Detection Of Inter-turn Short Circuits Research Articles

Interturn Short Circuit Detection of Pumped Storage Unit Based on Optical Fiber Weak Magnetic Sensing

Interturn Short Circuit Detection of Pumped Storage Unit Based on Optical Fiber Weak Magnetic Sensing

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.

Fault Detection of Interturn Short Circuit in Induction Motors Under Nonstationary Conditions and Unbalanced Supply Voltage

Fault Detection of Interturn Short Circuit in Induction Motors Under Nonstationary Conditions and Unbalanced Supply Voltage

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Inter-Turn Short-Circuit Detection Through Differential Admittance Monitoring in Transformers

Inter-Turn Short-Circuit Detection Through Differential Admittance Monitoring in Transformers

Detection of Inter-Turn Short Circuits in Induction Motors under the Start-Up Transient by Means of an Empirical Wavelet Transform and Self-Organizing Map

Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and affects induction motors (IMs), leading to catastrophic damage. Therefore, this work proposes the use of the empirical wavelet transform to characterize the time frequency behavior of the IM combined with a self-organizing map (SOM) structure to perform an automatic detection and classification of different severities of ITSC. Since the amount of information obtained from the empirical wavelet transform is big, a genetic algorithm is implemented to select the modes that allow a reduction in the quantization error in the SOM. The proposed methodology is applied to a real IM during the start-up transient considering four different fundamental frequencies. The results prove that this technique is able to detect and classify three different fault severities regardless of the operation frequency.

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Fault detection and fault tole rant control of interturn short circuit of double redundancy motor

This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor.

Inter-turn fault detection of induction motors using a method based on spectrogram of motor currents

Steady-state signatures seem more reliable than incipient fault signatures for detection of inter-turn short circuit (ITSC) fault in induction motors (IMs). An efficient detection approach is proposed based on the deviation of the histogram relevant to the time–frequency plane’s image of the steady-state modal current signal from the standard normal distribution. First, the misleading frequency components like 3rd, 5th, and 7th harmonics are excluded using Kalman Filter (KF). Then, from the time–frequency spectrogram of the signal, the converted gray-level image and its histogram are obtained. A considerable deviation of the histogram distribution from a normal distribution is observed in the case of ITSC fault, used for the detection. A cumulative index including normalized skewness and kurtosis of the histogram is used as the criterion of the detection. Finally, a novel threshold setting method based on Otsu’s threshold principle is proposed, which efficiently distinct faulty conditions from healthy operations. The required data for assessment of the approach is gathered from different experiments carried out on a test bench, subjected to various fault percentages and different load levels. The results confirm the effectiveness of the proposed methodology.

Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

Using Flux Linkage Difference Vector in Early Inter-Turn Short Circuit Detection for the Windings of Offshore Wind DFIGs

Winding faults are the most commonly occurring type of fault in offshore doubly-fed generator units. Such faults are difficult to detect and repair, and therefore higher fault identification accuracy is required. Most of the existing research analyzes stator and rotor winding faults separately, but due to the coupling characteristics between windings, it is necessary to analyze doubly-fed induction generator (DFIG) winding faults in a unified way. This paper proposes a general fault diagnosis method that can be applied to both the stator and rotor winding faults of DFIGs. First, a general mathematical model of a windings short circuit of a doubly-fed generator is established. Then, according to the mechanism of a turn-to-turn short circuit fault of a DFIG and the expression of a bilateral flux linkage between stator winding and rotor winding, a new fault feature-bilateral flux linkage difference vector (FLDV) is obtained by mathematical derivation. Finally, the recommended value for an experimental motor of feature quantity is set. The proposed method for inter-turn fault (ITF) diagnosis of stator and rotor windings provides technical support for the sustainable development of offshore wind power. A simulation and experiment prove that FLDV has high sensitivity and high reliability for windings ITF diagnosis.

Model Predictive Controller Utilized as an Observer for Inter-Turn Short Circuit Detection in Induction Motors

In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.

The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

Observer-based IM stator fault diagnosis: Experimental validation

In this paper, an experimental validation of an efficient approach to the Fault Detection and Isolation (FDI) of Induction Motor (IM) is proposed. The problem of Inter-turn short circuits (ITSC) in the stator windings is addressed. By introducing fault factors in the IM model an observer-based residual generator is designed, allowing the detection of ITSC in stator windings. The residual generator is built around an extended Kalman Filter (EKF) in order to estimate state variables and fault factors, which permits the evaluation of the severity of the fault. To overcome the problem of tuning the EKF a PSO algorithm is developed. It carries out a heuristic search of the noise matrices by optimizing a cost function. The proposed solution is validated by computer simulations and by real-time implementation on dSPACE 1104 Digital Signal Processor (DSP) test-bench under the healthy and the faulty conditions of IM. To perform tests under faulty conditions, an IM with customized design is built and the stator is rewound permitting to create ITSC. The results reveal the quick detection of the faults, the quantification of its severity and confirm the efficacy of this observer-based FDI algorithm.

