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感应电动机的实时速度估计：使用电压 - 电流传感器对扩展卡尔曼滤波器进行自动调谐

Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage-Current Sensors.

作者信息

Miloud Ines, Cauet Sebastien, Etien Erik, Salameh Jack P, Ungerer Alexandre

机构信息

Université de Poitiers, ISAE-ENSMA Poitiers, LIAS, 86073 Poitiers, France.

Chauvin Arnoux, 74940 Annecy, France.

出版信息

Sensors (Basel). 2024 Mar 7;24(6):1744. doi: 10.3390/s24061744.

DOI:10.3390/s24061744
PMID:38544007
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10975228/
Abstract

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets (using voltage-current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally.

摘要

本文旨在使用扩展卡尔曼滤波器（EKF）实现感应电动机的实时最优速度估计。速度估计对于电机电流特征分析（MCSA）中的故障诊断至关重要。通过探索EKF算法的噪声协方差矩阵估计来获得估计精度。噪声协方差矩阵使用改进的子空间模型识别方法确定。为了实现这一目标，该方法将从可用的输入-输出数据集（使用电压-电流传感器）导出的确定性系统估计模型与卡尔曼滤波器方程中使用的离散时间状态空间表示进行比较。这种比较导致模型不确定性的确定，随后将其表示为噪声协方差矩阵。基于感应电动机的五阶非线性模型，使用优化的EKF算法估计转子速度，并对该算法进行了实验测试。

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