To minimize the simulated performance error of gas turbines, traditional adaptive methods are mainly concerned with the tuning of cycle design point and component maps given by the manufacturer, usually ignoring the fact that performance at cycle design point may not match the field data due to the deviation between test-rig conditions and field conditions. In this paper, a new tuning scheme of the cycle reference point is proposed to minimize the simulated errors simultaneously at design point and off-design points. The scheme is composed of a backward iteration algorithm and a genetic algorithm. During the backward iteration, the field data at the maximum operating condition is selected to obtain the initial cycle reference point with several undetermined parameters. Further, the genetic algorithm is used to optimize the undetermined parameters. The accuracy of the proposed method was validated by the simulated performance of a PGT25+ gas turbine.

为了使燃气轮机的模拟性能误差最小化，传统的自适应方法主要涉及制造商给出的循环设计点和零件图的调整，通常忽略了以下事实：由于温度变化，循环设计点的性能可能与现场数据不匹配。试验台条件和现场条件之间的偏差。本文提出了一种新的循环参考点调整方案，以最小化设计点和非设计点的仿真误差。该方案由反向迭代算法和遗传算法组成。在向后迭代过程中，选择处于最大运行条件的现场数据以获得具有几个不确定参数的初始循环参考点。此外，遗传算法用于优化不确定的参数。