Doubly fed induction generator (DFIG) wind turbine controlled by artificial organic networks

The main goal of this paper is to show the control capabilities of artificial organic networks when they are applied to variable speed wind generators. Since doubly fed induction generator (DFIG) is one of the most important variable wind generators, it requires to include advanced controllers which allow to improve its performance during operation. On the other hand, the artificial organic controllers (AOC) are intelligent controllers based on ensembles of fuzzy inference systems and artificial hydrocarbon networks. To understand AOC, this paper introduces the fundamentals of artificial hydrocarbon networks, describes the fuzzy-molecular inference ensemble, and discusses artificial organic controllers when they are deployed in variable speed wind generators. Additionally, DFIG wind turbine model is completely derived in order to test the AOC. A conventional proportional–integral–derivative (PID) controller is compared with the proposed PID-based AOC (PID-AOC) for wind generators under linear and nonlinear wind profiles. Five parameters were used for evaluation: pitch angle, stator power, rotor power, generator’s speed and power coefficient. Results showed the superior control performance in wind generators when artificial organic networks are implemented. Particularly, the PID-AOC response obtained higher values of rotor and stator powers, small pitch angle response meaning less energy consumption, high power coefficient values, and smooth starting phase minimizing risks of damage in the DFIG. The proposed PID-AOC can be applied in DFIG to minimize the undesired fluctuation on the electric grid, to reduce the mechanical stress in the blades preventing mechanical damages and to perform good sensitivity when noise in the wind is included.