Stator Inter-turn Fault Detection Research Articles

Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles

Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles

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.

Experimental validation of a novel stator interturn fault detection method in induction motor

Experimental validation of a novel stator interturn fault detection method in induction motor

Search-Coil Based Stator Interturn Fault Detection in Permanent Magnet Machines Running under Dynamic Condition

Interturn short circuit (ITSC) fault is a common fault in electric machines, which may severely damage the machines if no protective measure is taken in time. There are numerous fault diagnosis methods under a steady-state condition. However, there is relatively limited research on fault diagnosis under dynamic conditions. The dynamic operation of motors, such as in electric cars, is a very common scenario. Hence, this paper proposes a search-coil based online method for detecting ITSC fault in permanent magnet synchronous machine (PMSM) under a dynamic condition. The search coils are placed on the stator circumference at equal intervals. Each search coil reflects the information about the magnetic field in its vicinity and also contains the fault information. In this paper, the voltage induced by the odd sideband harmonics around the even carrier (2ωc±ω0) is selected as the fault characteristic to be used in effectively improving the detected signal-to-noise ratio by excluding the interference of the counter-potential of the permanent magnet. Since two adjacent search coils are placed one pole apart, a set of quadrature signals can be acquired. The Digital Lock-In Amplifier (DLIA) technology is applied to extract the amplitude of the characteristic voltage, which overcomes the shortcomings of the traditional spectrum analysis in applying to non-stationary conditions. The amplitudes of the voltage at different search coils can be compared to further determine the occurrence of a fault and also its rough location if occurred. Experiments were conducted with a six-phase PMSM for demonstrating the effectiveness of the proposed method. The obtained results show that the proposed method can accurately determine the occurrence of a fault.

A Temperature and Magnetic Field-Based Approach for Stator Inter-Turn Fault Detection

Effective fault detection techniques are needed to reduce the maintenance and downtime costs of induction motors. This article presents an approach for stator end-winding inter-turn short-circuit fault detection using a metal-coated fiber Bragg grating (FBG) sensor with 0.5-mm diameter and 10-mm length to monitor the temperature and magnetic field around the end winding. The sensing theory, design, manufacturing, and installation of the sensor are detailed in this article. The nickel coating on the surface of the sensor is formed by electroless plating and electroplating, which makes the sensor sensitive to both temperature and magnetic field. The feasibility of the presented approach has been supported by both simulation and experimental results. The temperature and magnetic field signals are decoupled by filtering, and the experimental data are analyzed in the time-frequency domain. The results show that the inter-turn short-circuit fault can be detected at an incipient stage with 5% shorted turns.

A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm

Today, due to the widespread utilization of Induction Motors (IMs) in different industries, their condition monitoring is of great importance. Concentrating on the failures of IMs, it has been acknowledged that the stator inter-turn faults (SITFs) are the most frequent electrical failures. This paper puts forward an algorithm for SITF detection founded on Kalman Filter (KF). More specifically, the proposed algorithm employs KF to extract motor current signatures (MCS) and motor voltage signatures (MVS). Afterward, a statistical SITF index is used, based on the standard deviation of the extracted signatures. The proposed SITF index is technically robust against non-fault conditions including voltage quality problems and heavy load changes as well as has a significant performance in the presence of high measured noise-impregnated signals due to utilization of KF algorithm-based. Moreover, during source harmonic pollutions, the proposed algorithm has a very robust performance. Also, due to straightforward and low-computational mathematical basis, the proposed method is computationally efficient. The performance of the proposed method is validated with numerous simulation and experimental scenarios. The results indicate the proposed SITF index has robust performance, promising accuracy and good speed of convergence.

Stator End-Winding Thermal and Magnetic Sensor Arrays for Online Stator Inter-Turn Fault Detection

In this paper, two novel stator inter-turn fault sensing schemes, based on infrared (IR) thermopile sensor array (IRSA) and Hall-effect sensor array (HESA) are proposed. These circular sensing arrays are mounted along the inner wall of the stator's casing facing the end-winding region. This enables a direct measurement of temperature and magnetic flux distribution along the end-winding region in a non-contact way. Thus, the deviation in thermal and magnetic symmetry introduced by an inter-turn short circuit fault can be readily assessed. The proposed sensing schemes provides an intuitive and straight forward approach to inter-turn fault monitoring compared to the conventional methods, which depend on detecting fault induced secondary effects on external signals (motor currents, casing vibration, and temperature). The practicality and the diagnostic ability of the end-winding sensor array approaches in detecting the stator inter-turn faults is demonstrated using a 1.5 kW induction motor test rig. Compared to IRSA, HESA is found to be more versatile and with better early fault detection capability. Hence, an embedded online monitoring algorithm based on HESA is developed and demonstrated using the test rig.

Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

This paper proposes an improved diagnosis method for early detection and localization of Inter-Turn Short Circuit (ITSC) faults in the stator winding of the induction motor (IM). The main advantages of the method are the simplicity, low cost, and accurate diagnosis of these types of faults such that it can detect and localize even a low number of shorted turns faults in the stator winding of the motor. This is achieved by using a novel indicator that is based on the Discrete Wavelet Energy Ratio (DWER) of three stator currents, with Artificial Neural Network (ANN). Three different models of typical neural networks, namely, Multi-Layer perceptron (MLP), radial basis function (RBF), and Elman Neural Network (ENN) based on Bayesian Regularized (BR) training algorithm are proposed for ITSC classification based on fault feature extraction using discrete wavelet transform. To test the effectiveness of the proposed method, several experimental tests were carried out under different operating conditions of the IM, which contains the healthy and the ITSC faults cases that have experimented under various loads and different numbers of shorted turns in the three phases of the motor. The obtained results proved that the DWER is an accurate and robust indicator to diagnose the ITSC fault, this is confirmed by ANN results which gave the best results with the Bayesian regularized Elman network model that has the highest performance with minimal error rate is 10−9.Consequently, the combination DWER-ENN has assured its ability to accurately detect high and even low numbers of the shorted turns and localize the defective phase even within various loads in the IM.

High-Frequency Voltage Injection Based Stator Interturn Fault Detection in Permanent Magnet Machines

An interturn short-circuit fault in the stator winding of an electric machine, denoted as turn fault, has been recognized as one of the most severe faults in permanent magnet machines, which require a swift and reliable detection, in order to implement appropriate mitigations. The asymmetry brought by a turn fault is widely used for fault detection. However, similar features also emerge in a less severe high-resistance connection (HRC) fault, which may lead to an incorrect fault identification. In this article, a more exclusive turn fault detection method with the ability to differentiate from the HRC fault is proposed. It injects high-frequency square-wave voltage signals and makes use of the difference in the high-frequency impedance under the two fault conditions. The sensitivity to the HRC fault is largely reduced. The proposed turn fault indicator is independent of the operating conditions and robust with respect to state transients. This method is validated in a fault-tolerant permanent magnet assisted synchronous reluctance machine drive.

Reliable Detection of Stator Interturn Faults of Very Low Severity Level in Induction Motors

The interturn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature in most past efforts is the high level of severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault severity levels that are noncatastrophic and that are by their nature very low. This is the motivation behind this article, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The article studies induction motors with incipient interturn fault severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low level interturn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.

Designand application of data aquistion interface circuit

A commitment to condition monitoring involves the operators of plant in the conduct of a range of activities. These activities may be complicated in nature and indeed may often be performed automatically under computer control. They can, however, always be down into a relatively small number of easily identifiable functional tasks. This makes it much easier to identify the common elements of machine condition monitoring schemes. A proposed interface circuit design and application will be further explain in this paper, the implemented monitoring unit circuit also illustrated, see appendix A. Two scenarios presented in this paper, first ten turns assume to be shorted, and in the second thirty turns shorted to show the difference in the amplitude of frequencies at each case. This paper present. An improvement in three-phase squirrel-cage induction motor stator inter-turn fault detection and diagnosis based on a neural network approach is presented.

Stator Inter-turn Fault Detection in Inverter Fed Induction Motor Drives

The Squirrel Cage Induction motor(SCIM) with advanced power electronic inverters presents the greater advantages on cost and energy efficiency as compared with other industrial solutions for varying speed applications. In recent, the inverter fed induction motors are being popular in the industries. These inverter fed-motors are recently gathering great recognition for multi-megawatt industrial drive applications. In this present paper, a dynamic simulation model of PWM inverter fed SCIM with direct torque control jointly has been presented and analyzed in the recent MATLAB/Simulink environment. From the proposed simulation model, the transient behavior of SCIM has been analysed for healthy as well as for stator inter-turn fault condition. The dynamic simulation of induction motor is one of the key steps in the validation of design process of the electric motor and drive system. It is extremely needed for eliminating probable faults beforehand due to inadvertent design mistakes and changes during operation. The simulated model gives encouraging results with reduced harmonics [1]. By using the model, the successful detection of stator inter-turn fault of the SCIM is carried out in the transient condition. Therefore, early stator fault detection is possible and may avoid the motor to reach in the catastrophic conditions. Therefore, may save millions of dollars for industries.

