Model Predictive Control Based on System Identification of Photovoltaic Grid Connected Inverter
Keywords:
Model predictive control, system identification, hammerstein – wiener model, 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 voltage
for each possible switching state every sampling time. Then cost function is applied as a criterion for selecting the most suitable switching state for the next sampling interval. The model output is compared with the reference voltage sine wave and the error is feedback to the optimizer. Simulation results shown that the proposed control scheme can achieve the output target with 97% of accuracy.
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