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    ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 >  Item 310904400/88393


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/88393


    Title: ANeural Network Based Data-Driven Nonlinear Model on Time- and Frequency-Domain Voltage-Current Characterization for Power Quality Study
    Authors: Chen, Cheng-I;Chen, Cheng-I;陳永欽;CHEN, YEONG-CHIN
    Contributors: 資訊工程學系
    Keywords: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
    Power quality, AC EAF, time and frequency domain, voltage-current characterization, harmonics, flickers.
    Date: 2015
    Issue Date: 2015-03-25 15:45:49 (UTC+8)
    Abstract: 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 proposed model in the time-domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasi-stationary and long-term features of AC EAF would be extracted via the frequency-domain phase of proposed model. From the comparison on the field measurement data, the performance of proposed model can be verified in the applications of PQ studies.
    Relation: 陳永欽
    Appears in Collections:[資訊工程學系] 期刊論文

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