A technique for driving a star‐connected PMSM without a position or speed sensor, which is known in the literature as Direct Flux Control (DFC), is described here. This technique is based on voltage measurements at the machine star point and can work even at very low speeds including standstill. The underlying principle of this technique is outlined and an improved position estimation algorithm allowing the exact reconstruction of the direction of maximum inductance is presented. The DFC technique is implemented on a microcontroller board and experimental validation is conducted on a PMSM both for quasi‐static characterisation and for open‐loop driving.

Estimation methods based on the magnetic anisotropy of the machine can be used to estimate the rotor position down to low speeds including standstill. Beside the classical approach of measuring current changes, anisotropy information can also be obtained by measuring the neutral point voltage when the machine is star-connected. This work analytically compares the anisotropy information that can be obtained by these two measurement approaches. FEM simulation results are also used to show the obtainable signals and the estimated positions for an exemplary PMSM.

An important tool in the field of Permanent Magnet Synchronous Motors (PMSMs) is parameter identification, which allows for final stage inspection, adaptive control and condition monitoring. Common identification approaches require the PMSM to be mounted into a test-bench with encoders, which is complicated or not possible for some machines. In order to address this issue, this work combines a Recursive Least-Squares based parameter identification algorithm with the sensorless control technique Direct Flux Observer (DFO). The developed identification technique is implemented on a microcontroller and experimental results on different kind of PMSMs are shown and discussed.

Information obtained about magnetic anisotropy via neutral point voltage measurements can be used to estimate the position of synchronous and induction machines at all speeds including standstill. Due to its usually high signal-to-noise ratio, this estimation method can be an attractive alternative to approaches that acquire anisotropy information via current measurements. The position estimation method looked at in this paper relies on anisotropy information in the form of position-dependent inductance ratios. These are obtained through measurement of the voltage between the machine’s neutral point and an artificial neutral point during the application of different voltage vectors via a two-level switching inverter. We analyze the conditions placed on and the consequences of implementing necessary modifications to a standard space vector modulation. Five different modified modulation strategies are compared and investigated, including two newly proposed strategies which use only a minimal set of active and zero voltage vectors for measurement of the inductance ratios and therefore allow high utilization of available voltage and a high update rate of the estimated position. Experimental results for three low-power three-phase permanent magnet synchronous machines are presented which suggest that modulation strategies that use active measurement vectors in all three instead of only two axes of the machine are less susceptible to systematic deviations in the position estimation that presumably result from nonlinear machine properties. As part of the machine model, a normalizing inductance variation ratio is introduced, which simplifies expressions and supports the comparison of motors.

This paper presents an improvement of sensorless techniques based on anisotropy for the estimation of the electrical angular position of synchronous machines by means of an iterative algorithm. The presented method reduces the effect of the fourth saliency harmonics on the measured signals avoiding the use of an observer or filter, thus, no additional dynamics are introduced on the system. Instead, a static algorithm based on iterative steps is proposed, minimizing the angular position error. The algorithm is presented and applied using the DFC (Direct Flux Control) technique but it is not limited to this choice. The advantages and limitations of this method are presented within this paper. The proof of the algorithm convergence is given. Simulations and experimental tests are performed in order to prove the effectiveness of the proposed algorithm.

In the above article [1], there are a few errors that are corrected in the following.