Abstract
This work aims to present the development and testing of an innovative tool for surge prevention in advanced gas turbine cycles. The presence of additional components, such as a saturator in humid cycles, a heat exchanger for an external combustor, a solar receiver or fuel cell stack in a hybrid system, implies the presence of larger size volumes between compressor outlet and recuperator or expander inlet. This large volume increases the risk of incurring in surge instability, especially during dynamic operations. For these reasons, at the University of Genoa, the Thermochemical Power Group (TPG) has implemented four surge precursors in a new diagnostic real-time software which can recognise a surge incipience condition comparing the precursor values with a set of moving thresholds. The most innovative aspects of this work are: (i) operational range extension and safer management of advanced gas turbine systems for energy generation, (ii) positive impact in energy efficiency due to this range extension of high efficiency systems, (iii) development of a new diagnostic tool for surge prevention using standard probes, (iv) small impact of this tool on the control and sensor costs, (v) software flexibility for adaptation to different conditions and machines. This very important final aspect is obtained with thresholds able to change automatically to adapt themselves to the plant and machine operational regime. From the cost point of view, the utilization of standard measurements is an essential requirement to equip commercial machines without significant impact on the capital costs. The software performance has been demonstrated using experimental data from a test rig composed of a T100 microturbine connected with a modular vessel, which permits to generate the effect of additional components (especially from the volume size point of view). Vibro-acoustic data, collected during machine transients from a stable operative condition to surge, were used to tune all the software parameters and to obtain a good surge predictivity.
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