Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation

Abstract The major cause of the performance degradation of industrial gas turbines is compressor fouling due to airborne contaminants. Performance diagnostics is required to evaluate degradation precisely. In general, the measured performance in the fully opened inlet guide vane (IGV) condition is regarded as full-load performance and used for diagnostics. A new diagnostic method is proposed in this study. A scheme to determine whether the measured performance is at full-load operation is suggested. If operation is not at full-load, a virtual gas turbine state corresponding to the measured data is modeled using adaptive modeling. Then, the virtual full-load performance and the corrected performance are predicted using a reference firing temperature. This calculation methodology is applied to almost two-years of data of a 150 MW class gas turbine. The analysis revealed that the maximum reduction of power output and efficiency are 14.8 MW and 0.8 percentage points compared with the rated performance. In addition, it was shown that if the measured performance is used directly, the maximum deviation in the predicted power degradation was as much as 4.9 MW (2.8%) compared with the rated performance. This paper demonstrates the necessity of a model-based analysis for enhancing the accuracy of performance diagnostics.