A Genetic Algorithm-Based Low Voltage Ride-Through Control Strategy for Grid Connected Doubly Fed Induction Wind Generators

This paper proposes a new computational intelligence-based control strategy, to enhance the low voltage ride-through capability of grid-connected wind turbines (WTs) with doubly fed induction generators (DFIGs). Grid codes world-wide require that WTs should supply reactive power to the grid during and after the fault, in order to support the grid voltage. The conventional crowbar-based systems that were initially applied in order to protect the rotor-side converter at the occurrence of grid faults, do not fulfill this requirement, as during the connection of the crowbar, the DFIG behaves as a squirrel cage machine, absorbing reactive power from the grid. This drawback led to the design of control systems that eliminate or even avoid the use of the crowbar. In order to conform to the above-mentioned requirement, this paper proposes a coordinated control strategy of the DFIG converters during a grid fault, managing to ride-through the fault without the use of any auxiliary hardware. The coordination of the two controllers is achieved via a fuzzy controller which is properly tuned using genetic algorithms. To validate the proposed control strategy, a case study of a 1.5-MW DFIG supplying a relatively weak electrical system is carried out by simulation.