Particle-Filter-Based Magnet Flux Linkage Estimation for PMSM Magnet Condition Monitoring Using Harmonics in Machine Speed

For permanent magnet synchronous machines (PMSMs), accurate magnet flux linkage information is critical for permanent magnet condition monitoring and drive performance improvement. This paper proposes a novel particle-filter-based magnet flux linkage estimation approach by using the harmonics in the machine speed. In the proposed approach, the harmonic current is first injected to change the speed harmonics, and the particle filter is then applied to estimate the magnet flux linkage from the speed harmonics. With a proper selection of injected harmonic current, it is capable of simultaneously estimating the magnet flux linkage and reducing the torque ripples as well as the speed ripples. The proposed approach is based on the machine mechanical equation, so it is not influenced by the magnetic saturation, the resistance variation, and the inverter nonlinearity. Specifically, at first, a novel state-space model is developed based on the machine mechanical equation, which models the relation between the magnet flux linkage and the speed harmonic. The state-space model is nonlinear, so the particle filter is employed for a magnet flux linkage estimation. Our particle-filter-based estimation approach is validated on a laboratory PMSM drive system under different loads, speeds, and temperatures.