The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications

In this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, an...

Full description

Saved in:
Bibliographic Details
Published in:Arabian journal for science and engineering (2011) 2022-03, Vol.47 (3), p.2639-2648
Main Authors: Unlersen, Muhammed Fahri, Balci, Selami, Aslan, Muhammet Fatih, Sabanci, Kadir
Format: Article
Language:English
Subjects:
Artificial intelligence
Brushless motors
Computer architecture
Computer simulation
D C motors
Dynamic response
Engineering
Finite element method
Humanities and Social Sciences
multidisciplinary
Parameters
Permanent magnets
Pulse duration
Recurrent neural networks
Research Article-Electrical Engineering
Rotor speed
Science
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, and motor power). The rotor speed is defined as the output parameter to determine the dynamic response, and 600 parametric data are obtained according to the simulation studies. In order to estimate the rotor speed of the BLDC motor modeled using artificial intelligence (AI), an advanced recurrent neural network architecture known as bidirectional long short-term memory has been designed. Rotor speed is successfully estimated with the proposed architecture, and as a result, the mean absolute percentage error value is calculated as 3.25%. These results show that the analysis of BLDC motor parameters can be determined quickly with the proposed AI method without long-running simulations.
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-05700-w