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首页> 外文期刊>WSEAS Transactions on Power Systems >Optimization of droop setting using Genetic Algorithm for Speedtronic Governor controlled Heavy Duty Gas Turbine Power Plants
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Optimization of droop setting using Genetic Algorithm for Speedtronic Governor controlled Heavy Duty Gas Turbine Power Plants

机译:Speedtronic调速器控制的重型燃气轮机发电厂的遗传算法优化下垂设置

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摘要

Biomass is identified as one of the major renewable energy sources for electrical power generation. Heavy duty gas turbine engines are preferred for clean and efficient power generation. An extensive literature survey reveals that the governor droop setting of the heavy duty gas turbines varies from 2 percentage to 10 percentage. But it needs to be optimized for analyzing the dynamic response of heavy duty gas turbine plants in grid connected operation. An attempt has been made in this paper to optimize the speedtronic governor droop setting of all heavy duty gas turbine plants ranging from 18.2MW to 102.6MW using genetic algorithm. Step response of all heavy duty gas turbine plants with the genetic algorithm based droop setting are obtained using MATLAB/Simulink. On comparing the simulation results based on all time domain specifications and performance index criteria, it is witnessed that the genetic algorithm based droop setting yield optimal transient and steady state responses than the previous findings using SYSTAT software. Therefore the genetic algorithm based droop setting is identified as the optimal droop setting for all heavy duty gas turbine plants in grid connected operation.
机译:生物质被认为是用于发电的主要可再生能源之一。重型燃气涡轮发动机是清洁和高效发电的首选。大量的文献调查显示,重型燃气轮机的调速器下垂设置在2%到10%之间变化。但是需要对其进行优化以分析并网运行中重型燃气轮机的动态响应。本文尝试使用遗传算法优化从18.2MW到102.6MW的所有重型燃气轮机电厂的speedtronic调速器下垂设置。使用MATLAB / Simulink获得了基于遗传算法下垂设置的所有重型燃气轮机设备的阶跃响应。通过比较基于所有时域规范和性能指标标准的仿真结果,可以看出,基于遗传算法的下垂设置比使用SYSTAT软件的先前发现可产生最佳的瞬态和稳态响应。因此,基于遗传算法的下垂设置被确定为并网运行中所有重型燃气轮机电厂的最佳下垂设置。

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