Search

In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer's specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter's identification field. The estimate method is focused on the parameter's estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy. [ABSTRACT FROM AUTHOR]

In this paper, a sliding mode controller is implemented to a permanent synchronous generator direct driven wind energy conversion system. The goal of this proposed work is to control the stator directly and quadrature axis currents to minimize the chattering phenomenon. To achieve this goal, we use two numerical techniques which are PSO (particle swarm optimization) and GWO (grey wolf optimization) algorithms to obtain gains parameters of the controller. The analysis of the recommended approach robustness and responsiveness of the system has been carried out under the real wind speed of the Adrar region (south of Algeria) and simulation results are tested under MATLAB/Simulink tool. The main result shows the effectiveness and the better performance of the PSO compared to the GWO. [ABSTRACT FROM AUTHOR]