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Frequency domain multiplexing (FDM) allows simplifying the data acquisition in vector control when the resolver is used as a position sensor. However, the crosstalk reduction in the demultiplexing process is still a difficult task. This article proposes an improvement of the FDM-based acquisition system through synchronous sampling and Differential Evolution (DE). The resolver signals and the PWM carrier are synchronized so that the resolver outputs are zero when the PWM carrier reaches its valleys. Thus, the fundamental component of the stator currents can be easily estimated from the signals sent to the acquisition system. These estimated current signals and an angle tracking observer (ATO) are used to estimate the angular position. DE and a discretized model of the ATO are applied in order to improve the angle estimation transient response and to reduce crosstalk. Conditions to apply the proposed multiplexing approach will be defined. Simulation and experimental results show that the proposed approach allows an accurate estimation of the current fundamental components and the angular position when FDM is used, with better performance respect to other approaches in the literature. This is the first time that a DE is used to tune an ATO. [ABSTRACT FROM AUTHOR]

A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results. [ABSTRACT FROM PUBLISHER]

This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window). [ABSTRACT FROM AUTHOR]