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Wind turbines are increasingly expanding worldwide and Doubly‐Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning‐based method is proposed for incipient detection of inter‐turn short‐circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state‐of‐the‐art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi‐class classification problem and a Long Short‐Term Memory network, which is more appropriate for time‐series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well‐known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady‐state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning‐based methods. [ABSTRACT FROM AUTHOR]

Here a new sensitive fault detection criterion is proposed, based on the stationary wavelet transform (SWT) decomposition components of the stator currents, for the online detection of stator winding turn‐to‐turn faults at an early stage of development. The energies of the desired detail components are calculated and the normalized Euclidean distance of the energy differences between the three phases is used as a new fault detection criterion. The robustness of the proposed technique against the presence of rotor turn‐to‐turn faults and loading variations is demonstrated. In addition, an adaptive threshold level as a function of the voltage unbalance factor is proposed to guarantee the robustness of the diagnostic technique against unbalanced voltage sources. Experimental results demonstrate the effectiveness and sensitivity of the proposed diagnostic approach for the detection of stator faults in line‐connected and inverter‐fed motors. [ABSTRACT FROM AUTHOR]

Owing to the low equivalent impedance of six‐phase machines in the secondary subspace, in which currents do not contribute to torque production, low‐order current harmonics with high amplitude are likely to appear in the stator currents, increasing the harmonic distortion and the losses in the machine. In the case of permanent magnet synchronous machines (PMSMs), the main causes of these harmonics are machine asymmetries, non‐linearities of the power converters and back‐electromotive force harmonics. The existing predictive control strategies based on virtual vectors for six‐phase machines only regulate the currents mapped into the fundamental subspace, responsible for flux/torque production and leave the currents mapped into the secondary subspace uncontrolled. This study proposes a novel bi‐subspace predictive control strategy for six‐phase PMSMs with optimal amplitude virtual vectors, which is able to effectively regulate the currents in both subspaces simultaneously, leading to a significant minimisation of the current harmonic distortion. Moreover, the proposed control strategy also allows operating the six‐phase PMSM with a current unbalance between the two sets of windings. Both simulation and experimental results confirm the much better performance of the proposed strategy in comparison with other state‐of‐the‐art predictive control strategies found in the literature. [ABSTRACT FROM AUTHOR]

In this study, a wound rotor induction machine, as the core of a doubly‐fed induction generator (DFIG) system, is modelled using the magnetic equivalent circuit approach. The entire DFIG system is implemented in Matlab/Simulink environment, including the machine model, power converters, control system and the grid. The model, which includes the ability to simulate rotor inter‐turn short‐circuits (RITSCs), is subsequently used for the investigation and comparison of the impact of this fault in several DFIG quantities, namely in the stator current and in some control variables. This research also deals in detail with the differences found when comparing RITSC and unbalanced rotor resistance (URR) faults. It is observed that on the contrary to the URR, the stator current spectrum is more appropriate than the control signals for the detection of RITSC. Simulation and experimental results obtained with a DFIG system operating at sub‐synchronous and super‐synchronous speeds, for different values of the active and reactive powers injected into the grid, corroborate the findings reported in this study. [ABSTRACT FROM AUTHOR]

The operational reliability and high efficiency of modern electrical machines depend on the ability to transfer heat in the construction parts of the machine. Therefore, many authors study various thermal models and work on the development of effective heat dissipation. New insights and methods lead to improved techniques for the thermal design of electrical machines. This paper presents an experimentally validated thermal model of a permanent magnet synchronous motor (PMSM) with an improved slot winding model. It also deals with various approaches to homogenization and equivalent material properties of a tooth-coil winding sub-model. First, an algorithm for building a lumped-parameter thermal network (LPTN) of PMSM is described and its properties and problems are discussed. Subsequently, a sub-model of a slot with a winding based on the finite element method (FEM) is introduced. This sub-model is able to generate different conductor distributions based on probabilistic methods for a specified fill factor. This allows the verification of various homogenization approaches and at the same time it is a tool that automatically calculates thermal resistances for the LPTN. [ABSTRACT FROM AUTHOR]