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
Cet article présente un système d'aide à la décision (RDSS) en temps réel basé sur l'intelligence artificielle (IA) pour éviter l'effondrement de tension (VCA) dans les réseaux d'alimentation électrique. Le schéma RDSS utilise un réseau neuronal composite hyperrectangulaire flou (FHRCNN) pour effectuer l'identification des risques de tension (VRI). Dans le cas où une menace pour la sécurité du réseau d'alimentation électrique est détectée, un algorithme basé sur une programmation évolutive (EP) est déclenché pour déterminer les paramètres opérationnels requis pour restaurer le réseau d'alimentation électrique dans un état sécurisé. L'efficacité de la méthodologie RDSS est démontrée par son application au système American Electric Power Provider (AEP, système à 30 bus) dans diverses conditions de charge élevée et scénarios d'urgence. En général, les résultats numériques confirment la capacité du système RDSS à minimiser le risque d'effondrement de tension dans les réseaux d'alimentation électrique. En d’autres termes, le RDSS fournit aux entreprises de fournisseurs d’électricité (EPI) un outil viable pour effectuer une évaluation des risques de tension en ligne et des fonctions d’amélioration de la sécurité du système électrique.
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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},}
Copier
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 -