2016
DOI: 10.1002/9781119082989
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Model Predictive Control of Wind Energy Conversion Systems

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Cited by 216 publications
(217 citation statements)
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“…Because the sampling time is sufficiently small, and the sampling frequency is much higher than the fundamental frequency, no extrapolation is considered for this study [38]. Instead, the simple one step method was used, which selects the switching state that minimizes the error at the (k + 1) instant and applies it at the kth instant.…”
Section: Bidirectional Dc-dc Converter In Discrete Time Domainmentioning
confidence: 99%
“…Because the sampling time is sufficiently small, and the sampling frequency is much higher than the fundamental frequency, no extrapolation is considered for this study [38]. Instead, the simple one step method was used, which selects the switching state that minimizes the error at the (k + 1) instant and applies it at the kth instant.…”
Section: Bidirectional Dc-dc Converter In Discrete Time Domainmentioning
confidence: 99%
“…Thus the predictive control can be achieved. Yaramasu et al discussed about the Model predictive control of wind energy conversion systems [23]. Experimental validation of a robust continuous nonlinear model predictive control based gridinterlinked photovoltaic inverter was reported by Errouissi et al [24].…”
Section: Introductionmentioning
confidence: 99%
“…Script principal para simulação no tempo a partir das funções fsolve e ode15i *** Parte 3 *** Comportamento dinâmico do SCIG 0.75 MW. Os parâmetros deste aerogerador podem ser encontrados em Yaramasu e Wu (2017). Perturbação: perda de duas linhas de transmissão durante 500 ms.…”
Section: D3 Implementação Em Matlab ®unclassified
“…Os parâmetros referentes a cada tipo de máquina são típicos de aerogeradores comerciais. Mais detalhes podem ser encontrados em Yaramasu e Wu (2017). Para o cálculo da constante de inércia e dos valores por unidade, foi adotada a potência aparente base de 3 MVA.…”
Section: Comentários Finaisunclassified
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