Reliable Accuracy of On-line Permanent Magnet Synchronous Machine Parameters’ Identification Process by Using a Meta-heuristic Optimization Algorithm

Abstract

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.