Stator Inter-turn Fault Detection in Inverter Fed Induction Motor Drives

The Squirrel Cage Induction Motor (SCIM) with advanced power electronic inverters presents the greater advantages on cost and energy efficiency as compared with other industrial solutions for varying speed applications. In recent, the inverter fed induction motors are being popular in the industries. These inverter fed-motors are recently gathering great recognition for multimegawatt industrial drive applications. In this present paper, a dynamic simulation model of PWM inverter fed SCIM with direct torque control jointly has been presented and analyzed in the recent MATLAB/Simulink environment. From the proposed simulation model, the transient behavior of SCIM has been analysed for healthy as well as for stator inter-turn fault condition. The dynamic simulation of induction motor is one of the key steps in the validation of design process of the electric motor and drive system. It is extremely needed for eliminating probable faults beforehand due to inadvertent design mistakes and changes during operation. The simulated model gives encouraging results with reduced harmonics [1]. By using the model, the successful detection of stator inter-turn fault of the SCIM is carried out in the transient condition. Therefore, early stator fault detection is possible and may avoid the motor to reach in the catastrophic conditions. Therefore, may save millions of dollars for industries.

Rotor/Stator Inter-Turn Short Circuit Fault Detection for Saturable Wound-Rotor Induction Machine by Modified Magnetic Equivalent Circuit Approach

This paper presents a new method to model healthy and faulty wound-rotor induction machines (WRIMs) with and without inter-turn short circuit fault by a single set of equations. The model is presented for a machine with arbitrary pole, and rotor and stator slot numbers. A single set of equations, including both electric and magnetic parts, is solved to obtain full dynamic behavior of the machine. Therefore, the modified magnetic equivalent circuit with non-linear elements is used for modeling and a numerical technique is employed to solve the non-linear equations. The proposed modified method has a simple calculation procedure in comparison with the conventional magnetic equivalent circuit method. Solving the electric and magnetic equations by a single set of equations is the main advantage of the presented method, which is proposed for a WRIM machine, for the first time. In addition, to increase the accuracy, the saturation phenomenon is also considered in the model. Due to the saturation effect, there are some non-linear algebraic magnetic equations, which should be simultaneously solved with differential equations. A trapezoidal technique is used to convert differential equations to algebraic ones. To solve the non-linear equations, non-linear equations, Newton Newton–Raphson method is employed. Current signature analysis is used for rotor and stator inter-turn short circuit fault detection. Finally, the finite-element method is used to verify the effectiveness of the proposed modeling method. It is worth noting that the proposed method can model a healthy and faulty machine with different faults by a single model.

Transient Model and Detection of Stator Inter-Turn Fault In Inverter Driven Induction Motor Drives by Time Domain Technique

Transient Model and Detection of Stator Inter-Turn Fault In Inverter Driven Induction Motor Drives by Time Domain Technique

Synchronous Demodulation of Control Voltages for Stator Interturn Fault Detection in PMSM

In this paper, a new technique for stator winding interturn fault detection in current-controlled permanent magnet synchronous machines is proposed. The method is based on online extraction of the second harmonic component of the voltage reference vector in the dq frame. It will be shown that the amplitude of this harmonic depends on the interturn fault severity and the fault detection is realized by comparing the magnitude of the control voltage second harmonic, derived using a proper time-domain transformation, to a predefined threshold. This is a low-cost method in CPU burden time. The experimental results show the validity of the proposed method.

Winding Turn-to-Turn Faults Detection of Fault-Tolerant Permanent-Magnet Machines Based on a New Parametric Model

This paper proposes a parametric model for inter-turn fault detection in a fault- tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short- circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.

Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniquesMotivated by the superior performances of neural networks and neuro-fuzzy approaches to fault detection of a single phase induction motor, this paper studies the applicability these two approaches for detection of stator inter-turn faults in a three phase induction motor. Firstly, the paper develops an adaptive neural fuzzy inference system (ANFIS) detection strategy and then compares its performance with that of using a multi layer perceptron neural network (MLP NN) applied to stator inter-turn fault detection of a three phase induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.

Fast Single-Turn Sensitive Stator Interturn Fault Detection of Induction Machines Based on Positive- and Negative-Sequence Third Harmonic Components of Line Currents

Unambiguous detection of stator interturn faults for induction machines at their incipient stage, i.e., few turns' fault, has recently received great attention. Traditionally, interturn faults are detected using negative-sequence current and impedance. However, their effectiveness under supply imbalance conditions is questionable. Recently, line-current third harmonic (± 3f) has also been used in an attempt to achieve this goal. Nevertheless, issues such as inherent structural asymmetry and voltage imbalance also influence the + 3f. In this paper, the positiveand negative-sequence third harmonics (± 3f) of line current under different operating conditions have been explored by combining space and time harmonics. The suggested fault signature was obtained by removing residual components from tested quantities. Simulation and experimental results using 1 s of data indicate that the proposed ± 3f signatures are capable of very effectively detecting even a single-turn fault, and distinguish it from voltage imbalance and structural asymmetry.