Multi-objective optimization of IGV position in a heavy-duty gas turbine on part-load performance
Multi-objective optimization of IGV position in a heavy-duty gas turbine on part-load performance
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DOI：
10.1016/j.applthermaleng.2017.07.091
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发表时间：
2017-10
影响因子：
6.4
通讯作者：
A. Mehrpanahi;G. Payganeh
中科院分区：
文献类型：
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作者：
A. Mehrpanahi;G. Payganeh
伊朗60%以上的发电量依赖于重型燃气涡轮机发电厂的技术特性。通过定位进口导叶（IGV）改变压缩机空气流量的能力已被认为是在不同的操作条件下提高所述燃气涡轮机功率的技术经济质量。在本研究中，基于操作数据，使用在各种条件下开发的工业代码的输出进行系统建模。该模型是通过热力学方程和线性回归函数导出的。随后，采用多目标遗传算法优化IGV位置，在40%-100%（标称）负荷的发电范围内优化目标函数。热效率和发电成本，其中包括在伊朗的能源管理系统的主要决策因素，被选为目标函数。
More than 60% of the power generated in Iran depends on the technical characteristics of heavy-duty gas turbine power plants. The ability to make changes in the amount of compressor air flow via positioning Inlet Guide Vanes (IGV) has been considered to improve the techno-economic quality of the mentioned gas turbine power in different operational conditions. In this study, system modeling was conducted based on operational data, using the outputs of the industrial code developed in various conditions. This model was derived via thermodynamic equations and linear regression functions. Subsequently, multi-objective genetic algorithm optimization of IGV position was employed to optimize the objective functions in the power generation range of 40%–100% (nominal) load. Thermal efficiency and electricity generation cost, which cover the main decision factors in energy management systems in Iran, were selected as the objective functions.