**7. Conclusions**

In this paper, a PVGCS system is modeled by the Hammerstein-Wiener nonlinear system identification method. Two main steps to obtain models from a system identification process are implemented. The first step is to set up experiments to obtain waveforms of DC inverter voltage/current, AC inverter voltage/current, point of common coupling (PCC) voltage, and grid and load current. Experiments are conducted under steady state and transient conditions for commercial rooftop inverters with rating of few kW, covering resistive and complex loads. In the steady state experiment, six conditions are carried out. In the transient case, two conditions of operating conditions are conducted. The second stage is to derive system models from system identification software. Collected waveforms are transmitted

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into a computer for data processing. Waveforms data are divided in two groups. One group is used to estimate models whereas the other group to validate models. The developed programming determines various model waveforms and search for model waveforms of maximum accuracy compared with actual waveforms. This is achieved through selecting model structures and adjusting the model order of the linear terms and nonlinear estimators of nonlinear terms. The criteria for selection of a suitable model are the "Best Fits" as defined by the software, and a model order which should be minimum.

After obtaining appropriate models, analysis and prediction of power quality are carried out. Modeled output waveforms relating to power quality analysis are determined from different scenarios. For example, irradiances and ambient temperature affecting DC PV outputs and nature of complex local load can be varied. From the model output waveforms, determination is made on power quality aspects such as voltage level, total harmonic distortion, complex power, power factor, power penetration and frequency deviation. Finally, power quality problems are classified.

Such modelling techniques can be used for system planning, prevention of system failures and improvement of power quality of roof-top grid connected systems. Furthermore, they are not limited to PVGCS but also applicable to other distributed energy generators connected to grids.
