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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]

This study presents a new fault‐tolerant predictive power control strategy for doubly‐fed induction generators (DFIGs) used in wind energy applications, with the rotor fed by a three‐level neutral point clamped converter. The proposed control strategy is able to maintain the system in operation with a good performance after the occurrence of either open‐circuit (OC) or short‐circuit faults in the insulated‐gate bipolar transistors (IGBTs) of the rotor‐side converter (RSC), thus reducing the downtime and maintenance costs of wind turbines. The fault‐tolerant strategy takes advantage of the discrete nature and flexibility of finite control set model predictive control strategies and restricts the possible switching states of the power converter according to the type of fault and its location. A diagnosis method for IGBT OC faults in the RSC, based on voltage errors, is also proposed for the considered system. This method uses the estimated rotor voltages from the DFIG model, thus avoiding the use of any extra sensors. Accurate and fast detection of OC faults in both interior and exterior IGBTs is achieved throughout the entire DFIG operational range. The effectiveness of the fault diagnosis method and fault‐tolerant control strategy is validated with several experimental results. [ABSTRACT FROM AUTHOR]

A new real‐time method able to diagnose multiple semiconductor open‐circuit (OC) faults in a three‐level neutral‐point clamped inverter is introduced in this paper. The proposed diagnostic method is based on the evaluation of the output pole voltages and output currents of the inverter. The proposed method allows a real‐time detection and localisation of multiple OC faults in all active power switches and clamp‐diodes within one modulation period. Experimental results obtained for different operating conditions of the inverter demonstrate the applicability and performance of the proposed diagnostic approach. [ABSTRACT FROM AUTHOR]