Neural-network-based single-sided non-enwrapping power loss tester

被引:0
|
作者
Passadis, K [1 ]
Meydan, T [1 ]
Beckley, P [1 ]
机构
[1] Univ Wales Coll Cardiff, Cardiff Sch Engn, Wolfson Ctr Magnet Technol, Cardiff CF24 0YF, S Glam, Wales
关键词
on-line tester; artificial neural network; hidden nodes;
D O I
10.1016/S0304-8853(02)00913-7
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
It is preferable to be able to assess the power loss of electrical steels during production. When a single-sided tester is used, flux sensing is undertaken from one side only and hence some leakage flux above the strip may not captured by the sensing coils. Therefore, the disadvantage of a single-sided non-enwrapping tester lies in the measurement of the flux density in the material. A neural network was successfully used to "predict" the correct level of flux density for accurate assessment of power loss. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:385 / 387
页数:3
相关论文
共 50 条
  • [1] Probabilistic neural-network-based protection of power transformer
    Tripathy, M.
    Maheshwari, R. P.
    Verma, H. K.
    IET ELECTRIC POWER APPLICATIONS, 2007, 1 (05) : 793 - 798
  • [2] OpeNPDN: A Neural-Network-Based Framework for Power Delivery Network Synthesis
    Chhabria, Vidya A.
    Sapatnekar, Sachin S.
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 41 (10) : 3515 - 3528
  • [3] NEURAL-NETWORK-BASED ALGORITHM FOR POWER TRANSFORMER DIFFERENTIAL RELAYS
    BASTARD, P
    MEUNIER, M
    REGAL, H
    IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1995, 142 (04) : 386 - 392
  • [4] Neural-network-based Power System State Estimation with Extended Observability
    Guanyu Tian
    Yingzhong Gu
    Di Shi
    Jing Fu
    Zhe Yu
    Qun Zhou
    JournalofModernPowerSystemsandCleanEnergy, 2021, 9 (05) : 1043 - 1053
  • [5] Neural-network-based Power System State Estimation with Extended Observability
    Tian, Guanyu
    Gu, Yingzhong
    Shi, Di
    Fu, Jing
    Yu, Zhe
    Zhou, Qun
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2021, 9 (05) : 1043 - 1053
  • [6] Improved Modulation-Domain Loss for Neural-Network-based Speech Enhancement
    Vuong, Tyler
    Stern, Richard M.
    INTERSPEECH 2022, 2022, : 206 - 210
  • [7] Single-Sided Compensation Network Design Method for Capacitive Power Transfer System Considering Coupling Variation
    Choi, Sunghyuk
    Chung, Euihoon
    Lim, Gyu Cheol
    Hong, Jin-Su
    Choe, Gyu-Yeong
    Ha, Jung-Ik
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2024, 60 (04) : 6351 - 6365
  • [8] Neural-network-based inverse control method for active power filter system
    Wu, Jianhua
    Pang, Hali
    Xu, Xinhe
    PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL, 2006, : 561 - +
  • [9] Power supply quality improvement with a SOFC plant by neural-network-based control
    Jurado, F
    JOURNAL OF POWER SOURCES, 2003, 117 (1-2) : 75 - 83
  • [10] NEURAL-NETWORK-BASED TRANSIENT STABILITY ASSESSMENT OF ELECTRIC-POWER SYSTEMS
    MARPAKA, DR
    BODRUZZAMAN, M
    DEVGAN, SS
    AGHILI, SM
    KARI, S
    ELECTRIC POWER SYSTEMS RESEARCH, 1994, 30 (03) : 251 - 256