doi: 10.7763/IJMO.2011.V1.6
A Multi Input Multi output (MIMO) Hammerstein -Wiener Model Based Predictive Control of Single Phase Grid Connected Inverter
- 1 Energy Technology Division, School of Energy Environment and Materials, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand.
- 2 Department of Electrical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand.
- 3 Department of Mathematics, Faculty of science, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand.
Abstract
This paper propose a model predictive control of single phase grid-connected inverter based on system identification. The single phase inverter is experimented and its model is determined by using SISO (single input – single output) and MIMO (multi input multi output) System identification approach with Hammerstein-Wiener model. The derived SISO nonlinear voltage and current model have accuracy more around 93.55% and 95.01%. The MIMO Hammerstein-Wiener Model has voltage and current waveform accuracy 91.03% and 91.7%. The nonlinear model 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 sine wave reference voltage reference and the error is feedback to the optimizer. Simulation results shown that the proposed control scheme can achieve the output target with 97% accuracy.
Keywords
- model predictive control; MIMO Hammerstein Wiener
- system identification; grid-connected inverter
How to Cite
N. Patcharaprakiti, K. Kirtikara, V. Monyakul, D. Chenvidhya, A. Sangswang, and J. Saelao, "A Multi Input Multi output (MIMO) Hammerstein -Wiener Model Based Predictive Control of Single Phase Grid Connected Inverter," International Journal of Modeling and Optimization, vol. 1, no. 1, pp. 29-36, 2011. https://doi.org/ 10.7763/IJMO.2011.V1.6
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).