Inter-turn Short Circuit Fault Research Articles

Analysis of interturn short circuit in regulating winding of power transformer based on field-circuit coupling

The interturn short circuit fault is one of the common faults in power transformers. At present, research on interturn short circuit faults focuses on high, medium, and low voltage windings, while there is relatively little research on interturn short circuit in regulating windings. Specifically, there is a lack of reported studies on the transient electromagnetic processes, magnetic field distribution, and electromagnetic force characteristics of interturn short circuits in regulating windings unconnected to the circuit. This study presents an actual fault scenario involving interturn short circuits occurring in the untapped portion of the regulating windings of a specific power transformer. A field-circuit coupled model was established to analyze the transient electromagnetic processes during the fault, and the model’s effectiveness was validated by comparing its results with actual fault recording data. Additionally, the magnetic field distribution and electromagnetic force characteristics during the fault were analyzed, and discussions were carried out regarding various ratios of short-circuit turns in the regulating windings. The results indicate that even when an interturn short circuit occurs in the portion of the regulating winding that is not connected to the circuit, the current in the short-circuited turns can reach several tens of times the rated value. Additionally, the leakage magnetic field and the electromagnetic force experienced by the short-circuited ring also increase significantly. The short-circuit ratio has a significant impact on the current of the short-circuited ring, leakage magnetic field intensity, and electromagnetic force. This study contributes to a better understanding of the impact of interturn short-circuit faults in the untapped portion of the regulating windings, offering crucial technical support for fault diagnosis and prevention of power transformers.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

A novel estimation methodology for multi-resonance equivalent inductance of transformer winding for inter-turn short-circuit fault detection

A novel, indirect measurement technique for estimating equivalent inductance associated with multi-resonance behavior of an isolated transformer winding is reported for both healthy and inter-turn short-circuit fault-condition. For healthy winding, the authors proposed measuring two unique admittance magnitude responses of the winding followed by utilizing their natural frequencies for estimating the said inductance. Here, the one response is for line-end excitation with neutral-end grounded and the other one is for two equal and opposite polarity excitations from two ends of the winding. For inter-turn shorting, driving-point admittance magnitude response with neutral-end open is also utilized. This inductance seems to be one important parameter for verifying the design constraint mainly for impulse behavior as well as for identifying inter-turn shorting of the winding. The proposed method was also verified by experimental measurements on two actual transformer windings; one of which incorporated an iron core within it. All pertinent analytical derivations and the associated results are also included in the paper.

A Detection Method for Slight Inter-Turn Short-Circuit Fault in Dry-Type Air-Core Shunt Reactors

Dry-type air-core shunt reactors are integral components in power transmission and distribution networks, designed to control reactive power and enhance system stability. However, inter-turn short-circuit faults (ISCFs) are common occurrences in shunt reactors, which are caused by various factors, including manufacturing defects, insulation degradation, or operational stresses. At the early stage of the ISCFs, the current does not reach a sufficient level to activate the protective equipment. These faults may lead to serious consequences, such as overheating, insulation breakdown, and even catastrophic failures, posing risks to the entire power system. Therefore, developing an effective and reliable detection method for ISCFs at the early stage is paramount. In this paper, a new method named the fault detection factor (FDF) based on equivalent resistance is presented to detect the slight ISCFs in dry-type air-core shunt reactors considering insulation resistance. In addition, the effect of noise signal existence in the monitoring process is taken into account. A moving average filter is adopted to guarantee both the sensitivity and the reliability of the proposed method. Ultimately, the simulation results of the FDF under different conditions are presented, which show the effectiveness and potential of the proposed method in observing and monitoring slight ISCFs.

The influence of ITSF on vibration of high voltage Line-Start permanent magnet synchronous motor

PurposeThe purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).Design/methodology/approachIn this paper, the ampere–conductor wave model of HVLSPMSM after ITSF is established. Second, a mathematical model of the magnetic field after ITSF is established, and the influence law of the ITSF on the air-gap magnetic field is analyzed. Further, the mathematical expression of the electromagnetic force density is established based on the Maxwell tensor method. The impact of HVLSPMSM torque ripple frequency, radial electromagnetic force spatial–temporal distribution and rotor unbalanced magnetic tension force by ITSF is revealed. Finally, the electromagnetic–mechanical coupling model of HVLSPMSM is established, and the vibration spectra of the motor with different degrees of ITSF are solved by numerical calculation.FindingsIn this study, it is found that the 2np order flux density harmonics and (2 N + 1) p order electromagnetic forces are not generated when ITSF occurs in HVLSPMSM.Originality/valueBy analyzing the multi-harmonics of HVLSPMSM after ITSF, this paper provides a reliable method for troubleshooting from the perspective of vibration and torque fluctuation and rotor unbalanced electromagnetic force.

A Search Coil Design Method of PMSM for Detection of Inter-Turn Short-Circuit Fault

Fault diagnosis systems based on search coils are widely used for detecting the inter-turn short-circuit (ITSC) fault, which is one of the most common electrical faults in electrical machines. Although various search coil structures have been proposed, there is not a systematic method proposed for the design of search coils. In addition, there is not a quantitative evaluation standard to evaluate the performances of search coils in each aspect, making it difficult to judge whether the proposed search coil structure is the optimal one. In this paper, the reliability, sensitivity and positioning ability of search coils are derived and evaluated by four defined characteristic indexes. And based on these four characteristic indexes, a systematic search coil design method based on the mutual inductance matrix is also proposed. With the proposed design method, the optimal search coil structures for different permanent magnet synchronous machines (PMSMs) are designed and optimized. It is found that the search coils designed by the proposed method have good performance in reliability, sensitivity and positioning ability. The performances of designed search coils are validated by the finite element analysis (FEA) and experiment.

