Reactive power control of single-stage three-phase photovoltaic system during grid faults using recurrent fuzzy cerebellar model articulation neural network

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/108084

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108084

Title:	Reactive power control of single-stage three-phase photovoltaic system during grid faults using recurrent fuzzy cerebellar model articulation neural network
Authors:	林法正;Lin, Faa-Jeng;Lee, Hsuan-Yu;Lu, Kuang-Chin
Contributors:	資訊電機學院電機工程學系
Keywords:	Active control;Algorithms;Analysis;Control systems;Distance learning;Electric utilities;Electricity distribution;Fuzzy;Laws, regulations and rules;Mathematical models;Methods;Neural networks;Online instruction;Photovoltaic cells;Reactive power;Regulation;Solar cells;Solar energy;Solar energy industry
Date:	2014-07-27
Issue Date:	2026-04-23 14:35:41 (UTC+8)
Publisher:	Hindawi Publishing Corporation;Cairo, Egypt: Hindawi Publishing Corporation
Abstract:	摘要： This study presents a new active and reactive power control scheme for a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT) control of the PV panel with the function of low voltage ride through (LVRT). Moreover, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. To reduce the risk of overcurrent during LVRT operation, a current limit is predefined for the injection of reactive current. Furthermore, the control of active and reactive power is designed using a two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN). In addition, the online learning laws of 2D-RFCMANN are derived according to gradient descent method with varied learning-rate coefficients for network parameters to assure the convergence of the tracking error. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme. 出版者： Cairo, Egypt: Hindawi Publishing Corporation 出版日期： 2014-01-01 出處： International Journal of Photoenergy, 2014-01, Vol.2014 (2014), p.1-13 資源來源：中文電子期刊服務 CEPS: Chinese Electronic Periodical Services 版權： Copyright © 2014 Faa-Jeng Lin et al. 版權： COPYRIGHT 2014 John Wiley & Sons, Inc. 版權： COPYRIGHT 2014 Hindawi Limited 版權： Copyright © 2014 Faa-Jeng Lin et al. Faa-Jeng Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 識別號： ISSN: 1110-662X 識別號： ISSN: 1687-529X 識別號： EISSN: 1687-529X 識別號： DOI: 10.1155/2014/760743
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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