This paper presents a design procedure for tangentially magnetized variable flux machines that aims to reduce the machine inverter rating. The proposed design can achieve high torque density at low speeds, and high efficiency at an extended speed range, as armature d-axis current pulses are applied at high speeds to reduce the magnet flux. In order to regain the full torque capability when the motor slows down, a magnetizing current pulse has to be applied. The amplitude of the magnetizing current is usually larger than the machine rated current. Therefore, the machine may require an oversized inverter in order to be able to remagnetize the magnets. The impact of different machine design parameters on the magnetization current requirement is investigated in order to reduce the inverter cost. The proposed machine design procedure is validated by experimental measurements on a variable flux machine prototype.

This paper proposes a novel design for variable-flux machines with Alnico magnets. The proposed design uses tangentially magnetized magnets to achieve high air-gap flux density and to avoid demagnetization by the armature field. Barriers are also inserted in the rotor to limit the armature flux and to allow the machine to utilize both reluctance and magnet torque components. An analytical procedure is first applied to obtain the initial machine design parameters. Then, several modifications are applied to the stator and rotor designs through finite-element analysis (FEA) simulations to improve machine efficiency and torque density. A prototype of the proposed design is built, and the experimental results are in good correlation with the FEA simulations, confirming the validity of the proposed machine design concept.

This paper is concerned with the development of a vector controller for a variable flux inset permanent magnet synchronous machine (VF-IPMSM). This variable flux machine (VFM) uses low cost aluminum-nickel-cobalt (AlNiCo) permanent magnets (PMs). The demagnetization of the permanent magnets (PMs) is conducted on the stator winding of the machine hence there is no separate winding for the excitation of the PMs. The proposed control method incorporates field weakening for smooth transitions between torque and speed from a higher magnetization state to a lower one. The benefits of the variable flux over a normal insert permanent magnet synchronous machine during field weakening operation are demonstrated. Due to the use of AlNiCo PMs the size of the converter is reduced and hence converter costs reduction.

In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble dominated by classifiers with the same structure produced poor accuracy. Uncertainty rule from information theory was also used to further define diversity. Genetic algorithms were used to find the optimal ensemble by using the diversity indices as the cost function. The method of voting was used to aggregate the decisions.

The paper shows the reduction of core losses by using a three-level inverter over a two-level inverter for the same dc-bus voltage and switching frequency. A synchronous reluctance machine stator-core toroid was used in the analysis. Hence, the analysis is realistic as it accounts for mechanical effects through the stator core, and the distorted supply of the line-to-line inverter voltage supplies. The paper also shows the limitations of using finite element-derived excitation; hence, the toroid was directly supplied by the inverter. The reduction of core losses by use of a three-level inverter is significant at very high flux densities and frequencies; approximately 60% lower core losses. Therefore, it can reduce the cooling burden especially in the hard to cool teeth, increase the service life, and allow increased output. The latter is because the output of the three-level fundamental voltage is higher for the same dc-bus and switching frequency at lower machine losses.

Blades icing will seriously affect the performance of wind turbines in respect to power loss and dynamic load increase. Blades icing detection technique becomes necessary to advance de-icing maintenance. Extracting effective features from SCADA data has become a challenging task during operating conditions under icing. Current research work lacks for the integration of physical information and insufficient analysis of icing dynamic evolution in the process of feature extraction. In order to eliminate these deficiencies, a multilevel feature extraction model combined with physical information and intelligent algorithm (MFT-PI) is proposed in this paper. First, features that characterize the severity of icing are extracted by power characteristic curve technology and feature selection technology in the transient feature extraction level. These techniques are driven by the information of power loss and icing mass growth. Second, a fully connected neural network integrated with triplet loss function is designed for extracting fusion feature, which considers the similarity of data distribution between early-icing stage and non-icing stage. Finally, the proposed feature extraction model is evaluated in two icing wind turbines. Results show that fusion features extracted from the proposed model is more stable, reliable and accurate by comparing the performance of other feature groups in different classifiers.