Biblio

This paper proposes a novel concept for an electric generator in which both ac windings and permanent magnets (PMs) are placed in the stator. Concentrated windings with a special pattern and phase coils placed in separate slots are employed. The PMs are positioned in a spoke-type field concentrating arrangement, which provides high flux intensification and enables the use of lower remanence and energy non-rare earth magnets. The rotor is exterior to the stator and has a simple and robust reluctance-type configuration without any active electromagnetic excitation components. The principle of operation is introduced based on the concept of virtual work with closed-form analytical airgap flux density distributions. Initial and parametric design studies were performed using electromagnetic FEA for a 3MW direct-drive wind turbine generator employing PMs of different magnetic remanence and specific energy. Results include indices for the goodness of excitation and the goodness of the electric machine designs; loss; and efficiency estimations, indicating that performance comparable to PM synchronous designs employing expensive and critical supply rare-earth PMs may be achieved with non-rare earth PMs using the proposed configuration.

In this first paper of a two-part series, the harmonic plane decomposition is introduced, which is an extension of the vector-space decomposition. In multiphase electrical machines with variable phase-pole configurations, the vector-space decomposition leads to a varying numbers of vector spaces when changing the configuration. Consequently, the model and current control become discontinuous. The method in this paper is based on samples of each single slot currents, similarly to a discrete Fourier transformation in the space domain that accounts for the winding configuration. It unifies the Clarke transformation for all possible phase-pole configurations such that a fixed number of orthogonal harmonic planes are created, which facilitates the current control during reconfigurations. The presented method is not only limited to the modeling of multiphase electrical machines but all kinds of existing machines can be modeled. In the second part of this series, the harmonic plane decomposition will be completed for all types of machine configurations.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.