Real-Time Implementation of Extended Kalman Filter Observer With Improved Speed Estimation for Sensorless Control

被引：24

作者：

Jayaramu, Mohana Lakshmi ^{[1
]}

Suresh, H. N. ^{[1
]}

Bhaskar, Mahajan Sagar ^{[2
]}

Almakhles, Dhafer ^{[2
]}

Padmanaban, Sanjeevikumar ^{[3
]}

Subramaniam, Umashankar ^{[2
]}

机构：

[1] Malnad Coll Engn, Dept Elect & Elect Engn, Hassan 573202, India

[2] Prince Sultan Univ PSU, Coll Engn, Dept Commun & Networks, Renewable Energy Lab, Riyadh 11586, Saudi Arabia

[3] Aarhus Univ, Dept Business Dev & Technol, CTiF Global Capsule, DK-7400 Herning, Denmark

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Extended Kalman filter; inductor motor; real-coded-genetic algorithm; sensorless drives; speed estimator;

D O I：

10.1109/ACCESS.2021.3069676

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work presents an investigation on Improved Extended Kalman Filter (IEKF) performance for induction motor drive without a speed sensor. The performance of a direct sensorless vector-controlled system through simulation and experimental work is tested. The proposed observer focuses on estimating rotor flux and mechanical speed of rotor from the stationary axis components. Extended Kalman Filters' estimation performance depends on the system matrix's proper value (Q) and measurement error matrix (R). These matrices are assumed to be persistent and are calculated by the trial-and-error method. But, the operating environment affects these matrix values. They must be updated based on the prevailing operating conditions to get high speed and accurate estimates. The values of Q and R in the Improved EKF (IEKF) algorithm are obtained using the genetic algorithm. Besides, IEKF is incorporated to reduce in computational burden for real-time applications.

引用

页码：50452 / 50465

页数：14

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