TY - GEN
T1 - Comparing Particle Filter, Adaptive Extended Kalman Filter and Disturbance Observer for Induction Motor Speed Estimation
AU - Indriawati, Katherin
AU - Wijaya, Febry Pandu
AU - Mufit, Choirul
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/6
Y1 - 2020/10/6
N2 - Electric motors in industry are required to operate at a certain speed with varying loads. In general, speed and position information can be measured using an encoder or tachogenerator on a motor shaft, but it will affect the cost and complexity factors. To reduce the cost factor and increase the reliability and robustness of the system, this information can be estimated, known as speed sensorless. This paper discusses three model-based estimation algorithms: Disturbance Observer (DO), Particle Filter (PF), and Adaptive Extended Kalman Filter (AEKF). The main topic in this paper is to evaluate these algorithms in estimating induction motor speed. Based on the performance testing results of the three algorithms, namely using root mean square error (RMSE) value, it was found that the DO algorithm is better than compared to the AEKF and PF algorithms.
AB - Electric motors in industry are required to operate at a certain speed with varying loads. In general, speed and position information can be measured using an encoder or tachogenerator on a motor shaft, but it will affect the cost and complexity factors. To reduce the cost factor and increase the reliability and robustness of the system, this information can be estimated, known as speed sensorless. This paper discusses three model-based estimation algorithms: Disturbance Observer (DO), Particle Filter (PF), and Adaptive Extended Kalman Filter (AEKF). The main topic in this paper is to evaluate these algorithms in estimating induction motor speed. Based on the performance testing results of the three algorithms, namely using root mean square error (RMSE) value, it was found that the DO algorithm is better than compared to the AEKF and PF algorithms.
KW - Adaptive Extended Kalman Filter
KW - Disturbance Observer
KW - Induction Motor
KW - Particle Filter
UR - http://www.scopus.com/inward/record.url?scp=85098738905&partnerID=8YFLogxK
U2 - 10.1109/ICITEE49829.2020.9271744
DO - 10.1109/ICITEE49829.2020.9271744
M3 - Conference contribution
AN - SCOPUS:85098738905
T3 - ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering
SP - 57
EP - 62
BT - ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Information Technology and Electrical Engineering, ICITEE 2020
Y2 - 6 October 2020 through 8 October 2020
ER -