It is known that skewed rotor structure for electrical machines can reduce torque ripple and radial force which will produce the unwanted vibration and noise, however, utilizing this structure can also affect other parameters of machine such as the average torque, the inductance, the flux-linkage and the magnetic energy and so on. This drawback always shows in the special and popular machines-switched reluctance motors. In this paper, different skewed angles of rotor and their effects on the key electromagnetic parameters are analyzed for switched reluctance motor. During the whole analysis process, three-dimensional finite element method is used to model, mesh, solve, and plot the results. Especially, the properties of the magnetic materials, the complex motor geometry, the effects of magnetic saturation and end windings were considered. Simulated and compared results show that an appropriate skewed angle selected can reduce the radial force largely but not make the average torque decreasing badly, which can be observed from the final conclusion: under the skewed angle of 5°, the radial force is reduced of 56.2%, the average torque is only reduced of 18.9%. So, if the target of designing for switched reluctance motors is to reduce the vibration and noise to be the most extent, then selecting an appropriate skewed structure for rotor shape is an effective way.

A novel approach for the power quality (PQ) disturbances classification based on the wavelet transform and self-organizing learning array system is proposed. Wavelet network is utilized to extract feature vectors for various PQ disturbances and the wavelet transform can accurately localizes the characteristics of a signal both in the time and frequency domains. These feature vectors then are applied to the system for training and disturbance pattern classification. By comparing with a classic neural network, it is concluded that the proposed system has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method are discussed and the proposed method can provide accurate classification results. On the basis of hypothesis test of the averages, it is shown that corresponding to different wavelets selection, there is no statistically significant difference in performance of PQ disturbances classification and the relationship between the wavelet decomposition level and classification performance is discussed. The simulation results demonstrate the proposed method gives a new way for identification and classification of dynamic power quality disturbances.

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