Development of optimal controllers for a DFIG based wind farm in a smart grid under variable wind speed conditions

Variations in wind speed cause different transient responses in a Doubly Fed Induction Generator (DFIG)-based wind farm. Transients during grid disturbances, depending on controller parameters, can lead to a system collapse, especially when significant fluctuations exist in wind speed despite the presence of SmartParks (energy storage). When a fault is introduced, the variable frequency converter (VFC) is the most susceptible part in a DFIG. The VFC is controlled by a set of Proportional Integral (PI) controllers. Parameters of PI controllers are very difficult to tune using traditional methods due to nonlinearity in DFIGs and the increasing complexity of smart grids. This paper presents an implementation of a new efficient heuristic approach, the Mean Variance Optimization (MVO) algorithm, on a digital signal processor, for online tuning of PI controllers on the rotor-side converter of a DFIG. With the MVO-optimized PI controllers on the wind farm, the entire smart grid remains stable under grid disturbances for a wide range of wind speeds. The results demonstrate that intelligent controller tuning is critical for optimal operation of DFIG-based wind farms in a smart grid despite the presence of energy storage.