Transient performance and control strategy of three shaft variable geometry industrial gas turbines

Industrial gas turbines are gaining substantial momentum in decentralized energy scenarios owing to wide range of benefits. In line with global measures to address climate change, industrial gas turbines are also integrated with intermittent renewable energy systems (solar and wind) as a hybrid power generation source. To respond to the fluctuating nature of renewables, gas turbines undergo frequent ramping, cycling and rapid load change operations. These transient regimes lead to several thermal, aerodynamic, and mechanical stresses that are very adverse for engine’s reliability, availability, overall health, and performance. To ensure resilient operation flexibility, a transient model of three shaft (GE LM 1600) variable geometry industrial gas turbine was developed using a commercially available tool GasTurb 12. To simulate load change phenomenon through acceleration and deceleration two kinds of fuel flow schedules were incorporated. Moreover, a variable inlet guide vane (VIGV) schedule and variable bleed schedule were also incorporated for low pressure compressor (LPC) to maintain stability and reliability. An optimization approach was employed to adapt the design point results with the OEM data. The design point and off design steady state results were compared with the already OEM performance data and showed good agreement. Additionally, the dynamic performance was also incorporated with a PID control scheme to assure a surge free compressor operation along with overall engine stability. It became evident from this research that, during transient operation, burner exit temperature depicts an overshoot while low pressure (LP) and high pressure (HP) spool speeds show a time delay that are deemed inappropriate as they can cause low cycle fatigue in hot parts such as combustor, HP turbine blades and casing. Hence, this transient model will help the future researchers in real time predictive condition monitoring and fault detection and diagnostics (FDD) models by developing advance reliability models.