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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Um controlador de fluxo de potência unificado (UPFC) de redes neurais recorrentes baseado em Lyapunov é desenvolvido para melhorar a estabilidade transitória de sistemas de energia. Primeiro, um modelo dinâmico UPFC simples, composto por uma susceptância shunt controlável no lado shunt e um transformador complexo ideal no lado série, é utilizado para analisar as características dinâmicas do UPFC. Em segundo lugar, estudamos a configuração de controle do UPFC com dois blocos principais: o controle primário e o controle suplementar. O controle primário é implementado por técnicas PI padrão quando o sistema de potência é operado em condições normais. O controle suplementar será eficaz somente quando o sistema de potência estiver sujeito a grandes perturbações. Propomos um novo controlador UPFC baseado em Lyapunov do clássico sistema de barramento infinito de máquina única para aprimoramento de amortecimento. A fim de considerar modelos geradores detalhados mais complicados, também propomos um controlador de rede neural recorrente adaptativo baseado em Lyapunov para lidar com tais incertezas do modelo. Este controlador pode ser tratado como aproximações de redes neurais das ações de controle de Lyapunov. Além disso, este controlador também oferece capacidade de aprendizado on-line para ajustar os pesos correspondentes com o algoritmo de retropropagação construído na camada oculta. O esquema de controle proposto foi testado em dois sistemas de potência simples. Os resultados da simulação demonstram que a estratégia de controle proposta é muito eficaz para suprimir oscilações de potência mesmo sob condições severas do sistema.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
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
Copiar
@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},}
Copiar
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 -