Small-signal stability analysis of DFIG based wind power system using teaching learning based optimization

• State space modelling of the DFIG connected to grid for small-signal assessment.
• Optimization of the PI controllers gains by using TLBO.
• Investigation of the impact of optimized gains on the nature of modes of oscillations.
• Investigation of the fault ride through capabilities of the DFIG.

The present paper formulates the state space modelling of doubly fed induction generator (DFIG) based wind turbine system for the purpose of small-signal stability analysis. The objective of this study is to discuss the various modes of operation of the DFIG system under different operating conditions such as three phase fault and voltage sags with reference to variable wind speed and grid connection. In the present work, teaching learning based optimization (TLBO) algorithm optimized proportional–integral (PI) controllers are utilized to control the dynamic performance of the modelled DFIG system. For the comparative analysis, TLBO based simulated results are compared to those yielded by particle swarm optimization (PSO) method for the same DFIG model. The simulation results show that the proposed TLBO based PI controller effectively works in minimizing the damping phenomena, oscillation in rotor currents and fluctuation in electromagnetic torque for the studied DFIG model. It is also observed that TLBO is offering better results than the PSO for the dynamic performance analysis of the studied model.