Self-Tuning Controller for Generator Excitation Control

Dao-zhi, Xia; Heydt, G.T.

doi:10.1109/tpas.1983.317797

Cited by 109 publications

(33 citation statements)

References 7 publications

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“…Diversas técnicas tais como controle adaptativo têm sido propostas como solução do problema (Ghandra, 1988), (Xia, 1983). Porém, a maioria dos controles adaptativos é baseada na identificação de parâme-tros do modelo do sistema em tempo real, o que consome muito tempo.…”

Section: Introductionunclassified

Estabilizador De Sistema De Potência Ajustado Por Redes Neurais

Daniel¹,

Souza²,

Silva³

et al. 2016

Anais Do 8. Congresso Brasileiro De Redes Neurais

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Abstract⎯ This paper presents a parameter tuning to Power System Stabilizers using artificial neural networks; Multi Layer Perceptron and Radial Basis Function are used. The neural networks use its searchs the better dynamic stability performance of the electric power systems through of the better damping oscillation when a disturbance occurs. The PSS parameters are automatically adjusted by the neural network in sense to give a satisfactory control when some variation in current plant operating conditions occurs. Simulations were used to verify the model performance proposed. The simulations were based in nonlinear dynamic model with a machine connected in an infinite bus. The modeling proposed confirm your efficiency through simulation results, which the dynamic performance was improved.Keywords⎯ Power System Stabilizer, PSS, Neural Network, RBF, MLP.Resumo⎯ Este trabalho propõe um ajuste de parâmetros para os Estabilizadores de Sistemas de Potência utilizando redes neurais artificiais, Multi Layer Perceptron e Radial Basis Function. A utilização dessas redes neurais busca o aprimoramento da estabilidade dinâmica em sistemas elétricos de potência através do melhor amortecimento das oscilações eletromecânicas da má-quina quando submetida a perturbações. Os parâmetros do estabilizador passam a ser ajustados automaticamente pelas redes neurais de modo a manter satisfatório o desempenho do sistema de controle quando existirem variações nas condições operacionais da planta. Simulações foram utilizadas na análise do desempenho da modelagem proposta. As simulações basearam-se em modelo dinâmico não linear com uma máquina conectada a uma barra infinita. A modelagem proposta confirma sua eficiência através dos resultados das simulações, nas quais são obtidas melhorias no desempenho.Palavras-chave⎯ Estabilizador do Sistema de Potência, Redes Neurais, RBF, MLP.

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Section: Introductionunclassified

Estabilizador De Sistema De Potência Ajustado Por Redes Neurais

Daniel¹,

Souza²,

Silva³

et al. 2016

Anais Do 8. Congresso Brasileiro De Redes Neurais

View full text Add to dashboard Cite

show abstract

“…Different modern control theory based approaches have been applied to PSS design problem. These contain optimal control, fuzzy & neuro-fuzzy control, variable structure control and adaptive control [4][5][6][7]. It is shown in [8] that appropriate selection of conventional PSS parameters result in satisfactory performance under system disturbances.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Ahmed¹,

Amin²

2012

IJCA

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In this paper, two evolutionary algorithms-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a derivative-free and flexible optimizer which is powered by the behavior of organism, such as bird flocking. Eigen-value based objective function is considered for the tuning of PSSs to enhance system damping of electromechanical mode. The performance of proposed IWO-based PSS and PSO-based PSS is tested and demonstrated under different disturbances for a four machine example power system. The Eigen value analysis and non-linear time domain simulation results shows that both IWO-based PSS and PSO-based design can successfully damp out the oscillations and thus improve the stability of the system. However, the abilities like faster convergence and greater shifting of critical modes to the left of s-plane keeps the choice of IWO based design in front of PSO based design for the system under consideration. General TermsPower system stability, Multi-machine system, Optimization.

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“…Many random search methods, such as genetic algorithm (GA) and simulated annealing (SA) (Feliachi et al, 1988;Xia & Heydt, 1983 optimal solution in problem space and they have been applied to the problem of PSS design (Abe & DOI, 1983;Arredondo, 1997;Do Bmfim et al, 2000).…”

Section: Introductionmentioning

confidence: 99%