Full and Part-Load Performance Deterioration Analysis of Industrial Three-Shaft Gas Turbine Based on Genetic Algorithm

In this paper, common faults in main components of industrial three-shaft gas turbine are simulated through the nonlinear steady-state model, and the effects of these faults on the performance of all five main components and main measurement parameters are analyzed in both part and full load. Meanwhile, the sensitivity of these parameters to the location and type of fault and the fault severity in both part and full load is analyzed. In this study, in order to improve the accuracy of the non-linear steady-state model, the variable specific heat is considered with fitting formulas. In this model, the faults simulation is performed by changing the flow capacity and isentropic efficiency of each component via modification of the compressors and turbines characteristic curve. Meanwhile, a Genetic Algorithm (GA) based non-linear gas turbine steady-state performance simulation approach has been presented to best estimate the unknown component parameters. The fault simulation results in both part and full-load conditions indicate that by increasing the faults severity, the component performance parameters and the measurement parameters will deviate almost linearly from clean condition. Moreover, the sensitivity of these parameters varies with the location and type of the fault and also varies with the operating condition. Therefore, it can be used as the basis for selection of key parameters to identify the location and type of the fault. Furthermore, it also shows that considering the effects of load variations in design of a gas turbine diagnosis system is needed.