Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Abstract

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.

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

“…Building upon the LSTM network, the EMD signal-processing tool is employed to extract representative features, enabling the detection of various stator short-circuit faults, even in the presence of a single short turn, with an accuracy of approximately 95% [126]. To estimate the ITSF of PMSMs, a medium network is employed within an encoderdecoder structure featuring an attention mechanism [124].…”

“…Building upon the LSTM network, the EMD signal-processing tool is employed to extract representative features, enabling the detection of various stator short-circuit faults, even in the presence of a single short turn, with an accuracy of approximately 95% [126].…”

An explainable deep learning approach for detection and isolation of sensor and machine faults in predictive maintenance paradigm

“…The proposed sensor and machine fault isolation technique applies its uniqueness to mitigate the drawbacks of the existing literature by isolating motor faults in the presence of sensor faults, without requiring additional redundant sensors. Although several works perform motor fault detection with high accuracy [8,13], they do not consider the faults occurring in the sensors that are used for monitoring the motor health. On the other hand, sensor fault detection and correction techniques [17,20] do not take the faults in the associated systems into account.…”

“…The three-phase induction motors (TIMs) are one of the major workhorses early detection of stator faults is essential in health monitoring and smooth operations of TIMs. In this respect, Alipoor et al [8] used empirical mode decomposition and long short-term memory network for the identification of SITF in TIM. In [4], a recurrence plot convolutional neural network has been used, in which the phase stator current is encoded into RGB images to detect stator winding fault severity in TIM.…”

Incipient Inter-Turn Short Circuit Detection in Induction Motors Using Cumulative Distribution Function and the EfficientNetv2 Model

“…Also, Guedidi et al [25] employ a modified SqueezNet, a convolutional neural network (CNN), to develop the fault detection of ITSC fault in IMs using 3D images generated from image transformation based on Hilbert transform (HT) and variational mode decomposition (VMD), reaching the detection of five SCTs. Alipoor et al [26] propose a long short-term memory (LSTM) model for detecting five specific ITSCs in an IM. This approach utilizes features derived from empirical mode decomposition alongside 21 statistical indices.…”