Predicting Onset of Combustion Instability in Gas Turbines Using Fiber Optic Sensors

Abstract

A data driven method based on cross-entropy derived from symbolic time series analysis of the chemiluminescence and pressure variations has recently been found to be robust and computationally efficient among existing methods for predicting instability. In this article, we report the development and testing of compact and field-deployable optical fiber-based modules for sensing chemiluminescence and pressure variations in the combustor. The time-series data obtained from the above sensor modules are used to deduce the D-Markov cross-entropy for different protocols corresponding to typical combustor operation conditions. Such cross-entropy data is compared with similar data obtained from standard high speed camera and PZT-based pressure sensors. It is found that the fiber optic sensor modules compare favorably with the conventional sensors thereby providing an exciting new pathway for field-deployable solutions to monitor the onset of thermo-acoustic instability in combustors.