[关键词]
[摘要]
以Bowman TG80微型燃气轮机为研究对象,采用模块化的建模方法,基于Modelica/Dymola平台建立了回热型微型燃气轮机的全工况动态热力系统及控制系统模型。根据试验数据对模型不同负荷下的发电效率进行了对比,并通过验证模型的启动、连续变负荷和停机动态过程,确定了多个关键技术参数。最后,分析了阶跃升降25%负荷过渡过程中不同回热器热惯性、转轴转动惯量、燃烧室容积惯性和控制器参数的系统动态响应。结果表明:不同负荷下的发电效率与试验值吻合良好,设计工况相对误差为0.11%,部分负荷最大相对误差为3.58%;根据停机工况确定的转动惯量和高温气道质量分别为0.016 kg•m2和5 kg,启动和连续变负荷过程与试验数据吻合较好;回热器热惯性和转轴转动惯量是影响系统响应特性的主要因素,回热器和转轴轻量化有利于实现更优的动态响应;合理的PID控制参数可以优化控制品质,比例增益和积分时间对控制效果的影响较大,微分时间作用不明显。
[Key word]
[Abstract]
Based on Modelica/Dymola platform,the thermodynamic system and control system model of Bowman TG80 micro gas turbine under total operating condition was established by using modular modeling method.Then,the power generation efficiencies of the model at different loads were compared with the experimental values,and several key technical parameters were determined by verifying the dynamic processes of the model including startup,continuous load change and shutdown.Finally,the dynamic responses of model in different recuperator thermal inertia,rotational shaft inertia,combustor volume inertia and controller parameters during the step change process of 25% load were analyzed.The results show that the power generation efficiencies at different loads are in good agreement with the experimental values,the relative error of design condition is 0.11%,and the maximum relative error at partial loads is 3.58%.According to shutdown process,rotational inertia and mass of hightemperature pipe are determined to be 0.016 kg•m2 and 5 kg respectively.The startup and continuous load change processes match well with the test data.In addition,the thermal inertia of recuperator and the rotational inertia of rotating shaft are the main factors affecting the response characteristics of the system.The lightweight of recuperator and shaft is beneficial to achieving better dynamic response.Reasonable parameters of PID can optimize the control quality in which the proportional gain and integral time play a great role in the control effect,while the effect of derivative time is not obvious.
[中图分类号]
TK47
[基金项目]
国家科技重大专项(2017-I-0009-0010)