Inter-turn Short Circuit Fault Research Articles

Inter-Turn Short Circuit Fault Diagnosis of Synchronous Generator in Field Winding Based on the Eigenvalues of the State Matrix

Inter-Turn Short Circuit Fault Diagnosis of Synchronous Generator in Field Winding Based on the Eigenvalues of the State Matrix

A Novel Deep Fusion Network Based on Statistical Denoising and Deep Learning Method for Transformer Inter-Turn Short Circuit Fault Prediction

A Novel Deep Fusion Network Based on Statistical Denoising and Deep Learning Method for Transformer Inter-Turn Short Circuit Fault Prediction

Experimental investigation in induction motors using signal processing techniques for early detection of inter-turn short circuit faults

ABSTRACT This paper presents a method based on advanced signal processing techniques for the detection of inte-rturn short circuit faults in an induction machine. The techniques presented can be classified into power spectral density estimation techniques and time-frequency techniques. The theoretical principles of these methods are presented and their performances are compared from the stator current analysis (MCSA). This article proposes a comparative study of these signal processing techniques. One is based on the power spectral density estimation techniques, such as Periodogram and Welch periodogram. The second method is based on the Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energy levels (HDWT). This technique is powerful for the non stationary signal processing. Moreover, the combination HT-DWT has assured its ability to evaluate the severity in detecting faults of the shorted turns and it is effective for nonstationary and stationary signals to extract useful information for the detection of ITSC fault. The techniques have usd experimentally data issued from the induction motor.

Equivalent Broadband Electrical Circuit of Synchronous Machine Winding for Frequency Response Analysis Based on Gray Box Model

Frequency response analysis (FRA) is a popular tool to detect transformer winding deformation faults. It has been recently used to diagnose the winding inter-turn short circuit fault of the electric machine. The application of FRA is based on the fact that the winding can be equivalent to an electrical circuit consisting of resistance, inductance, and capacitance. Therefore, to obtain an accurate broadband equivalent circuit model of synchronous machine and perform the fault analysis and diagnosis, this paper introduces the gray box model technique based on the state space and genetic algorithm. The electrical circuit of a form-wound coil of a 4 kW machine with known electrical parameters is used to verify the proposed method primarily. Besides, the equivalent circuit modeling is carried out and experimentally verified on an actual 5 kW synchronous machine. Finally, the fault simulation analyses of winding inter-turn short circuits are performed based on the proposed equivalent electrical circuit. The verification result shows that the circuit parameters identified by the proposed method are accurate. Moreover, the fault simulation results indicate that the inter-turn short circuit faults can cause apparent variations in the frequency response curves. It is feasible to detect the winding fault of the synchronous machine by the FRA method.

Winding Condition Monitoring for Inverter-Fed PMSM Using High-Frequency Current Injection

This article proposes a winding condition monitoring scheme for inverter-fed permanent magnet synchronous machine (PMSM) based on high frequency (HF) current injection. By setting specific current references for the HF positive and negative sequence current regulators, HF current can be injected into different phase winding pairs, and then the HF line voltage deviations are extracted for winding condition monitoring. The amplitude of the extracted HF line voltage deviation is defined as the condition indicator to evaluate the condition of phase winding. In the proposed method, the conditions of all phase windings can be separately monitored, where not only inter-turn short-circuit fault (ISF) in one phase but also ISFs in multiple phases can be accurately identified. Simulations and experiments have been carried out to validate the proposed method. It shows that the proposed method has high sensitivity to ISF and negligible sensitivity to the operation conditions of the PMSM.

Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction

This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.

Fault Injection Model of Induction Motor for Stator Interturn Fault Diagnosis Research Based on HILS

Recently, in order to ensure the reliability and safety of trains, online condition monitoring and fault diagnosis of traction induction motors have become active issues in the area of rail transportation. The fault diagnosis algorithm can be developed and debugged in a real-time environment based on hardware-in-the-loop simulation (HILS). However, the dynamic space model of induction motors with stator interturn short-circuit faults faces the problem that the faulty state and the healthy state are not compatible, which is inconvenient for the HILS. In this paper, a fault injection model is proposed for the first time, which can realize the online switching between the healthy state and the faulty state of the motor. The feasibility and effectiveness of the proposed model are verified by simulation experiments the based on MATLAB/Simulink and dSPACE HILS platforms.

Pattern Recognition of Interturn Short Circuit Fault in a Synchronous Generator Using Magnetic Flux

This article provides a novel approach for discriminating the interturn short circuit (ITSC) fault in a salient pole synchronous generator (SPSG) based on the use of noninvasive sensors. A stray magnetic field is used as a signal that can provide valuable data regarding the health condition of the SPSG. Generally, the captured signal under an ITSC fault is examined using a classical signal processing tool based on fast Fourier transforms (FFTs). The amplitude of the side-bands to the frequency spectrum is increased by ITSC, eccentricity, and broken damper-bar faults. Therefore, determining the type of fault is not possible by analyzing the FFT side-bands. A unique feature is introduced using a short-time Fourier transform to identify the ITSC in its early stage. The proposed feature can locate and recognize the ITSC fault with high accuracy in either the no-load or the full-load operation of the SPSG. This novel methodology is verified by applying an ITSC fault to a 100 kVA custom-made SPSG.

