Wind Farm Management using Artificial Intelligent Techniques - Benlahbib, B.; Bouchafaa, F.; Mekhilef, Saad; Bouarroudj, N. (Institute of Advanced Engineering and Science, 2017)
This paper presents a comparative study between the genetic algorithm and particle swarm optimization methods to determine the optimal proportional-integral (PI) controller parameters for wind farm supervision algorithm. The main objective of this study is to obtain a rapid and stable system by tuning of the PI controller, thereby providing an excellent monitor for our wind farm by sending separate set points to all wind generators. A supervisory system controls the active and reactive power of the entire wind farm by sending out set points to all wind turbines. A machine control system ensures that the set points at the wind turbine level are reached. The entire control is added to the normal operating power reference of the wind farm established by a supervisory control. Finally the performance of the proposed algorithm is verified through MATLAB/Simulink simulation results by considering a wind farm of three doubly-fed induction generators.