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
Un contrôleur unifié de flux de puissance (UPFC) de réseaux neuronaux récurrents basé sur Lyapunov est développé pour améliorer la stabilité transitoire des systèmes électriques. Tout d’abord, un modèle dynamique UPFC simple, composé d’une susceptance shunt contrôlable du côté shunt et d’un transformateur complexe idéal du côté série, est utilisé pour analyser les caractéristiques dynamiques de l’UPFC. Dans un deuxième temps, nous étudions la configuration de contrôle de l'UPFC avec deux blocs majeurs : le contrôle primaire et le contrôle supplémentaire. Le contrôle primaire est mis en œuvre par des techniques PI standard lorsque le système électrique fonctionne dans des conditions normales. Le contrôle supplémentaire ne sera efficace que lorsque le système électrique est soumis à des perturbations importantes. Nous proposons un nouveau contrôleur UPFC basé sur Lyapunov du système classique de bus infini à machine unique pour l'amélioration de l'amortissement. Afin d'envisager des modèles de générateurs détaillés plus complexes, nous proposons également un contrôleur de réseau neuronal récurrent adaptatif basé sur Lyapunov pour gérer de telles incertitudes de modèle. Ce contrôleur peut être traité comme une approximation du réseau neuronal des actions de contrôle de Lyapunov. De plus, ce contrôleur offre également une capacité d'apprentissage en ligne pour ajuster les poids correspondants avec l'algorithme de rétro-propagation intégré dans la couche cachée. Le schéma de contrôle proposé a été testé sur deux systèmes électriques simples. Les résultats de simulation démontrent que la stratégie de contrôle proposée est très efficace pour supprimer les oscillations de puissance, même dans des conditions de système sévères.
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Chia-Chi CHU, Hung-Chi TSAI, Wei-Neng CHANG, "Transient Stability Enhancement of Power Systems by Lyapunov- Based Recurrent Neural Networks UPFC Controllers" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 9, pp. 2497-2506, September 2008, doi: 10.1093/ietfec/e91-a.9.2497.
Abstract: A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.9.2497/_p
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@ARTICLE{e91-a_9_2497,
author={Chia-Chi CHU, Hung-Chi TSAI, Wei-Neng CHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Transient Stability Enhancement of Power Systems by Lyapunov- Based Recurrent Neural Networks UPFC Controllers},
year={2008},
volume={E91-A},
number={9},
pages={2497-2506},
abstract={A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.},
keywords={},
doi={10.1093/ietfec/e91-a.9.2497},
ISSN={1745-1337},
month={September},}
Copier
TY - JOUR
TI - Transient Stability Enhancement of Power Systems by Lyapunov- Based Recurrent Neural Networks UPFC Controllers
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2497
EP - 2506
AU - Chia-Chi CHU
AU - Hung-Chi TSAI
AU - Wei-Neng CHANG
PY - 2008
DO - 10.1093/ietfec/e91-a.9.2497
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2008
AB - A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.
ER -