Abstract—This paper present an identification of model system performance for Photovoltaic (PV) System under normal and shading operating condition in UiTM Pulau Pinang, Malaysia of 2.4 kW systems. A system identification approach was implemented by employing a Hammerstein-Weiner (HW) model as model structure. The approach concerned on the estimation of the photovoltaic system basis of observed data. The nonlinearity input and output are taken from irradiance and dc output current data of the real system severally. These data were used in Hammerstein-Weiner model to generate a black-box model structure which provides a flexible parameterization for nonlinear models. The best fit nonlinear model when using data from normal operating condition is when HW model incorporate with piecewise linear as the input channel and wavelet network estimators as output channel. For normal operation of PV system, the percentage of best fit was 96.51% by means of bn = 1, fn = 3, and kn = 2 of the linear model order. While the percentage best fit model generate considering shading effect was 86.32% with bn = 1, fn = 3, and kn = 1 of the linear model order. The modelling is implemented using system identification toolbox of Matlab software package.
Index Terms—Photovoltaic system, nonlinear hammerstein-weiner, shading, system identification.
Authors are with Faculty of Electrical Engineering, UiTM (Universiti Teknologi MARA). (e-mail: email@example.com; firstname.lastname@example.org; email@example.com; firstname.lastname@example.org; email@example.com)
Cite: Mohd Najib Mohd Hussain, Ahmad Maliki Omar, Pais Saidin, Ahmad Asri Abd Samat, and Zakaria Hussain, "Identification of Hammerstein-Weiner System for Normal and Shading Operation of Photovoltaic System," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 239-243, 2012.