Transient envelope current analysis for inter-turn short-circuit detection in induction motor stator

Electrical machines are subject to faults which, depending on the type and importance of the plant, can cause material, economic and social damage. The reliability required for electric motors constantly grows due to the importance of their applications and technological advancement. One of these possible faults is an inter-turn short circuit of the stator winding. This is a very severe failure because its evolution to a worst fault is very fast. Thus, the initial detection of the inter-turn short circuit is of fundamental importance to avoid further damage to the motor. Steady-state analysis is not adequate when applied in some cases, such as unloaded machines, loads with oscillating torque and power with voltage fluctuations. The present work brings a new study of the transient current to analyze the beginning of inter-turn short circuits in the induction motor stator windings. The proposed methodology extracts the second component from the transient envelope of the motor starting current signals by principal component analysis. These components reflect the asymmetry caused by the initial short circuit and carry the desired fault information. By variance analysis it becomes possible to monitor the evolution of a short-circuit fault. Numerical and experimental signals are provided to validate the effectiveness of the novel technique.

Stator Short-Circuit Fault Detection and Location Methods for Brushless DFIMs Using Nested-Loop Rotor Slot Harmonics

This article proposes and demonstrates a fault-detection method to locate interturn short circuits (ITSCs) in the stator windings of a brushless doubly fed induction machine (BDFIM). The detection of ITSC is important in machine health prognostics as ITSC is an early stage fault that may lead to other faults such as coil-to-coil and coil-to-ground. BDFIM consists of power and control windings in the stator that are magnetically coupled through a nested-loop rotor winding. Existing ITSC detection algorithms use rotor slot harmonics in stator current spectra as fault indicators for only conventional doubly fed induction machines. However, these algorithms cannot be applied to BDFIM due to its different stator/rotor winding structure. This article primarily proposes a new analytical formulation for the nested-loop rotor slot harmonics (NRSHs) as ITSC fault indicators in BDFIMs. Using NRSHs, a detection algorithm is also proposed to detect and locate ITSC in power/control windings of BDFIMs. The proposed algorithm is verified based on numerical analysis of a BDFIM using the finite-element method. The accuracy of the proposed fault-detection algorithm is also experimentally investigated and demonstrated using a BDFIM test rig. Both numerical and experimental test results confirm effectiveness of the proposed ITSC fault-detection algorithm.

Severity Estimation of Interturn Short Circuit Fault for PMSM

This paper presents a novel method to estimate the number of shorted turns in a permanent magnet synchronous machine (PMSM) following the detection of interturn short-circuit (ITSC) fault and its location. In this proposed method, PMSM is excited through a low sinusoidal voltage at standstill condition to obtain the winding resistance and synchronous inductance by current response. It is shown that the ITSC fault introduces variation in the current response, which can be used to calculate the number of shorted turns under zero fault resistance assumption. Using this practical procedure, the fault severity can be estimated directly in a straight-forward manner. In other words, the severity estimation for a given machine can be done without complex machine modeling or experiments on ITSC prototype with multiple taps. The findings in this paper are essential for a comprehensive solution including fault mitigation algorithms and postfault operations. In order to verify the findings, a three-phase equivalent circuit model supported by finite element analysis results is used to take saturation and space harmonics into account. In addition, experimental results are presented to demonstrate the validity and practicability of the severity estimation.

DETECCIÓN MULTICRITERIO DE FALLOS DE UN MOTOR BLDC (BRUSHLESS DIRECT CURRENT)

This work presents a multiple criteria approach for stator fault detection (FD) in an in-wheel brushless DC (BLDC) motor for electric vehicles. The detection of inter-turn short circuits in the stator windings of the BLDC is studied. The fault detection scheme is performed based on the instantaneous space phasor (ISP). The fault signature (residual) is built using the fast Fourier transform (FFT) and discrete wavelet transform (DWT). An experimental setup comprising a modified in-wheel BLDC was used to validate this approach. The results of signal processing based FD are verified by an analysis made with thermographic techniques. Key Words: in-wheel BLDC motor, fault detection, FFT, DWT, entropy, infrared image, electric vehicle.

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

AbstractRecently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

Detection of Interturn Short-Circuits in the Rotor Winding of a Hydrogenerator

A method of detecting interturn short-circuits in the rotor winding by analyzing the magnetic field in the stator-to-rotor gap of a hydrogenerator is discussed. For the purpose of theoretical justification, the magnetic field is analyzed numerically using the finite-element method in the cases of presence and absence of short-circuited turns in the rotor winding. The numerical results are compared with experimental data for a 640 MW hydrogenerator.