The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo apresenta um sistema de suporte à decisão em tempo real (RDSS) baseado em inteligência artificial (IA) para evitar colapso de tensão (VCA) em redes de fornecimento de energia. O esquema RDSS emprega uma rede neural composta hiperretangular difusa (FHRCNN) para realizar a identificação de risco de tensão (VRI). Caso seja detectada uma ameaça à segurança da rede de fornecimento de energia, um algoritmo baseado em programação evolutiva (EP) é acionado para determinar as configurações operacionais necessárias para restaurar a rede de fornecimento de energia a uma condição segura. A eficácia da metodologia RDSS é demonstrada através da sua aplicação ao American Electric Power Provider System (AEP, sistema de 30 barramentos) sob diversas condições de carga pesada e cenários de contingência. Em geral, os resultados numéricos confirmam a capacidade do esquema RDSs em minimizar o risco de colapso de tensão nas redes de fornecimento de energia. Em outras palavras, o RDSS fornece às Empresas Fornecedoras de Energia (PPEs) uma ferramenta viável para realizar avaliações de risco de tensão on-line e funções de aprimoramento da segurança do sistema de energia.
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Chen-Sung CHANG, "A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1740-1747, June 2008, doi: 10.1093/ietisy/e91-d.6.1740.
Abstract: This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1740/_p
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@ARTICLE{e91-d_6_1740,
author={Chen-Sung CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks},
year={2008},
volume={E91-D},
number={6},
pages={1740-1747},
abstract={This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1740},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1740
EP - 1747
AU - Chen-Sung CHANG
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1740
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.
ER -