Nowadays, regulation standards regarding the injection of harmonics in the grid power supply are becoming stricter. These standards have a direct impact on the design and control of converters, especially in medium-voltage drives. To fulfil these standards, converters are designed to work with the power factor as close to unity as possible and to correct the harmonics spectrum in case of a grid power supply with multiple resonances. The preferred modulation technique for medium-voltage drives is usually selective harmonic elimination pulse width modulation. This approach requires a precise calculation of pulse patterns (switching angle vs. modulation index) with additional constraints. This research presents a new approach for the determination of optimal pulse patterns. The technique ensures the elimination of low-order harmonics and minimization of some high-order ones. The proposed technique incorporates the additional constraints regarding minimum on/off switching time (pulse duration) and ensures the continuity of pulse patterns. Optimal pulse patterns are determined with the brute force method which searches the feasible solution space by use of the Jacobian matrix null space. Determined pulse patterns are verified by the simulation and experimental measurements.

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Flux estimation is a key feature of the field-oriented control for the electrically excited synchronous machine which enables the high-performance, high-dynamic drive behavior. In this work, an electrically excited synchronous machine flux estimator based on a current and voltage model is proposed. In this case, the transition between the estimators is done with a fuzzy logic set of rules. The flux estimator based on the current model of the machine in this paper considers the saturation and cross-coupling effect in both axis and it is suitable for applications where a limited amount of the machine data is available. The flux estimator based on the voltage model is specially designed for the drives where high voltage and current ripple is present under normal operating conditions, e.g., like in cycloconverter applications. To exploit all the advantages of both models, a fuzzy logic transition is proposed based on multiple choices which manages the transition between the models based on a speed and torque reference. The proposed flux estimator is experimentally verified on a cycloconverter fed salient-pole electrically excited synchronous machine. The experimental results clearly show that the proposed flux estimator enables the accurate and stable operating conditions for different operating points of the cycloconverter-fed salient-pole electrically excited synchronous machine.

Accurate knowledge of the magnitude and position of the magnetic flux is essential for implementing field-oriented control (FOC) and achieving high-performance behaviour of AC drives. For estimating the flux in a wide range of speeds, so-called hybrid flux estimators, which are a combination of current-model and voltage-model based estimators, are usually used. Since the inductances are used as parameters in the current model, knowledge of the actual flux–current relationship, i.e., of the actual flux linkage map, is inevitable. In this paper, a novel experimental method for identifying the flux linkage map of an electrically excited synchronous machine (EESM) with double stator winding is proposed, which, unlike most existing experimental methods, does not require an additional machine to be used as a load. The flux is determined for different operating points to which the unloaded and sped-up machine is brought to by injecting d- and q-axis stator current components, whereby the current controllers are used to keep them constant for a certain operating point. The proposed method has been used to identify the flux linkage map of a medium-voltage EESM with double stator winding. A more than acceptable accuracy confirmed by comparison with three different analytical methods, together with the fact that it does not require a complex experimental setup, makes the proposed method suitable for the identification of a machine’s flux linkage map in an industrial environment.