Performance Prediction and Analysis of Micro Gas Turbine Running on Natural Gas and Hydrogen Hybrid Fuel Based on Data-driven Model

LI Quan; ZHANG Qian; ZHANG Lei

doi:10.13334/j.0258-8013.pcsee.251585

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Performance Prediction and Analysis of Micro Gas Turbine Running on Natural Gas and Hydrogen Hybrid Fuel Based on Data-driven Model

更新时间：2026-10-10

- Performance Prediction and Analysis of Micro Gas Turbine Running on Natural Gas and Hydrogen Hybrid Fuel Based on Data-driven Model
- Vol. 46, Issue 17, Pages: 6324-6334(2026)
- 作者机构：
  
  华北电力大学动力工程系,河北省,保定市,071003
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.251585
  CLC：
- Published：2026
- 稿件说明：
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LI Quan, ZHANG Qian, ZHANG Lei. Performance Prediction and Analysis of Micro Gas Turbine Running on Natural Gas and Hydrogen Hybrid Fuel Based on Data-driven Model[J]. 2026, 46(17): 6324-6334.
DOI：

LI Quan, ZHANG Qian, ZHANG Lei. Performance Prediction and Analysis of Micro Gas Turbine Running on Natural Gas and Hydrogen Hybrid Fuel Based on Data-driven Model[J]. 2026, 46(17): 6324-6334. DOI： 10.13334/j.0258-8013.pcsee.251585.

摘要

微型燃气轮机(micro gas turbine，MGT)以其高效地满足离网发电、能源中心和智能微电网等小范围场景的冷热电联供需求而得到广泛应用。该研究聚焦于天然气与氢气 (hydrogen，H2)混合燃料在MGT系统中的应用，提出并验证一种基于物理机制和数据驱动相结合的MGT建模方法。采用多种学习算法对MGT关键性能参数进行预测，发现装袋回归和随机森林回归算法具备较高精度。评价指标R2和MAPE均在0.75~0.85范围内，预测方案合理；通过田灰法对混合燃料比例及系统工况进行多目标优化，实现发电效率提升与能耗降低的协同控制；在低温环境下(T1＜ 288.15 K)运行且H2体积分数为25%或50%时，系统性能最优。研究为混合燃料MGT在离网电力系统中的应用提供理论支撑与优化路径。

Abstract

Micro gas turbine (MGT) is widely used for its high efficiency in meeting the demand of combined cooling

heating

and power in small-scale scenarios (e.g.

off-grid power generation

energy centers and smart microgrids

etc.). This study focuses on the application of natural gas (NG) and hydrogen (H2) hybrid fuel in MGT systems

proposing and validating a modeling approach for MGT that integrates physical mechanisms with data-driven methods. Various learning algorithms are used to predict the key performance parameters of MGT

and bagging regression (BR) and random forest regression (RFR) algorithms are found to have high accuracy. The evaluation indicators R2 and MAPE are both within the range of 0.75 to 0.85. Through the Taguchi-grey method

this paper employs a multi-objective optimization of the fuel mixture ratio and system operating conditions to achieve the synergistic control of power generation efficiency improvement and energy consumption reduction. The system performance is optimized at low temperature (T1＜ 288.15 K) with hydrogen volume fraction (HVF) of 25% or 50%. The study provides theoretical support and an optimization path for the application of hybrid fuel MGT in off-grid power systems.

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