An Improvement in Dynamic Behavior of Single Phase PM Brushless DC Motor Using Deep Neural Network and Mixture of Experts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254573" target="_blank" >RIV/61989100:27240/23:10254573 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10162182" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10162182</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3289409" target="_blank" >10.1109/ACCESS.2023.3289409</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Improvement in Dynamic Behavior of Single Phase PM Brushless DC Motor Using Deep Neural Network and Mixture of Experts

Popis výsledku v původním jazyce

Brushless DC motors play a vital role as a workhorse in many applications, especially home appliances. In the competitive world of the day, a brushless DC motor is a wise choice for many applications because of its high power density, a simple driving circuit, and high efficiency. Accordingly, demonstrating the feasibility of a new controller on this type of motor has undoubtedly paramount importance. Two methods of speed controllers, namely linear-quadratic regulator, and proportional-integral-derivative, are mixed using a mixture of experts (MoE) for a single-phase PM brushless DC external rotor motor. The dynamic model of the SP PM BLDC ER motor characterizes the behavior of the motor, involving cogging torque and electromotive force (EMF) gained from 2D finite element analyses. The motor is supplied by a pulse width modulation inverter with a constant voltage source. The results disclose that the SP PM BLDC performance is enhanced and more robust during load disturbance. ANSYS and MATLAB environments are used for obtaining finite element analysis and dynamic analysis of single-phase PM brushless DC external rotor motors, respectively. The merits of the proposed approach are validated through implementing a low-scale experimental setup.

Název v anglickém jazyce

An Improvement in Dynamic Behavior of Single Phase PM Brushless DC Motor Using Deep Neural Network and Mixture of Experts

Popis výsledku anglicky

Brushless DC motors play a vital role as a workhorse in many applications, especially home appliances. In the competitive world of the day, a brushless DC motor is a wise choice for many applications because of its high power density, a simple driving circuit, and high efficiency. Accordingly, demonstrating the feasibility of a new controller on this type of motor has undoubtedly paramount importance. Two methods of speed controllers, namely linear-quadratic regulator, and proportional-integral-derivative, are mixed using a mixture of experts (MoE) for a single-phase PM brushless DC external rotor motor. The dynamic model of the SP PM BLDC ER motor characterizes the behavior of the motor, involving cogging torque and electromotive force (EMF) gained from 2D finite element analyses. The motor is supplied by a pulse width modulation inverter with a constant voltage source. The results disclose that the SP PM BLDC performance is enhanced and more robust during load disturbance. ANSYS and MATLAB environments are used for obtaining finite element analysis and dynamic analysis of single-phase PM brushless DC external rotor motors, respectively. The merits of the proposed approach are validated through implementing a low-scale experimental setup.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

June 2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

64260-64271

Kód UT WoS článku

001021935000001

EID výsledku v databázi Scopus

2-s2.0-85163499389