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首页> 外文期刊>International Transactions on Electrical Energy Systems >Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer
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Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

机译：使用基于卡尔曼平滑的观测器实时实现三相感应电动机的无传感器间接磁场定向控制

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This paper proposes an indirect vector control scheme of an induction motor, using a Kalman smoothing-based observer to estimate speed. The observer is used to estimate the stator currents, rotor currents, and rotor mechanical speed. Observers based on conventional extended Kalman filters (CEKFs) depend on past output measurements of a system to predict its state variables at the next instant. In a smoothing-based observer, some future output measurements are also used to obtain a smoothed estimate of a past instant. The smoothed estimate, thus obtained, is used to predict and correct the states of the next instant. The performance of a CEKF-based observer largely depends on the proper determination of its measurement and process error covariance matrices. A trial and error method is usually engaged to arrive at these matrices. Smoothing helps to obtain a better state estimate compared with CEKF, with the same covariance matrices used in CEKF, which is obtained by trial and error. The improvement in estimation is mainly in the transient region. Estimates of low and zero speeds also show good improvement over those of CEKF. This betterment accomplished is without much increase in computational load. Experiments are conducted to compare the performance of a speed sensorless indirect vector control system with the observer, based on CEKF and smoothing for the same values of noise covariance matrices. The experiments conducted used various reference speeds, including low and zero speeds. Results show the superiority of the smoothing-based Kalman observer over CEKF-based observers.
机译：提出了一种基于卡尔曼平滑的观测器来估计速度的感应电动机的间接矢量控制方案。该观察器用于估计定子电流，转子电流和转子机械速度。基于常规扩展卡尔曼滤波器（CEKF）的观察者依赖于系统的过去输出测量值来预测其下一时刻的状态变量。在基于平滑的观察器中，某些将来的输出测量值也用于获取过去瞬间的平滑估计。如此获得的平滑估计被用于预测和校正下一瞬间的状态。基于CEKF的观察器的性能在很大程度上取决于对其测量值和过程误差协方差矩阵的正确确定。通常采用试错法得出这些矩阵。与CEKF相比，平滑化有助于获得更好的状态估计值，而CEKF中使用的协方差矩阵是通过反复试验获得的。估计的改进主要在过渡区域。低速和零速的估计值也显示出比CEKF更好的改进。实现的这种改进不会大大增加计算负荷。进行了实验，以基于CEKF和针对相同噪声协方差矩阵值的平滑对无速度传感器间接矢量控制系统和观察者的性能进行比较。进行的实验使用了各种参考速度，包括低速和零速。结果表明，基于平滑的卡尔曼观测器优于基于CEKF的观测器。

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