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Keywords = turn-to-turn short circuit

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32 pages, 9710 KiB  
Article
Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features
by Ádám Zsuga and Adrienn Dineva
Energies 2025, 18(15), 4048; https://doi.org/10.3390/en18154048 - 30 Jul 2025
Viewed by 220
Abstract
Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) [...] Read more.
Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power and Energy Systems)
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22 pages, 10293 KiB  
Article
Inter-Turn Short Circuits in Stator Winding of Permanent Magnet Synchronous Generator Dedicated for Small Hydroelectric Power Plants
by Adam Gozdowiak and Maciej Antal
Energies 2025, 18(14), 3799; https://doi.org/10.3390/en18143799 - 17 Jul 2025
Viewed by 174
Abstract
This article presents the simulation results of inter-turn short circuits in the stator winding of a permanent magnet synchronous generator (PMSG) dedicated for small hydroelectric power plants. During the calculations, a field–circuit model is used via ANSYS software. The simulations were performed for [...] Read more.
This article presents the simulation results of inter-turn short circuits in the stator winding of a permanent magnet synchronous generator (PMSG) dedicated for small hydroelectric power plants. During the calculations, a field–circuit model is used via ANSYS software. The simulations were performed for both a fault-free generator and faulty generator with various degrees of short-circuited turns under various operating conditions. The degree of stator winding damage is modeled by changing the number of shorted turns in one phase. The studied generator has a two-layer stator winding made of winding wire. In addition, it is made of three parallel branches. In this way, a more difficult-to-detect condition is simulated. We analyzed the influences of short-circuit fault on the magnetic field and their impact on generator behavior. The analysis of the obtained results indicates the possibility of using the measurement of the stator current histogram, higher-order harmonics of the stator current, back electromotive force (back EMF), phase current growth, and power factor fluctuations for early detection of an inter-turn short circuit. Full article
(This article belongs to the Section F: Electrical Engineering)
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19 pages, 8663 KiB  
Article
Digital Twin-Based Online Diagnosis Method for Inter-Turn Short Circuit Fault in Stator Windings of Induction Motors
by Yujie Chen, Leiting Zhao, Liran Li, Kan Liu and Cunxin Ye
Energies 2025, 18(12), 3063; https://doi.org/10.3390/en18123063 - 10 Jun 2025
Viewed by 474
Abstract
Inter-turn short-circuit fault is a common electrical issue in high-speed train traction motors, which can severely degrade motor performance and significantly shorten operational lifespan. Early detection is crucial for ensuring the safety of traction systems. This paper presents a digital twin-based method for [...] Read more.
Inter-turn short-circuit fault is a common electrical issue in high-speed train traction motors, which can severely degrade motor performance and significantly shorten operational lifespan. Early detection is crucial for ensuring the safety of traction systems. This paper presents a digital twin-based method for diagnosing stator winding inter-turn short-circuit faults in induction motors. First, an advanced rapid-solving algorithm is employed to establish a real-time digital twin model of the motor under healthy conditions. Second, a mathematical model characterizing stator winding faults is developed. Subsequently, fault detection and localization are achieved through analyzing three-phase current residuals between the digital twin model and the actual system. Extensive simulations and experiments demonstrate that the proposed method generates a fault index amplitude approximately 20 times larger than traditional sampling-value-based prediction methods, indicating exceptional sensitivity. The approach is minimally invasive, requiring no additional measurement equipment. Moreover, it maintains diagnostic capability even under motor parameter mismatch conditions, outperforming traditional methods. The proposed method demonstrates distinct advantages for high-speed train traction systems. It enables real-time monitoring and predictive maintenance, effectively reducing operational costs while preventing catastrophic failures. Full article
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17 pages, 3508 KiB  
Article
Zero-Sequence Voltage Outperforms MCSA-STFT for a Robust Inter-Turn Short-Circuit Fault Diagnosis in Three-Phase Induction Motors: A Comparative Study
by Mouhamed Houili, Mohamed Sahraoui, Antonio J. Marques Cardoso and Abdeldjalil Alloui
Machines 2025, 13(6), 501; https://doi.org/10.3390/machines13060501 - 7 Jun 2025
Viewed by 1122
Abstract
Three-phase induction motors are widely adopted in industrial systems due to their robustness, ease of maintenance, and simple operation. However, they are prone to various types of faults, notably stator winding faults. Previous research indicates that 20–40% of three-phase induction motor failures are [...] Read more.