Performance Evaluation of Signal Processing Tools Used for Fault Detection of Hydrogenerators Operating in Noisy Environments

Signal processing plays a crucial role in addressing failures in electrical machines. Experimental data are never perfect due to the intrusion of undesirable fluctuations unrelated to the investigated phenomenon, namely so-called noise. Noise has disturbing effects on the measurement data and, in the same way, could diminish or mask the fault patterns in feature extraction using different signal processors. This article introduces various types of noise occurring in an industrial environment. Several measurements are performed in the laboratory and power plants to identify the dominant type of noise. Fault detection in a custom-made 100-kVA synchronous generator under an interturn short-circuit fault is also studied using measurements of the air-gap magnetic field. Signal processing tools such as fast Fourier transform, short-time Fourier transform (STFT), discrete wavelet transform, continuous wavelet transform (CWT), and time-series data mining are used to diagnose the faults, with a central focus on additive noise impacts on processed data. Two novel patterns are introduced based on STFT and CWT for interturn short-circuit fault detection of synchronous generators that do not need a priori knowledge of a healthy machine. Useful methods are presented for hardware noise rejection.

Reactive power characteristics and vibration properties under SISC in synchronous condensers

A stator interturn short-circuit (SISC) fault in synchronous condensers can bring about serious damage if the fault is not detected in time. At present, researches on SISC fault characteristics and diagnosis methods are still scarce because only in recent years are synchronous condensers adopted in power grids. Different from previous studies, this paper considers the practical operation condition of synchronous condensers that need to change exciting current frequently and work in either under-excitation or over-excitation according to the actual demand of power grids. Detailed reactive power and unbalanced magnetic pull (UMP) expressions under SISC are deduced firstly based on the analysis of the magnetic flux density variation. It can be seen that the reactive power increases under under-excitation running and decreases under over-excitation running after SISC. The second, fourth and sixth harmonics of stator UMP/vibration are enlarged under the SISC. Specifically, the sixth harmonic augments with the increase of exciting current in the over-excitation running, while the variation of the sixth harmonic with exciting current depends on parameters and fault degree of synchronous condensers under the under-excitation operation. Moreover, the two-dimensional finite-element analysis (FEA) and experimental tests are performed on a customized synchronous machine. In addition, the FEA of a TTS-300-2 synchronous condenser is accomplished in order to validate the proposed theoretical analysis further. The research conclusions of fault characteristics in this paper provide potential solutions for the monitoring and diagnosis of SISC in synchronous condensers.

Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis

This research puts forward a novel approach by proposing mathematical equations to estimate the fault intensity index in induction motor with stator inter-turn short circuit (ITSC) fault. Determination of the failure index is significant to create appropriate mitigation methods, as well as to define a safe working area for the motor to prevent the subsequent damage to the stator winding. Discrete Wavelet Transform (DWT) is employed to analyze the obtained raw currents in the time domain. From the detailed coefficients, the statistical parameter maximum norm has been computed under a variety of loading conditions, in addition to several fault severities. The originality of this work is to present an efficient and robust method based on new mathematical equations to detect the ITSC fault at an early level and to precisely specify the number of shorted turns in the defective phase. The methodology provides notable results, reduces computational time consumption, and simplifies the estimation process remarkably. In order to validate the efficacy of the proposed method, several experiments are conducted on the machine. The hardware experimental outcomes demonstrate the practicability and competency of the methodology, with a high level of correctness.

A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation

Induction motors (IMs) are one of the most commonly used rotating machines in industry. In order to avoid downtimes and economical losses, development of condition monitoring systems is of paramount importance. Inter-turn short circuit (ITSC) faults are one of the most commonly occurring faults in IMs. In this regard, motor current signature analysis (MCSA) is a low cost and noninvasive technique, requiring only the current signal to perform the condition monitoring. Also, the development of tools that emulate IM faults, allow the design, calibration and validation of MCSA based techniques. In this work, a multilayer neural network-based model to reproduce the current signatures associated with ITSC fault conditions is presented. The model considers both five severities of ITSC faults and four torque levels. This model is also implemented on a power hardware in the loop scheme to provide a novel tool to test condition monitoring systems of IMs.

Self-Powered Diagnostic System for AC Machines Based on Inhomogeneity of Magnetic Field Distribution in Air Gap

This article presents a self-powered system for supplying rotor measurement equipment and detecting stator winding inter-turn short circuit, and broken rotor bar faults in AC Machines. This system uses harvesting coils to harvest air-gap magnetic field pulsation and generate energy for powering equipment installed on the rotor. Furthermore, installing this system on the stator or rotor of AC machines provides a voltage signal appropriate for fault detection purposes. To determine specifications of the harvesting coils – coils turn numbers, and their optimal mounting positions – numerous finite element simulations have been carried out. In addition, a fault detection method has been proposed to detect broken rotor bar and inter-turn short circuit faults using induced voltage in the harvesting coils. To verify the performance of the proposed system, some experimental and simulation tests have been performed on various AC machines. The results have confirmed the applicability of this system in self-powering applications and fault detection purposes.

