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首页> 外文期刊>Archives of Electrical Engineering >Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives
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Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

机译：在无传感器间接矢量控制感应电动机驱动器中使用基于简化无功功率参考模型的速度估计的神经学习自适应系统

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摘要

This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named a€?Reactive Power based Model Reference Neural Learning Adaptive Systema€? (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.
机译：本文提出了一种基于无功功率的模型参考神经学习自适应系统（RP-MRNLAS）的新型速度估计器，用于无传感器间接矢量控制感应电动机驱动器。使用简化的无功功率方程的基于模型参考自适应系统（MRAS）的速度估计器是用于无传感器间接矢量控制感应电动机驱动器的速度估计方法之一。传统的MRAS速度估算器将PI控制器用于自适应机制。神经网络（NN）的非线性映射功能和强大的学习算法增加了NN在电力电子和驱动器中的应用。本文提出将神经学习算法用于基于MRAS的无功功率技术中的速度估计。提出的方案结合了简化的无功技术的优势和神经学习算法的能力，形成了一个名为“基于无功模型参考神经学习自适应系统”的方案。 （RP-MRNLAS）用于无传感器间接矢量控制感应电动机驱动器中的速度估算器。拟议的RP-MRNLAS在精度，积分器漂移问题和定子电阻方面与常用的基于转子磁通的MRNLAS（RF-MRNLAS）进行了比较，并通过Matlab / Simulink进行了验证。证明了RP-MRNLAS技术的优越性。

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