基于人工神经网络的异步电动机容错调速

K. Zeb, Farhana, C. A. Mehmood, B. Khan, S. M. Ali, Ayesha, A. Jadoon, W. Uddin
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引用次数: 3

摘要

本文成功地开发并研究了基于人工神经网络(ANN)的间接矢量控制(IVC)三相异步电动机(IM)驱动鲁棒速度控制策略的实现。在Matlab/Simulink中,用同步旋转参考系中的dq对IVC进行建模。该设计的主要目的是实现对负载干扰、速度变化、参数不确定性和电气故障的鲁棒性。将上述控制方法与传统的调谐PI控制方法进行了性能比较。仿真结果表明,与传统PI控制方案相比,该方法在不同工况下对超调量、欠调量、上升时间、下降时间和抖振都有较好的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault tolerant speed regulation of Induction Motor using Artifical Neural Network
This paper successfully develops and investigates the implementation of Artificial Neural Network (ANN) based robust speed control strategy for Indirect Vector Control (IVC) three phase Induction Motor (IM) drive. The IM is modeled in terms of dq in synchronously rotating reference frame for IVC in Matlab/Simulink. The main purpose of the proposed design is to accomplish robustness for load disturbances, speed variation, parameter uncertainties, and electrical faults. The performance of aforesaid control technique is compared with that of conventional tuned PI control scheme. Simulation results of the ANN guarantee effectiveness and robustness regarding overshoot, undershoot, rise time, fall time and chattering for different operating condition in comparison to traditional PI control scheme.
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