Optimal controllers

Article history: 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). 1. INTRODUCTION Generated electric power from wind is growing at an increasing annual rate worldwide in the last decade as its cost has decreased considerably. Widespread use of wind power can largely reduce production costs, reduce carbon emissions, contribute to resolving global warming calls, and reduce the dependency on fossil fuels [1]. With the recent developments in power electronics technology, DFIG driven by a wind turbine [2] are becoming very popular and their worldwide market share is increasing quickly. DFIG has been widely used in grid-connected variable-speed constant-frequency wind energy conversion systems (WECS) due to their advantageous features such as higher efficiency, lower cost, wide-range operations, capability of producing reactive power, better energy capture, smoother operation, and flexible control. Therefore, with the increasing utilization of wind power in the interconnected power network, improving DFIG transient performance is becoming more significant in power system operations. Tuning the parameters of a classical PI controller of a grid-connected DFIG-based WECS is complicated due to the high nonlinearity and complexity of the system. Optimization techniques are applied