首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive
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Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

机译:基于人工神经网络的感应电动机驱动器在线速度和转子电阻估算器提高了无传感器FOC性能

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摘要

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.
机译:三相感应电动机驱动器在工业应用中的高性能过程中要求高精度。磁场定向控制是感应电动机中最常用的控制方案之一,其功能基于电动机的电参数估计。这些参数使电机驱动器无法正常工作,因为这些电气参数值会在低速,温度变化(尤其是在负载和占空比变化)下发生变化。本文的重点是在标准FOC方案中添加了CMAC-ADALINE模块的实时和在线电气参数,以改善IM驱动器性能并忍受驱动器和感应电动机的使用寿命。使用了两种神经网络结构:一个用于估计转子速度,另一个用于估计感应电动机的转子电阻。

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