Localization and evaluation method of interturn short circuit fault in BLDC motor

Localization and evaluation method of interturn short circuit fault in BLDC motor

Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault

Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate for the power loss caused by the faulty WT. With the proposed method, the competitiveness and the operational capacity of offshore WFs can be upgraded.

Fault diagnosis method based on multi‐source information fusion for weak interturn short circuit in synchronous condensers

The interturn short circuit fault in rotor windings is a common fault in a synchronous condenser. Aiming at the early faults that are difficult to detect, the theory of multi-source information fusion is applied herein for the identification of short circuit faults in the rotor windings of a synchronous condenser. Firstly, a field-circuit-network coupling model of a synchronous condenser for high-voltage direct current (HVDC) transmission was constructed using SIMULINK/ANSYS. The air gap magnetic flux density of the interturn short circuit regulator with a rotor winding was obtained, via co-simulation, before and after the commutation failure in the HVDC inverter. Further, three-dimensional models of the stator and rotor of the synchronous condenser were established using ANSYS/Workbench; the stator and rotor vibration responses of the synchronous condenser were monitored before and after the system commutation failure. Secondly, three types of evidence bodies were obtained based on the Dempster–Shafer evidence theory. The results show that the confidence level of the evidence body of each faulted component of the synchronous condenser increases, and the accuracy of fault recognition also increases upon system commutation failure. Finally, a three-pair-pole synchronous motor (MJF-30-6) was used to simulate the rated and overexcited operations of the synchronous condenser for experimental verification of the feasibility of the proposed method. This method provides a reference for the identification of weak interturn short circuit faults in synchronous condensers.

Induction machine stator short-circuit fault detection using support vector machine

PurposeThis paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.Design/methodology/approachTo evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.FindingsThe obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.Originality/valueThe original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

Stator Faults Detection on Induction Motors Using Harmonic Sequence Current Components Analysis

In this paper we propose a strategy for detecting interturn short circuits on stator windings of three-phase induction motors (IM). The strategy is based on an analytical model that includes a stator fault in any of the phase windings considering the h-th harmonic in the supply voltage. To find the adequate model, we propose an equivalent electrical circuit of the IM in steady state, considering each sequence component. The proposed strategy allows the detection of an interturn short-circuit fault by monitoring the fifth-order harmonic component. We present simulation and experimental results for the faulty IM and different operating conditions. In the laboratory tests, we use a programmable power supply in order to synthesize distorted and unbalance voltages. Also, the windings of a three-phase IM have been modified to generate stator faults and validate the proposed model. The results show that the sustained increase of the fifth-order harmonic of positive-sequence current is a good indicator of the fault severity.

Transformer inter-turn winding fault detection based on no-load active power loss and reactive power

An inter-turn fault in a transformer can cause severe damage to transformer windings if not detected quickly. In this paper, the fault diagnosis is discussed by examining the no-load active power loss (NLAPL) and no-load reactive power (NLRP) of the transformer. Two indices, Total harmonic distortion of active power (THDAP) and Total harmonic distortion of reactive power (THDRP), based on odd harmonic components of NLAPL and NLRP, are proposed. To validate the obtained results, the acquired outcomes of tests on a physical device are compared with the results of the finite element analysis. To do this, three-dimensional model of a single-phase transformer is simulated in the transient mode using ANSYS MAXWELL 17.02. Results show that in the presence of an inter-turn short circuit fault on the transformer high voltage (HV) windings, the low voltage side current and NLAPL increase while the variation in NLRP is very small. In addition, from the transient analysis point of view, it becomes clear that THDAP and THDRP are reduced in the presence of an inter-turn fault.

Electromagnetic Torque Characteristics Analysis of Nuclear Half-Speed Turbine Generator with Stator Winding Inter-Turn Short Circuit Fault

To research the variation characteristics and influence factors of air gap magnetic field and electromagnetic torque of nuclear power half-speed turbine generator with stator winding inter-turn short circuit, the two-dimensional transient electromagnetic field of generator-external circuit-grid coupling mathematical model is established by field-circuit-network coupling method. Taking the national laboratory’s generator as an example, and comparing the simulation results of the inter-turn short circuit of the armature winding with experimental data, the correctness of the model is validated. On this basis, the simulation model of a 1407MVA nuclear power half-speed turbine generator is built. Then the stator winding inter-turn short circuit fault is simulated when the generator is connected to the grid with rated load. The characteristics of air gap flux density and electromagnetic torque are obtained before and after fault, as well as, the relationship between their each component and the fault degree is obtained. At the same time, the variation law of air gap flux density and electromagnetic torque is obtained when the relative position between fault winding and magnetic pole changes. The theoretical reference for fault diagnosis and localization using harmonic electromagnetic torque after fault is provided.