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

Md Fahdul Wahab Chowdhury, M. Schoen, Ji-chao Li
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引用次数: 1

Abstract

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.
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基于Moore-Greitzer模型的单级轴流压气机参数辨识及模糊控制器设计
本文提出了一种解决轴流压气机长期存在的不稳定问题的新方法。失速和浪涌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起来,形成了Moore-Greitzer (MG)模型。该模型建立在压缩机特定特性的假设基础上。然而,特性的参数取决于压缩机的几何形状和其他因素。由于各压气机的特性不同,MG模型的特征方程参数不相同,难以估计。因此,MG模型不适合提供压缩机的具体动力学,而是描述压缩系统的一般流体动力学。因此,在不了解具体特性的情况下,使用MG模型解决流体流动控制问题是困难的。为了解决这一问题,本文提出了一种新的方法,该方法允许仅使用实验数据提取压缩机的特定特征参数。该方法采用了基于遗传算法的优化技术。利用MG模型的仿真数据和单级轴流压气机试验系统的实验数据对该方法进行了验证。然后利用提取的参数,设计了针对特定单级轴流压气机的模糊控制器。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,以保持特定的工作点。控制器的输入是期望工作点与实际工作点之间的误差。压缩机在没有控制的情况下运行,在最大压升系数时变得不稳定。系统的工作点设置在最大压升系数和相应的质量流量系数的正下方。从压升和质量流量系数的模拟结果来看,压缩机在新的工作点上可以安全运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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