The present work is willing to enlarge the field of using the solar energy power generation in order to take better advantage of the sun radiation. In this paper a particular attention has been paid to the dynamic behavior of the grid connected PV system, against voltage sag and voltage swell caused respectively by adding a load and then removing the same load, the analysis has been done on IEEE 14 bus system. As an alternative to these irregular working conditions, the voltage source converter is a key component of the PV system with the purpose of providing voltage support by generating or absorbing reactive power at the point of common coupling, which makes it act as a real STATCOM. This novel usage of PV solar farm can keep the bus voltages at wanted level without requiring any extra voltage control devices that could increase the whole transmission system cost. On the other hand the efficiency of photovoltaic generator can be improved by forcing the panel to operate at its maximum power point, this function is guaranteed by the maximum power point tracking (MPPT), in fact numerous techniques are described in the literature where the perturb and observe seems to be the simplest and commonly used method, however it has many disadvantages. In this paper adaptive neural fuzzy inference system (ANFIS) based MPPT method combines the ability of fuzzy logic to handle imprecise data and the learning abilities of artificial neural networks. It is found that this technique can precisely track the maximum power point under various irradiance and temperature conditions. Simulation results using Matlab/Simpower systems toolbox show an excellent waveforms quality and high performance dynamic behavior compared to the perturb and observe technique.

Keywords: Adaptive neuro fuzzy inference system (ANFIS), Static synchronous compensators (STATCOM), IEEE 14 bus test system, Maximum power point tracking (MPPT), Perturb and observe (P&O), Photovoltaic (PV) solar farm, Voltage regulation, Voltage source converter (VSC)