Parameter identification of a PMSG using a PSO algorithm based on experimental tests

Abstract EN

An accurate model for a permanent magnet synchronous generator (PMSG) is important for the design of a high-performance PMSG control system.

The performance of such control systems is influenced by PMSG parameter variations under real operation conditions.

In this paper, the electrical parameters of a PMSG (the phase resistance, the phase inductance and the rotor permanent magnet (PM) flux linkage) are identified by a particle swarm optimization (PSO) algorithm based on experimental tests.

The advantages of adopting the PSO algorithm in this research include easy implementation, a high computational efficiency and stable convergence characteristics.

For PMSG parameter identification, the normalized root mean square error (NRMSE) between the measured and simulated data is calculated and minimized using PSO.