms the basis of the outcomes obtained. This work shows that during operation, the engine performance is mostly deteriorating, though to varying degrees. Online washing also showed an influence on this, reducing the average degradation rate each hour by half, when compared to the engine operating only with offline washing. Hourly marginal improvements were also observed with an increased average wash frequency of nine hours and a similar outcome obtained when the washing solution is 2.3 times more concentrated. Clear benefits of offline washes are also presented, alongside the typically obtainable values of increased power output after a wash, also in relation to the number of operating hours before a wash.