Diversified Diagnosis Strategy for PMSM Inter-Turn Short-Circuit Fault via Novel Sliding Mode Observer

Diversified Diagnosis Strategy for PMSM Inter-Turn Short-Circuit Fault via Novel Sliding Mode Observer

One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines

Achieving a model which accurately diagnoses faults in electric machines is a vital step in data-driven fault detection approaches. To this aim, this paper proposes a Long Short-Term Memory (LSTM) regulated deep residual network for data-driven fault diagnosis purposes in electric machines. The advantages of the proposed network are that it is more general in terms of fault type and measurement, results in a more accurate model for fault classification, and has faster convergence compared with other networks, such as conventional deep residual networks. In order to prove these advantages of the proposed network, it is evaluated by two different types of datasets. One is the Inter-Turn Short Circuit (ITSC) fault in a Permanent Magnet Synchronous Motor (PMSM) with the data of measured three-phase current. The second one is the Case Western Reverse University (CWRU) bearing fault dataset with the vibration measurements. The performance of the network is also compared with other networks. Results reveal that the model can accurately detect both types of faults by two different measurements with a test accuracy of 100%. Furthermore, it converges faster than other networks in the training procedure.

Investigation of Inter-Turn Short-Circuit Fault of PM Machines Using PWM Voltage-Based Modeling

Investigation of Inter-Turn Short-Circuit Fault of PM Machines Using PWM Voltage-Based Modeling

Diagnosis of inter-turn short circuit fault in IPMSMs based on the combined use of greedytracking and random forest

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm. The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

An Expeditious Algorithm for Identification and Classification of Rotor Faults in Salient Pole Synchronous Generators

Providing secure electricity depends on the reliable operation of the synchronous generators in power plants. Inter-turn short circuit (ITSC) and dynamic eccentricity (DE) faults are among the most prevalent fault types in wound field synchronous generators (WFSGs). Among the various signals used to detect faults such as current, voltage, and vibration, the stray magnetic field provides higher sensitivity for early-stage ITSC and DE detection. The introduced detection methods rely on comparison of various signals frequency spectrum's from fault and healthy generators. However, obtaining a frequency spectrum of a healthy synchronous generator that has been operating for decades in a power plant is almost impossible. A unique pattern is introduced using fast Fourier transform that can be implemented on a low-end microcontroller and ideal for IoT solutions. The efficacy of the proposed pattern is examined by finite element modeling and validated on a 100 kVA WFSG setup in the laboratory and field test in a power plant with a power rating of 22 MVA.

Dynamic Modeling for Interturn Short Circuit Faults in Symmetrical Six-Phase FSCW-PMSMs With Unaligned Fault Coil

Dynamic Modeling for Interturn Short Circuit Faults in Symmetrical Six-Phase FSCW-PMSMs With Unaligned Fault Coil

Identification Method of Interturn Short Circuit Fault for Distribution Transformer Based on Power Loss Variation

Identification Method of Interturn Short Circuit Fault for Distribution Transformer Based on Power Loss Variation

Investigating Pressure Patterns in Transformer Tanks after an Interturn Short Circuit: A Finite Element Approach

When an inter-turn short-circuit fault occurs during the operation of a transformer, the arc generates energy that causes the temperature in the tank to rise. This in turn increases the temperature of the insulating oil, vaporizing it, and the rising pressure in the tank acts on the tank such that it can easily explode. The arc energy is related to the initial pressure of the gas and its production in the tank. The pressure wave propagates in insulating oil, and the transient pressure at any point in the path of the pressure wave is the superposition of vectors of forward- and backward-traveling waves. The authors of this study applied a finite element simulation software to establish a model of the transformer tank and used it to analyze the changes in pressure in the tank and on the wall as well as the factors influencing this phenomenon. The results show that the wall pressure of the transformer increases with time after the failure of the interturn short circuit. The pressure wave travels from the initial position of the arc to the periphery and decreases with diffusion effects. The influence of pressure on the transformer tank can be reduced by selecting an appropriate location for the pressure-release valve and can in turn prevent the tank from rupturing due to the impact of rising pressure.

Permanent magnet synchronous motor inter-turn short circuit diagnosis based on physical-data dual model under oil-drilling environment

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods.

Diagnosis and location of inter-turn short circuit fault in pumped storage unit

In order to monitor the inter-turn insulation health of the rotor windings of pumped storage unit (PSU) in real time, a fault diagnosis method based on magnetic field detection is proposed in this study. Firstly, the characteristic harmonics of the air-gap flux density (AGFD) corresponding to the different states are obtained by deriving the expressions for the AGFD in the normal and inter-turn short circuit (ITSC) states of the rotor winding. Secondly, the detection principle of Hall sensor and its application are studied, and an intelligent detection theory combining frequency-resolved spectrum (FRS) and radar chart is proposed. Thirdly, constructing a electromagnetic model based on the actual operating PSU size, then the time/frequency characteristics of the Hall induced electromotive force (HIEF) are analysed, from which the intelligent diagnostic spectrums are constructed for fault detection. Finally, the Hall probe detection sensitivity is calibrated to 1 mV/mT. By installing Hall probe on the slot wedge surface of the generator stator, HIEF signals for different ITSC levels are acquired and integrated with pole positioning signals for condition monitoring, which can effectively identify and locate ITSC fault, while the detection sensor has significant advantages in terms of size and installation simplicity.

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM

Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.