【摘要】 燃气轮机机组测量数据存在随机误差，导致热力模型的仿真结果与机组实际运行的测量结果存在偏差，气路诊断存在局限性。为了更准确地诊断燃气轮机的气路性能退化，本文提出了一种基于数据协调的燃气轮机气路诊断方法，构建数据协调方程、仿真值与测量值均方误差相结合的目标函数，以数据协调值代替原本的仿真值，采用粒子群优化算法获得部件特性参数偏移的精确解。利用仿真模拟的退化故障案例，开展了气路诊断仿真试验。结果表明：仿真试验中退化因子的最大相对偏差小于0.96%,优于传统无数据协调气路诊断方法的最大相对偏差值3.93%。更多还原

【Abstract】 There are random errors in the measurement data of gas turbine units, resulting in a certain deviation between the simulation results of the thermal model and the measurement results of the actual operation of the unit, and the limitations of gas path diagnosis. To effectively diagnose the performance degradation of gas path of the gas turbine, this paper proposed a gas path diagnosis method based on data coordination. An objective function combining the data-coordinated equations with the mean-square error of the simulated and measured values was constructed, and the original simulated values were replaced by the data-coordinated values. The particle swarm optimization algorithm was used to obtain the accurate solution of the component characteristic parameter offset. The simulation test of gas path diagnosis was carried out based on the simulated degradation fault cases. The results show that the maximum relative deviation of degradation factor in the simulation test is less than 0.96%, which is 3.93% better than that of the traditional gas path diagnosis method without data coordination.更多还原