Author:
Wei Dong,Zhao Ruochen,Xiong Yaxuan,Zuo Mingxin, , ,
Abstract
In gas transmission, the regulator needs to adjust the gas pressure from high to low. The pressure energy can be then recovered by an expander, and the expander can drive a generator to produce electricity. However, the gas pressure regulator system and generator torque process often present difficult adjustment of PI parameters, and strong non-linearity of the hysteresis comparator and switching table in the traditional direct torque control (DTC) cause difficulties in the controller design and lead to large fluctuations of the generator torque. This paper designs a model predictive controller (MPC) for the gas pressure regulator process to reduce generator torque fluctuations. Simultaneously, a fuzzy PI controller is designed for the generator rotational speed process, and an MPC controller is exploited for the torque process; they operate in a cascaded manner. The fuzzy PI controller is used to calculate the torque set point. And the MPC controller is designed to obtain the optimal voltage vector of the generator for improving control performance through time delay compensation. The simulation experimental results highlight that the fluctuation of the regulator outlet gas pressure is reduced by 7.9% and 8.1%, and the output torque range is reduced by 3.4% and 2.1% compared with the traditional PI control and fuzzy PI control, respectively. The generator torque fluctuation range is reduced by 82.3%, the rotational speed fluctuation range is reduced by 76.9%, and the three-phase current fluctuation range is reduced by 76.6% compared with the traditional DTC.
Funder
Beijing Municipal Universities
China Ministry of Hosing and Urban-Rural Development
Beijing University of Civil Engineering and Architecture
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
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