Search Results (153)

Incipient stator interturn short-circuit faults in permanent magnet synchronous motors produce only weak changes in the terminal currents, which limits the sensitivity of conventional amplitude- and unbalance-based indicators. This paper proposes a phase-wise diagnostic framework that combines multiscale sample entropy (MSE), topological data analysis (TDA), and a Gaussian mixture model (GMM). For each three-period current window, ten scale-dependent sample-entropy components and two persistent-entropy components are concatenated into a 12-dimensional feature vector. A separate GMM is trained for each phase using healthy data only. The resulting likelihood-based health scores are used for fault detection and faulty-phase localization, while physically defined score boundaries calibrated from measured short-circuit-current groups are used for severity assessment. Experiments on a 1.5 kW, 8-pole, 12-slot PMSM demonstrate class-wise recalls of 96.50–100% and an overall accuracy of 97.50% under the investigated operating conditions. The results show that the combined temporal and topological representation can reveal weak current changes that are difficult to distinguish using conventional terminal-current indicators. Full article

Three-phase induction motors (TIM) are widely used in industrial applications, and in-ter-turn short-circuit faults represent one of the most critical incipient failure modes. This paper proposes a lightweight and interpretable vibration-based fault diagnosis framework that combines frequency-domain feature extraction with a multilayer perceptron (MLP) classifier. Vibration signals acquired by MEMS (Micro-Electro-Mechanical Systems) accelerometers were segmented and processed using the Fast Fourier Transform (FFT). From the resulting spectra, a compact set of statistical features—including energy, spectral centroid, bandwidth, kurtosis, and skewness—was extracted and used as input to the MLP. The method was evaluated under healthy conditions and multiple inter-turn short-circuit fault scenarios, considering different affected phases and severity levels. Experimental results demonstrate competitive classification performance with significantly reduced computational cost compared to deep learning approaches. Full article

Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses. Full article

Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article

The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis. Full article

This paper presents a novel hybrid protection scheme based on Maximal Overlapped Discrete Wavelet Transform (MODWT) energy and a specialized difference function (DF) to accurately detect low-level inter-turn short-circuit faults in power transformers while maintaining high-selectivity features against transient conditions. Low-level inter-turn short-circuit faults (LIFs) in power transformers start at a low level and gradually spread to other windings. It is crucial to detect the fault in early stages and prevent further damage by disconnecting the faulty transformer immediately. A wavelet transform and difference function-based Transformer Differential Protection (TDP) algorithm is proposed in this paper. A differential protection scheme consists of two stages: feature extraction and fault detection. Maximum Overlapped Discrete Wavelet Transform (MODWT) energy and a difference function are used for feature extraction and an analytical logic is used for fault detection. It is also shown that this combination provides more reliable differential protection scheme than TDP with the wavelet transform only or TDP with a difference function (DF) alone. The method is assessed with experimental datasets collected from a laboratory-based, custom-built transformer which is specifically designed for validating the methods to detect LIFs. The method is evaluated according to a confusion matrix method with accuracy, dependability and sensitivity indices. The proposed TDP method detected all LIF cases, representing less than 2% of total windings. Therefore, the proposed hybrid algorithm represents an innovative step in applied system monitoring by providing a high-precision, software-based solution that enhances the operational reliability and resilience of existing TDP systems without requiring additional hardware. Full article

Inter-turn insulation failure is the primary cause of dry-type air-core reactor burnout, yet early detection remains challenging due to weak power-frequency fault signatures. This paper proposes an integrated diagnostic framework combining impulse oscillation testing, electromagnetic simulation, and a physics-informed graph neural network. A scaled-down four-layer parallel reactor model and an impulse oscillation platform are developed to extract dynamic equivalent inductance and resistance as sensitive fault indicators. Validated finite element simulations reveal that inter-layer insulation near high-voltage terminals endures the highest electric field stress, with local field strength increasing nearly eightfold under short-circuit faults. For fault localization, a Spatio-Temporal Physics-Informed Graph Neural Network (ST-PIGNN) is constructed, representing winding topology as a heterogeneous graph and embedding electromagnetic transient equations as physical constraints. On a test set of 120 samples, the proposed method achieves 94.17% fault layer classification accuracy and 6.84% axial localization mean absolute error under low-noise conditions, and maintains 85.83% accuracy with 8.12% error under strong-noise interference. The proposed method is currently at the proof-of-concept stage, and further validation on full-scale reactors is required before field deployment. Full article

In this study, the effects of stator winding inter-turn short-circuit faults occurring in an Axial Flux Synchronous Reluctance Motor (AF-SynRM) on motor performance were experimentally investigated. Within the scope of the study, a total of 12 different fault scenarios were created using different fault ratios and fault resistances, and the experiments were carried out under 4 different speed conditions and 5 different load conditions. In the analyses, phase current, fault current, power factor, and MTPA angle behaviors were evaluated. The obtained results demonstrated that significant increases in motor currents occurred particularly under low fault resistance and high load conditions. In addition, it was determined that the faults caused variations in power factor characteristic, while the MTPA angle, which represents the optimum operating point of the motor, shifted depending on the fault severity. Overall, the results revealed that stator winding inter-turn short-circuit faults (SITF) affect not only current levels but also energy conversion performance and control characteristics of the motor. As the first study in the literature to experimentally investigate the effects of SITF in an AF-SynRM structure within this scope, it is considered that the findings obtained will provide significant contributions to fault analysis and fault-tolerant drive systems. Full article

