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Hybrid artificial neural network for induction motor parameter estimation

机译:混合人工神经网络用于感应电动机参数估计

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Three-phase induction motor electric parameter estimation has been widely used to improve induction motor control performance. A precise match between electrical parameter values and estimated ones is imperative. A value deviation can make induction motor misbehave, which can cause motor overheating even instability. Parameter estimation can be achieved on-line or off-line way with a large number of methods developed to calculate magnetic flux, motor speed, rotor resistance and rotor time constant. These methods include observers, adaptive systems, spectral analysis and artificial intelligence such as neural networks and fuzzy logic. This paper is focused on a hybrid neural network proposed to obtain rotor resistance and speed values, using Texas Instrument development tools to improve a sensorless vector control scheme an improve motor performance.
机译:三相感应电动机电参数估计已被广泛用于改善感应电动机的控制性能。电参数值和估计值之间必须精确匹配。值的偏差会导致感应电动机出现异常,从而导致电动机过热甚至不稳定。参数估计可通过开发用于计算磁通量,电动机速度,转子电阻和转子时间常数的多种方法以在线或离线方式实现。这些方法包括观察者,自适应系统,频谱分析和人工智能(例如神经网络和模糊逻辑)。本文着重于提出一种获得转子电阻和速度值的混合神经网络,使用德州仪器(TI)开发工具来改进无传感器矢量控制方案并改善电动机性能。

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