The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Bldc Motor, Long Short-Term Memory, Recurrent Neural Network, Rotor Speed, Parameter Estimation
Kaynak
Arabian Journal For Science And Engineering
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
47
Sayı
3
