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Parameter Identification and Fuzzy Logic Controller Design for a One-Stage Axial Flow Compressor System based on Moore-Greitzer Model

机译:基于Moore-Greitzer模型的一阶段轴流压缩机系统参数辨识和模糊逻辑控制器设计

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This paper presents a novel approach to mitigate a long-standing instability problem in axial flow compressors. The instabilities known as stall and surge limits the operating range of these systems. Moore and Greitzer combined their work on modelling axial compressor systems, resulting into the Moore-Greitzer (MG) model. This model is built on the assumption of a specific compressor characteristic. However, the parameters of the characteristics are dependent on the compressor geometry and other factors. As each compressor exhibits different characteristics, the parameters of the characteristic equation of the MG model are not the same and difficult to estimate. Thus, the MG model is not suitable to provide a compressor's specific dynamics - rather it describes the general fluid dynamics of a compression system. Hence, addressing the fluid flow control problem using the MG model is difficult without the knowledge of the specific characteristics. In order to solve this problem, a new approach is proposed in this paper that allows for the extraction of a compressor's specific characteristic parameters using only experimental data. This approach employs a genetic algorithm-based optimization technique. The proposed approach is tested using simulated data from the MG model and experimental data from a one-stage axial compressor test system. The extracted parameters are then utilized to design a fuzzy logic controller for the specific one-stage axial compressor. The objective of the controller is to regulate the mass flow rate by varying the throttle of the compressor in order to maintain a specific operating point. The input into the controller is the error between the desired operating point and the actual operating point. The compressor - operating without control - becomes unstable at the maximum pressure rise coefficient. The operating point of the system is set just below the maximum pressure rise coefficient and the corresponding mass flow coefficient. From the simulation result of the pressure rise and mass flow coefficient, it is found that the compressor can be operated safely at this new operating point.
机译:本文提出了一种新颖的方法来减轻轴流式压缩机中长期存在的不稳定性问题。被称为失速和喘振的不稳定性限制了这些系统的工作范围。 Moore和Greitzer将他们在轴向压缩机系统建模方面的工作结合在一起,从而形成了Moore-Greitzer(MG)模型。该模型是基于特定压缩机特性的假设而建立的。然而,特性的参数取决于压缩机的几何形状和其他因素。由于每个压缩机表现出不同的特性,因此MG模型的特性方程式的参数不相同且难以估算。因此,MG模型不适合提供压缩机的特定动力-而是描述了压缩系统的一般流体动力。因此,在不了解特定特性的情况下,很难使用MG模型解决流体流量控制问题。为了解决这个问题,本文提出了一种新方法,该方法允许仅使用实验数据来提取压缩机的特定特征参数。该方法采用基于遗传算法的优化技术。使用来自MG模型的模拟数据和来自一级轴向压缩机测试系统的实验数据对提出的方法进行了测试。然后,将提取的参数用于为特定的一级轴向压缩机设计模糊逻辑控制器。控制器的目的是通过改变压缩机的节流阀来调节质量流量,以维持特定的工作点。输入到控制器中的是期望工作点与实际工作点之间的误差。在无控制的情况下运行的压缩机在最大升压系数下变得不稳定。系统的工作点设置为刚好低于最大压力升高系数和相应的质量流量系数。从压力升高和质量流量系数的模拟结果可以发现,压缩机可以在这个新的工作点安全运行。

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