Three-phase induction motors are widely adopted in industrial systems due to their robustness, ease of maintenance, and simple operation. However, they are prone to various types of faults, notably stator winding faults. Previous research indicates that 20–40% of three-phase induction motor failures are stator-related, with inter-turn short circuits as a leading cause. These faults can pose significant risks to both the motor and connected equipment. Therefore, the early detection of inter-turn short circuit (ITSC) faults is essential to prevent system breakdowns and improve the safety and reliability of industrial operations. This paper presents a comparative investigation of two distinct diagnostic methodologies for the detection of ITSC faults in induction motors. The first methodology is based on a Motor Current Signature Analysis (MCSA) utilizing the short-time Fourier transform (STFT) for the real-time monitoring of fault-related harmonics. The second methodology is centered around the monitoring of the zero-sequence voltage (ZSV). The findings from several experimental tests performed on a 1.1 kW three-phase induction motor across a range of operating conditions highlight the superior performance of the ZSV method with respect to the MCSA-based STFT method in terms of reliability, rapidity, and precision for the diagnosis of ITSC faults. Full article
(This article belongs to the Section Electrical Machines and Drives)
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19 pages, 5766 KiB  
Article
Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet
by Carlos Javier Morales-Perez, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez and Martin Valtierra-Rodriguez
Computation 2025, 13(6), 140; https://doi.org/10.3390/computation13060140 - 4 Jun 2025
Cited by 1 | Viewed by 494
Abstract
This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals [...] Read more.
This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. This preprocessing step enhances fault-related features, enabling improved learning while maintaining the simplicity of a lightweight CNN. The model achieved classification accuracies above 99.16% across all folds in five-fold cross-validation and demonstrated the ability to detect faults involving as few as three short-circuited turns. Comparative experiments with the Multi-Scale 1D-ResNet demonstrate that the proposed method achieves similar or superior performance while significantly reducing training time. These results highlight the model’s suitability for real-time fault detection in embedded and resource-constrained industrial environments. Full article
(This article belongs to the Special Issue Diagnosing Faults with Machine Learning)
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16 pages, 3659 KiB  
Article
Online SSA-Based Real-Time Degradation Assessment for Inter-Turn Short Circuits in Permanent Magnet Traction Motors
by Zhenglin Cheng, Xueming Li, Kan Liu, Zhiwen Chen and Fengbing Jiang
Electronics 2025, 14(10), 2095; https://doi.org/10.3390/electronics14102095 - 21 May 2025
Viewed by 424
Abstract
Inter-turn short circuits (ITSCs) in permanent magnet synchronous motors (PMSMs) pose significant risks due to their subtle early symptoms and rapid degradation. To address this, we propose an online real-time diagnostic method for assessing the degradation state. This method employs the Sparrow Search [...] Read more.
Inter-turn short circuits (ITSCs) in permanent magnet synchronous motors (PMSMs) pose significant risks due to their subtle early symptoms and rapid degradation. To address this, we propose an online real-time diagnostic method for assessing the degradation state. This method employs the Sparrow Search Algorithm (SSA) for the online real-time identification of fault characteristic parameters. Following an analysis of the fault mechanisms of inter-turn short circuits, a mathematical model has been developed to include the short-circuit turns ratio and insulation resistance. An evaluation index has also been developed to assess the degree of fault-related degradation. To address the strong nonlinearity of parameters in the fault model, the SSA is employed for the real-time joint identification of parameters that characterize the relationship between fault location and degradation degree. Simulation experiments demonstrate that the SSA achieves convergence within 40 iterations, with a relative error below 5% and absolute error less than 0.007, outperforming traditional algorithms like the PSO, a significant improvement in the early detection of degradation caused by inter-turn short circuits and a step forward in technical support ensuring greater reliability and safety for the traction systems used in rail transit. Full article
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35 pages, 10924 KiB  
Article
Winding Fault Detection in Power Transformers Based on Support Vector Machine and Discrete Wavelet Transform Approach
by Bonginkosi A. Thango
Technologies 2025, 13(5), 200; https://doi.org/10.3390/technologies13050200 - 14 May 2025
Cited by 1 | Viewed by 603
Abstract
Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and [...] Read more.
Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and generate fault currents that remain within normal operating thresholds. As a result, conventional protection schemes like overcurrent relays, which are tuned for high-magnitude faults, fail to detect such internal anomalies. Moreover, frequency response deviations caused by TWFs often resemble those introduced by routine phenomena such as tap changer operations, load variation, or core saturation, making accurate diagnosis difficult using traditional FRA interpretation techniques. This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. The proposed system employs region-based statistical deviation labeling to enhance interpretability across five well-defined frequency bands. It is validated on five real FRA datasets obtained from operating transformers in Gauteng Province, South Africa, covering a range of MVA ratings and configurations, thereby confirming model transferability. The system supports post-processing but is lightweight enough for near real-time diagnostic use, with average execution time under 12 s per case on standard hardware. A custom graphical user interface (GUI), developed in MATLAB R2022a, automates the diagnostic workflow—including region identification, wavelet-based decomposition visualization, and PDF report generation. The complete framework is released as an open-access toolbox for transformer condition monitoring and predictive maintenance. Full article
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29 pages, 7926 KiB  
Article
Analysis and Diagnosis of the Stator Turn-to-Turn Short-Circuit Faults in Wound-Rotor Synchronous Generators
by Haotian Mao, Khashayar Khorasani and Yingqing Guo
Energies 2025, 18(9), 2395; https://doi.org/10.3390/en18092395 - 7 May 2025
Viewed by 432
Abstract
In this paper, we introduce a health parameter and estimation algorithm to assess the severity of stator turn-to-turn/inter-turn short-circuit (TTSC) faults in wound-rotor synchronous generators (WRSG). Our methodology establishes criteria for evaluating the severity of stator TTSC faults in WRSG and provides a [...] Read more.
In this paper, we introduce a health parameter and estimation algorithm to assess the severity of stator turn-to-turn/inter-turn short-circuit (TTSC) faults in wound-rotor synchronous generators (WRSG). Our methodology establishes criteria for evaluating the severity of stator TTSC faults in WRSG and provides a specific solution for estimating both the severity of these faults and the resultant power loss. Our assessment methodology directly reflects the intrinsic impact of stator TTSC faults on the WRSG, offering enhanced efficiency, accuracy, and resilience to interference compared with traditional methods in estimating and gauging the TTSC severity. First, we demonstrate that it is impossible to determine the two fault parameters of the WRSG stator TTSC faults solely based on the voltage and current measurements. Subsequently, we introduce a novel health parameter for the WRSG stator TTSC faults and show that for a given generator and load, the dynamics of voltage and current during these faults as well as the resulting power loss are determined by this health parameter. We then detail the characteristics of the proposed health parameter and criteria for evaluating the severity of the WRSG stator TTSC faults. Furthermore, we present an estimation algorithm that is capable of accurately estimating the health parameter and power loss, demonstrating its minimal estimation error. Finally, we provide a comprehensive set of simulation results, including Monte Carlo results, to validate our proposed methodology and illustrate that our approach offers significant improvements in terms of the efficiency, accuracy, and robustness of the WRSG stator TTSC fault detection and isolation (FDI) over conventional methods. Full article
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25 pages, 5934 KiB  
Article
Detection and Localization of Rotor Winding Inter-Turn Short Circuit Fault in DFIG Using Zero-Sequence Current Component Under Variable Operating Conditions
by Muhammad Shahzad Aziz, Jianzhong Zhang, Sarvarbek Ruzimov and Xu Huang
Sensors 2025, 25(9), 2815; https://doi.org/10.3390/s25092815 - 29 Apr 2025
Viewed by 526
Abstract
DFIG rotor windings face high stress and transients from back-to-back converters, causing inter-turn short circuit (ITSC) faults. Rapid rotor-side dynamics, combined with the unique capability of DFIG to operate in multiple modes, make the fault detection in rotor windings more challenging. This paper [...] Read more.
DFIG rotor windings face high stress and transients from back-to-back converters, causing inter-turn short circuit (ITSC) faults. Rapid rotor-side dynamics, combined with the unique capability of DFIG to operate in multiple modes, make the fault detection in rotor windings more challenging. This paper presents a comprehensive methodology for online ITSC fault diagnosis in DFIG rotor windings based on zero-sequence current (ZSC) component analysis under variable operating conditions. Fault features are identified and defined through the analytical evaluation of the DFIG mathematical model. Further, a simple yet effective algorithm is presented for online implementation of the proposed methodology. Finally, the simulation of the DFIG model is carried out in MATLAB/Simulink under both sub-synchronous and super-synchronous modes, covering a range of variable loads and low-frequency conditions, along with different fault severity levels of ITSC in rotor windings. Simulation results confirm the effectiveness of the proposed methodology for online ITSC fault detection at a low-severity stage and precise location identification of the faulty phase within the DFIG rotor windings under both sub-synchronous and super-synchronous modes. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 7230 KiB  
Article
Design of a New Busbar for VFTO Suppression and Analysis of the Suppression Effect
by Huan Wang, Xixiu Wu, Yinglong Diao, Xiwen Chen and Bolun Du
Electronics 2025, 14(9), 1815; https://doi.org/10.3390/electronics14091815 - 29 Apr 2025
Viewed by 294
Abstract
Very fast transient overvoltage (VFTO) is characterized by short wavefront time, high amplitude and wide frequency. VFTO poses a threat to the safe operation of power equipment. In order to suppress the harmful effects of VFTO transmission on power equipment, a new type [...] Read more.
