A Comparison of Two Trending Strategies for Gas Turbine Performance Prediction

Abstract

Complex multidomain dynamic systems demand reliable health monitoring to minimize breakdowns and downtime, thereby enabling cost savings and increased operator safety. Diagnostic and prognostic strategies monitor a system’s transient and steady-state operations, detecting deviations from normal operating scenarios and warning operators of potential system anomalies. System diagnostics detect, identify, and isolate a system fault while prognostics offer strategies to predict system behavior at a future operating time to define the useful period before failure criterion is reached. This paper presents the development and the experimental application of two methods to predict the system behavior based on trends in performance. Statistical regression concepts have been used to analyze dynamic plant signals, and based on these results, future plant operation was estimated. Wavelet transforms were used to condition the signal, and the denoised signals were subsequently forecast. The case study presented here applies the two methodologies to the operational data from a simple cycle 85MW General Electric gas turbine. Those operating data were used to train and validate the algorithms. A comparison of the two methodologies reveals that the wavelet forecast is better than the statistical strategy with lower forecasting error. The developed approaches may be used in parallel with a diagnostic algorithm to monitor gas turbine system behavior.