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首页> 外文期刊>International Journal of Power Electronics and Drive Systems >A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives
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A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

机译：基于鲁棒EKF的无传感器感应电动机驱动器速度估计和模糊优化技术

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The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
机译：感应电机的速度估计技术已成为一项不平凡的任务。为了精确且准确地估计感应电动机的速度，需要最佳状态估计器。本文使用递归的最佳状态估计器处理感应电动机驱动器的性能分析。该技术使用全阶状态空间扩展卡尔曼滤波器（EKF）模型，在该模型中可以估算转子磁通，转子速度和定子电流。感应电动机的主要挑战发生在非常低​​的速度和接近零的速度下。在这种情况下，在使用同步旋转的参考系时，关于定子侧的转子参数的信息变得不可观察。为了克服这种丢失的耦合效应，EKF观测器在每个采样周期内将非线性参数线性化，并同时估计状态和机器参数。对该算法进行了调整，以获得估计速度的最小误差。使用非线性模糊控制器进一步优化发现的任何错误，以提高驱动器的性能。

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