Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Abstract

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).