Very fast transient overvoltage (VFTO) is characterized by short wavefront time, high amplitude and wide frequency. VFTO poses a threat to the safe operation of power equipment. In order to suppress the harmful effects of VFTO transmission on power equipment, a new type of busbar based on the structure of “inductance + resistance” has been designed, which is called a “damping busbar”. In this study, the equivalent circuit of a damping bus including spurious parameters was developed. These stray parameters are formed between the GIS enclosure and the damping bus, and between the disk insulators and the damping bus. A damping bus simulation model was established based on the equivalent circuit to carry out analysis of the relationship between the structural parameters of the damping bus and the inductance generated by the damping bus. The simulation results showed that the number of turns plays a decisive role in bus inductance, and the relationship between the number of turns and inductance is approximately linear. Comparative analysis of multiple waveforms was carried out before and after the addition of damping buses to the GIS on a 550 kV test rig. The test data showed that the average amplitude of VFTO decreased by 20.36% after the installation of the damping bus, the number of breakdowns decreased by about 66.7%, and there was no obvious high frequency in the measured waveform after installation. In short, the damping busbar had a good suppression effect on the amplitude and frequency of VFTO, and reduced the number of breakdowns. This technique provides a novel solution for VFTO suppression. Full article
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18 pages, 5370 KiB  
Article
Diagnosis of Stator Inter-Turn Short Circuit Faults in Synchronous Machines Based on SFRA and MTST
by Junsheng Ding and Zhongyong Zhao
Energies 2025, 18(8), 2142; https://doi.org/10.3390/en18082142 - 21 Apr 2025
Viewed by 497
Abstract
As a key component of the power system, the good or bad conditions of synchronous machines will directly affect the stable supply of electric energy. The inter-turn short fault of the stator is one of the main dangers to the synchronous machine and [...] Read more.
As a key component of the power system, the good or bad conditions of synchronous machines will directly affect the stable supply of electric energy. The inter-turn short fault of the stator is one of the main dangers to the synchronous machine and is difficult to diagnose. Frequency response analysis has recently been introduced and used for detecting this type of fault; however, the fault degrees and locations cannot be directly recognized by traditional frequency response analysis. Therefore, this study improves the frequency response analysis by combining it with a deep learning model of a multivariate time series transformer. First, the principle of this study is introduced. Second, the frequency response data of short circuit faults are obtained using an artificially simulated experimental platform of a synchronous machine. The deep learning model is then well-trained. Finally, the performance of the proposed method is tested and verified. It concludes that the proposed method has the potential for classifying and diagnosing the inter-turn short circuit of stators in synchronous machines. Full article
(This article belongs to the Special Issue Condition Monitoring of Electrical Machines Based on Models)
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46 pages, 21569 KiB  
Article
Deep Learning-Based Fault Diagnosis via Multisensor-Aware Data for Incipient Inter-Turn Short Circuits (ITSC) in Wind Turbine Generators
by Qinglong Wang, Shihao Cui, Entuo Li, Jianhua Du, Na Li and Jie Sun
Sensors 2025, 25(8), 2599; https://doi.org/10.3390/s25082599 - 20 Apr 2025
Viewed by 721
Abstract
Wind energy is a vital pillar of modern sustainable power generation, yet wind turbine generators remain vulnerable to incipient inter-turn short-circuit (ITSC) faults in their stator windings. These faults can cause fluctuations in the output voltage, frequency, and power of wind turbines, eventually [...] Read more.
