In this work, component characteristics of a reheat cycle gas turbine in a commercial combined cycle power plant were evaluated. An inverse performance analysis, in which component characteristic parameters were estimated based on measured performance data, was carried out. The measured parameters were the power, the fuel flow rates of two combustors, and the temperatures and pressures at various locations such as the compressor discharge, exits of both the high-and low-pressure turbines. The estimated parameters from the analysis include the compressor and turbine efficiencies and the inlet air flow rate. The analysis was performed for a wide operation range in terms of the ambient temperature and load, providing a database for the variations of the characteristic parameters with changes in the operating condition. In addition, a sensitivity analysis was performed to examine the influence of the uncertainties of the measured parameters on the estimated parameters. The analysis program can be further developed into a performance diagnosis tool and the obtained component characteristic data can be used as reference database.
This study aims to simulate performance deterioration of a microturbine and apply artificial neural network to its performance diagnosis. As it is hard to obtain test data with degraded component performance, the degraded engine data have been acquired through simulation. Artificial neural network is adopted as the diagnosis tool. First, the microturbine has been tested to get reference operation data, assumed to be degradation free. Then, a simulation program was set up to regenerate the performance test data. Deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (deterioration of single and two components) were simulated to generate fault data. The neural network was trained with majority of the data sets. Then, the remaining data sets were used to check the predictability of the neural network. Given measurable performance parameters (power, temperatures, pressures) as inputs to the neural network, characteristic parameters of each component were predicted as outputs and compared with original data. The neural network produced sufficiently accurate prediction. Reducing the number of input data decreased prediction accuracy. However, excluding up to a couple of input data still produced acceptable accuracy.
This study evaluated component characteristics of the reheat cycle gas turbine in a combined cycle power plant. High pressure ratio, sequential combustion, large amount of cooling flow and full utilization of the inlet guide vane distinguishes the engine from simple cycle engines. Considering the detailed engine configuration, performance analysis using an inverse calculation, based on measured performance data, has been carried out to estimate the component characteristic parameters that closely match the measured performance parameters. The measured parameters are power, fuel flow rates of two combustors, and temperatures and pressures at compressor discharge, exits of both high and low pressure turbines. The estimated parameters from the analysis include not only the compressor and turbine efficiencies but also the inlet air flow rate. The analysis has been performed for a wide operation range in terms of ambient temperature and load. Not only the absolute value of the inlet air flow rate but also its variation with the operating condition change correspond very well with the reference data from the manufacturer. The compressor and turbine efficiencies at each full load condition and their variations with the operating condition change were examined. The sensitivity of the estimated parameters to the uncertainties of the measured parameters has also been investigated.
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