This paper proposes a novel fault-identification method for micro-coils in relays with forcibly guided contacts, a type of electromechanical elementary component (EMEC), combining a composite analytical model, a 2D thermal model, and a 1D-CNN. A low-order thermal circuit with one central node and four boundary nodes is established, while a two-dimensional anisotropic Poisson equation is used as a high-order calibration model. The two models are coupled through iterative correction of reusable thermal resistances. For thermal aging, enamel-film delamination, and inter-turn short-circuit faults, thermal-conductivity attenuation, asymmetric branch-resistance perturbation, and localized abnormal heat-source injection are introduced to generate physically constrained temperature sequences. Orthogonal centerline temperature distributions are extracted as one-dimensional feature vectors for 1D-CNN classification. Simulation results show that the hybrid model has an error of approximately 1.7% compared with finite-element results, and the trained 1D-CNN achieves 98.13% accuracy on 160 test samples. Experimental reconstruction and deep-feature visualization further verify its ability to distinguish normal, aging, delamination, and local short-circuit states. Full article

Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances. Full article

This paper deals with the investigation of the best topology of a five-phase fault-tolerant spoke-type permanent magnet (PM) motor for the propulsion of a multirotor aerial vehicle. This study is carried out through four stages. First, an assessment of the PM configuration effect on motor performance, considering three positions, namely surface PM, spoke-type PM, and V-shape PM. Second, an evaluation of the optimisation formulation problem on motor performance, where three formulations, respectively, involving either electric motor (EM) efficiency, EM efficiency and torque, or EM efficiency and active weight are considered for this purpose. Third, the stator winding configuration effect on performance in healthy and faulty operation mode (OM), e.g., open-circuit fault (OC) and inter-turn short-circuit (ITSC) fault, is also assessed. This evaluation is performed considering two winding configurations, namely fractional slot concentrated winding (FSCW) with single-layer (SL) or dual-layer (DL) winding. Fourth, a modified rotor geometry is proposed, based on the airgap length variation, in order to increase the airgap flux density amplitude and thus improve the motor torque and power densities. A comparative study, in this case, is performed with a classical rotor geometry in order to assess their influence on motor performance in healthy and faulty operation mode (OM). In addition, this paper presents a quantitative comparison of the proposed five-phase motor and a three-phase spoke-type PM motor, where the results, in healthy and faulty OM, show the interest of the proposed multiphase motor. Full article

In this study, we propose a data-driven diagnostic system that uses Extreme Gradient Boosting (XGBoost) to detect, classify, and assess the severity of multiple faults in permanent magnet synchronous motors (PMSMs). The three main fault categories that are the focus of the suggested method are inter-turn short-circuit (ITSC) faults, stator open-circuit faults, and permanent magnet demagnetization. To capture fault-specific characteristics and their development with severity, discriminative electrical features are retrieved from stator currents, flux linkage, and dq-axis values. Next, using the chosen electrical indications, an aggregated diagnostic index is created to facilitate defect diagnosis and severity quantification in a single learning process. The XGBoost-based model has been shown to produce excellent diagnostic accuracy and robust separation between various fault causes via extensive assessment. It also maintains dependable performance under previously unknown operating or fault situations. These findings show that an XGBoost-only approach offers a scalable and efficient way to monitor advanced PMSM conditions in industrial and safety-critical applications. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article

To address the challenges of insufficient transformer winding fault samples and the effective fusion of heterogeneous multi-source data, this study proposes an intelligent fault diagnosis method based on a time–frequency diffusion model and ConvNeXt-1D. First, data augmentation is performed on the original signals using the time–frequency diffusion model. Through a forward noise injection and reverse denoising process, the limited time-series samples are expanded. By alternately applying time-domain noise addition and frequency-domain blurring, the signals are jointly enhanced in the time–frequency domain, improving sample diversity and feature representation. Next, a ConvNeXt-1D network is constructed for multi-scale feature extraction and fault classification, incorporating an attention mechanism to efficiently fuse multi-source features and achieve precise fault identification. Finally, the proposed method is validated using dynamic model experiments. The results indicate that under typical fault conditions—such as inter-turn short circuits, winding deformation, and arc discharge—the proposed method achieves a diagnostic accuracy of 99.23 ± 0.29%. Compared with other classical models, the proposed approach demonstrates stronger classification capability and higher stability under small-sample data conditions. Full article

In this paper, a fault diagnosis and location method for internal short circuit faults of transformer winding in pumped storage power stations based on recurrent surge oscillography is proposed, and the comprehensive performance of three injection pulses of square wave, lightning pulse and sine pulse is compared. Firstly, the winding structure of the pumped storage transformer is analyzed, and a pulse injection scheme suitable for its structural characteristics is proposed. On this basis, the wave process and response characteristics of the injected pulse under inter-turn and inter-phase short circuit faults are analyzed, and a fault diagnosis scheme is proposed. Furthermore, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the novel Teager energy operator (NTEO) are used to obtain the time taken by the injection pulse to reach the fault point, and the precise location of the fault coil is realized by combining the traveling wave theory. Finally, the simulation results show the effectiveness of the proposed fault diagnosis and location method. At the same time, the comparative analysis shows that the comprehensive performance of the square wave pulse is the best. Full article