Wind energy is a vital pillar of modern sustainable power generation, yet wind turbine generators remain vulnerable to incipient inter-turn short-circuit (ITSC) faults in their stator windings. These faults can cause fluctuations in the output voltage, frequency, and power of wind turbines, eventually leading to overheating, equipment damage, and rising maintenance costs if not detected early. Although significant progress has been made in condition monitoring, the current methods still fall short of the robustness required for early fault diagnosis in complex operational settings. To address this gap, this study presents a novel deep learning framework that involves traditional baseline machine-learning algorithms and advanced deep network architectures to diagnose seven distinct ITSC fault types using signals from current, vibration, and axial magnetic flux sensors. Our approach is rigorously evaluated using metrics such as confusion matrices, accuracy, recall, average precision (AP), mean average precision (mAP), hypothesis testing, and feature visualization. The experimental results demonstrate that deep learning models outperform machine learning algorithms in terms of precision and stability, achieving an mAP of 99.25% in fault identification, with three-phase current signals emerging as the most reliable indicator of generator faults compared to vibration and electromagnetic data. It is recommended to combine three-phase current sensors with deep learning frameworks for the precise identification of various types of incipient ITSC faults. This study offers a robust and efficient pipeline for condition monitoring and ITSC fault diagnosis, enabling the intelligent operation of wind turbines and maintenance of their operating states. Ultimately, it contributes to providing a practical way forward in enhancing turbine reliability and lifespan. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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19 pages, 4643 KiB  
Article
Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Wavelet Packet Transform and Genetic Algorithm-Optimized Back Propagation Neural Network
by Ming Ye, Run Gong, Wanjun Wu, Zhiyuan Peng and Kelin Jia
World Electr. Veh. J. 2025, 16(4), 238; https://doi.org/10.3390/wevj16040238 - 18 Apr 2025
Viewed by 527
Abstract
In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase [...] Read more.
In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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25 pages, 5295 KiB  
Article
Interpretation of Partial-Discharge-Activated Frequency Response Analysis for Transformer Diagnostics
by Bonginkosi A. Thango
Machines 2025, 13(4), 300; https://doi.org/10.3390/machines13040300 - 4 Apr 2025
Viewed by 593
Abstract
This paper introduces a novel diagnostic approach called partial-discharge-activated impulse frequency response analysis (PD-IFRA), developed to overcome the limitations of conventional frequency response analysis (FRA) in detecting partial discharges (PDs) in power transformers. While traditional FRA with low-impulse-voltage excitation (LIVE) effectively identifies mechanical [...] Read more.
This paper introduces a novel diagnostic approach called partial-discharge-activated impulse frequency response analysis (PD-IFRA), developed to overcome the limitations of conventional frequency response analysis (FRA) in detecting partial discharges (PDs) in power transformers. While traditional FRA with low-impulse-voltage excitation (LIVE) effectively identifies mechanical deformations, inter-turn shorts, and insulation faults, it fails to detect incipient PD activity since PD phenomena require excitation beyond the PD inception voltage (PDIV) to initiate. This study proposes, for the first time, the extension of IFRA to moderate impulse voltage levels—without exceeding insulation limits—enabling the early and non-destructive detection of PDs. Experimental validation on a 315 kVA, 11 kV/420 V Dyn11 transformer shows that PD-IFRA effectively identifies PD-related impedance deviations within the 10 kHz–2 MHz frequency range, especially for PD sources associated with floating metal parts. Furthermore, the comparative analysis between normal, short-circuited, and PD-induced conditions demonstrates that PD-IFRA significantly enhances the precursory diagnosis of PDs where conventional FRA fails. This contribution advances transformer condition assessment by integrating PD sensitivity into FRA-based methods without compromising equipment safety. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 11833 KiB  
Article
Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
by Raul A. Ortiz-Medina, David A. Aragon-Verduzco, Victor A. Maldonado-Ruelas, Juan C. Olivares-Galvan and Rafael Escalera-Perez
Appl. Syst. Innov. 2025, 8(2), 50; https://doi.org/10.3390/asi8020050 - 4 Apr 2025
Viewed by 918
Abstract
Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An [...] Read more.
Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An experimental setup was developed using a three-phase transformer bank with three single-phase dry-type transformers. One transformer was modified to create controlled short circuits of two and four turns and to simulate a load imbalance by reducing the winding by four turns. The main contribution of this research is the development of a combined diagnostic approach using instantaneous space phasor (ISP) spectral analysis and infrared thermal imaging to differentiate between load imbalances and incipient interturn short circuits in transformers. This method enhances early fault detection by identifying distinctive electrical and thermal signatures associated with each condition. The results could improve transformer monitoring, reducing the risk of failure and enhancing grid reliability. Full article
(This article belongs to the Section Control and Systems Engineering)
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