为了提高燃气轮机模型的整体仿真精度，利用最小二乘法、BP神经网络法和XGboost算法对燃气轮机压气机特性曲线进行拟合。以某双轴燃气轮机为研究对象，将3种压气机特性曲线拟合结果应用到燃气轮机的整体仿真中。结果表明：在压气机特性曲线预测中，XGboost和BP神经网络对压气机特性曲线的拟合精度最高，最小二乘法的拟合精度相对较低；在燃气轮机的整体仿真中，3种方法对燃机截面参数的计算都能够达到很好的精度。其中基于XGboost算法的燃气轮机仿真模型对燃机截面参数的计算精度最高，最高平均相对误差仅为1.15%。压气机特性曲线的拟合精度直接影响燃气轮机截面参数的计算误差，利用XGboost算法和BP神经网络法对压气机特性曲线拟合能够为提高燃气轮机模型精度提供参考。
In order to improve the overall simulation accuracy of gas turbine model, the least square method, BP neural network method and XGboost algorithm are used to fit the compressor characteristic curve. Taking a double shaft gas turbine as the research object, the fitting results of three compressor characteristic curves are applied to the overall simulation of gas turbine.The results show that the accuracy of the XGboost and BP neural network is the highest in the prediction of compressor characteristic curve, and the least square method is relatively low; In the overall simulation of gas turbine, the three methods can achieve good accuracy in the calculation of cross-section parameters of gas turbine. The gas turbine simulation model based on XGboost algorithm has the highest calculation accuracy of gas turbine section parameters, and the highest average relative error is only 1.15%. Because the fitting accuracy of compressor characteristic curve directly affects the calculation error of gas turbine section parameters, the XGboost algorithm and BP neural network method can provide reference for improving the accuracy of gas turbine model.
2023,45(1): 129-134 收稿日期：2021-09-03
DOI：10.3404/j.issn.1672-7649.2023.01.023
分类号：TK474.8
基金项目：上海市“科技创新行动计划”地方院校能力建设专项资助项目（19020500700）
作者简介：张烨(1996-),男,硕士研究生,研究方向为燃气轮机建模与故障诊断
