Rotor Position Estimation Strategy Using Artificial Neural Network for a Novel Design Transverse Flux Machine

被引：1
|
作者：
Turker, Cigdem Gundogan [1 ]
Kuyumcu, Feriha Erfan [1 ]
机构：
[1] Kocaeli Univ, Dept Elect & Elect Engn, Kocaeli, Turkey
来源：
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY | 2015年 / 10卷 / 05期
关键词：
Artificial neural network; Rotor position estimation; Transverse flux machine; RELUCTANCE MOTOR-DRIVES; OBSERVER; SENSOR;
D O I：
10.5370/JEET.2015.10.5.2009
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The E-Core Transverse Flux Machine is a different design of transverse flux machines combined with reluctance principle. Determination of the rotor position is important for the movement of the ETFM by switching the phase currents in synchronism with the inductance regions of the stator windings. It is the first time that rotor position estimation based on Artificial Neural Network (ANN) is purposed to eliminate the position sensor for the ETFM. Simulation and experimental tests are demonstrated for the feasibility of the proposed estimation algorithm for the exercise bike application of the ETFM.
引用
收藏
页码：2009 / 2017
页数：9
相关论文
共 50 条
  • [41] Artificial neural network-based method for overhead lines magnetic flux density estimation
    Alihodzic, Ajdin
    Mujezinovic, Adnan
    Turajlic, Emir
    JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2024, 75 (03): : 181 - 191
  • [42] Fatigue design of leaf spring using artificial neural network
    Jung, Wonseok
    Bae, Dongho
    Song, Geewook
    Hyun, Jungseob
    Kim, Bumshin
    Experimental Mechanics in Nano and Biotechnology, Pts 1 and 2, 2006, 326-328 : 1083 - 1086
  • [43] Estimation of tunnel support pattern selection using artificial neural network
    Jiankang Liu
    Yujing Jiang
    Sodai Ishizu
    Osamu Sakaguchi
    Arabian Journal of Geosciences, 2020, 13
  • [44] Energy demand estimation of South Korea using artificial neural network
    Geem, Zong Woo
    Roper, William E.
    ENERGY POLICY, 2009, 37 (10) : 4049 - 4054
  • [45] Construction equipment productivity estimation using artificial neural network model
    Ok, Seung C.
    Sinha, Sunil K.
    CONSTRUCTION MANAGEMENT AND ECONOMICS, 2006, 24 (10) : 1029 - 1044
  • [46] Estimation of tunnel support pattern selection using artificial neural network
    Liu, Jiankang
    Jiang, Yujing
    Ishizu, Sodai
    Sakaguchi, Osamu
    ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (09)
  • [47] NEW APPROACH FOR ESTIMATION OF POLLUTANT LOAD BY USING ARTIFICIAL NEURAL NETWORK
    Liu, Minghuan
    Yoshimi, Kenji
    Ishikawa, Tadaharu
    Kudo, Kentaro
    ADVANCES IN WATER RESOURCES AND HYDRAULIC ENGINEERING, VOLS 1-6, 2009, : 683 - +
  • [48] RUNOFF ESTIMATION IN URBAN CATCHMENT USING ARTIFICIAL NEURAL NETWORK MODELS
    Bakhshaei, Mahsa
    Ahmadi, Hassan
    Motamedvaziri, Baharak
    Najafi, Payam
    SISTEMAS & GESTAO, 2020, 15 (02): : 170 - 180
  • [49] The Estimation of Formation Permeability in a Carbonate Reservoir Using an Artificial Neural Network
    Yeganeh, M.
    Masihi, M.
    Fatholahi, S.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (10) : 1021 - 1030
  • [50] FREEWAY TRAVEL TIME ESTIMATION USING ARTIFICIAL NEURAL NETWORK (ANN)
    Jiang, Zifeng
    Zhang, Shengrui
    Ju, Yongfeng
    TRANSPORTATION PLANNING AND MANAGEMENT IN THE 21ST CENTURY, 2001, : 378 - 384
12345