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    Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106377


    Title: A neural-network-based data-driven nonlinear model on time- and frequency-domain voltage-current characterization for power-quality study
    Authors: 陳正一;Chen, Cheng-I;Chen, Yeong-Chin
    Contributors: 資訊電機學院電機工程學系
    Keywords: Alternating current electric arc furnace (AC EAF);Data models;flickers;Frequency-domain analysis;harmonics;Load modeling;Minimization;power quality (PQ);time and frequency domain;Time-domain analysis;Voltage measurement;voltage-current characterization
    Date: 2015-06-01
    Issue Date: 2026-04-23 13:19:09 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers Inc.;IEEE
    Abstract: 摘要: An accurate model of nonlinear load is important for the evaluation of power quality (PQ). Among different PQ disturbance sources, alternating current electric arc furnace (AC EAF) is one of most complicated and serious loads. To provide effective operation prediction of AC EAF, a data-driven modeling approach based on time- and frequency-domain voltage-current (v -i) characterization is proposed in this paper. With the prediction of the proposed model in the time domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasistationary and long-term features of AC EAF would be extracted via the frequency-domain phase of the proposed model. From the comparison on the field measurement data, the performance of the proposed model can be verified in the applications of PQ studies.
    其他題名: TPWRD
    出版者: IEEE
    出版日期: 2015-06
    出處: IEEE Transactions on Power Delivery, 2015-06, Vol.30 (3), p.1577-1584
    資源來源: IEEE Xplore Digital Library (LUT)
    識別號: ISSN: 0885-8977
    識別號: EISSN: 1937-4208
    識別號: DOI: 10.1109/TPWRD.2015.2394359
    識別號: CODEN: ITPDE5
    Appears in Collections:[Department of Electrical Engineering] journal & Dissertation

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