2012
DOI: 10.7763/ijiee.2012.v2.167
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Model Predictive Control Based on System Identificationof Photovoltaic Grid Connected Inverter

Abstract: This paper proposes a model predictive control of photovoltaic grid-connected inverter based on system identification. The single phase inverter is experimented and its model is determined by using System identification approach with Hammerstein-Wiener model. The derived nonlinear voltage model has accuracy more around 97.34% and it is transformed to the state space model by linearization. A simulation of model based controller uses the discrete time model of inverter to predict the behavior of the output volt… Show more

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Cited by 3 publications
(2 citation statements)
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References 15 publications
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“…where V c is the amplitude of the control voltage, w = 2π f , φ is the shift angle of the signal. From (2) and (12), the control variable in the second orthogonal imaginary circuit v β is obtained as…”
Section: Vector Selectionmentioning
confidence: 99%
See 1 more Smart Citation
“…where V c is the amplitude of the control voltage, w = 2π f , φ is the shift angle of the signal. From (2) and (12), the control variable in the second orthogonal imaginary circuit v β is obtained as…”
Section: Vector Selectionmentioning
confidence: 99%
“…It is well known that proportional integral (PI) controllers could not cope with system uncertainties [6]. Numerous methods have been proposed in the literature to overcome the drawbacks of conventional PI controller, including sliding mode control (SMC) [7,8], fuzzy logic control (FLC) [9,10], model predictive control (MPC) [11][12][13]. Despite SMC has its advantages of insensitive to parameter variations and external disturbances, it suffers from an undesirable chattering phenomenon and limited switching frequency when using software control.…”
Section: Introductionmentioning
confidence: 99%