Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the h-th harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5th harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5th harmonic. The results demonstrated that the positive-sequence magnitude for the 5th harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages

Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

This chapter provides a comprehensive analysis of noninvasive methods to diagnose stator winding insulation faults of an induction motor. Further, a novel noninvasive method is proposed to diagnose the root cause of winding failure due to unbalanced voltage to avoid catastrophic failure. Therefore, a winding function approach is utilized to derive an analytical expression for stator winding distribution and magnetomotive force (MMF). This tactic qualifies the conductor segment that generates MMF, and it also helps to analyze a healthy current spectrum. One can easily observe higher order harmonics in current spectrum; therefore, a new series of rotor harmonics is introduced to diagnose unbalanced supply. The locus of these harmonics is dependent on the poles, rotor bars, and slip. Due to the rapid complexity in industrial plants, it is inconceivable to continue human inspection to diagnose the faults. Thus, to avoid human inspection, in addition to new series of rotor harmonic, a fully automatic method based on neural network is proposed. This method not only diagnoses unbalanced voltage but it also recognize the percentage of unbalanced voltage by use of feed-forward multilayer perceptron (MLP) trained by back propagation. Finally, the experimental results shows the validation of this research work proposed method

On Innovative Methods of Induction Motor Interturn and Broken-bar Fault Diagnostics

A fault indicator, the so-called swing angle, for broken-bar and interturn faults is investigated in this paper. This fault indicator is based on the rotating magnetic-field pendulous-oscillation concept in faulty squirrel-cage induction motors. Using the swing-angle indicator, it will be demonstrated here that an interturn fault can be detected even in the presence of machine manufacturing imperfections. Meanwhile, a broken-bar fault can be detected under both direct-line and PWM excitations, even under the more difficult condition of partial-load levels. These two conditions of partial load and motor manufacturing imperfections, which are considered as difficult situations for fault detection, are investigated through experimentally obtained test results for a set of 2- and 5-hp induction motors

Wide area condition monitoring of power electric drives in wind power generation system using radiated electromagnetic fields

Electric components in numerous applications (particularly wind generation) are not straightforwardly accessible for monitoring. Therefore, the monitoring and protection through voltage/current measurement may not be dependable since the current value passes numerous segments to reach the observing element. Accordingly, finding an unusual phenomenon of a specific element is difficult. To resolve this issue, using transmitted electromagnetic field of an element for wide area condition monitoring is proposed. It is planned to diagnose and locate short-circuit in induction generator drive such as interturn, intercoil and terminal-to-turn failures. The frequency characteristics of the propagated field is then utilized for finding the short-circuit. The theoretical foundation that relate the behavior of each elements to their frequency response is analyzed and used. To utilize the derived technique for different practical circumstances, two distinctive methods are used for locating the short-circuit. As the experimental test of major fault cases could destruct the winding, the full three-dimensional finite element analysis is used in these cases and some are verified experimentally through the wide area communication. Identifying the areas of partial faults Prevents the whole winding failure prior to a massive destruction, which is costly especially for cases in inaccessible situations such as offshore wind towers

Sistema de diagnóstico distribuido de fallas basado en redes inalámbricas de sensores

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.En este artículo presenta el desarrollo de un sistema de monitoreo y diagnóstico distribuido cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El dispositivo desarrollado como nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON a la base de datos del sistema de monitoreo industrial PI System. Se implementó y validó el acondicionamiento de señal para la medición de corrientes de estator (MCSA) que permitió identificar fallas entre espiras de motores de inducción tipo jaula de ardilla. La plataforma presentada finalmente es una herramienta para realizar comparación y validación de técnicas de análisis, indicadores y de clasificación de fallas, puesto que existen diversas combinaciones que pueden ser aplicadas con el fin de mejorar la confiabilidad del diagnóstico, la observación de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros

Physics-Based Modeling of Power System Components for the Evaluation of Low-Frequency Radiated Electromagnetic Fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantle

Analysis for inter turn stator fault with load variation in Induction Motor

This paper investigates the impact of load variation on the diagnosis of inter-turn stator faults in induction machines. The proposed detection technique relies on the analysis of stator current using the discrete wavelet transform (DWT) in both normal and faulty states of the machine. The energy of the approximation and detail signals obtained from DWT provides valuable information about the machine's health and the severity of the inter-turn stator faults. Experimental tests were conducted using a dSpace 1104 signal card-based interface to study the load effects in detecting and diagnosing stator inter-turn short circuit faults in induction motor

Online monitoring of turn insulation deterioration in mains-fed induction machines using online surge testing

The development of an online method for the early detection of a stator turn insulation deterioration is the objective of the research at hand. A high percentage of motor breakdowns is related to the failure of the stator insulation system. Since most of the stator insulation failures originate in the breakdown of the turn-to-turn insulation, the research in this realm is of great significance. Despite the progress that has been made in the field of stator turn fault detection methods, the most popular and the best known ones are still limited to the detection of solid turn faults. The time span between a solid turn fault and the breakdown of the primary insulation system can be as short as a few seconds. Therefore, it is desirable to develop a method capable of detecting the deterioration of the turn insulation as early as possible and prior to the development of a solid turn fault. The different stresses that cause the aging of the insulation and eventually lead to failure are described as well as the various patterns of an insulation failure. A comprehensive literature survey shows the methods presently used for the monitoring of the turn insulation. Up to now no well-tested and reliable online method that can find the deterioration of the turn insulation is available. The most commonly used turn insulation test is the surge test, which, however, is performed only when the motor is out of service and disconnected from the supply. So far no research at all has been conducted on the application of an online surge test. The research at hand examines the applicability of the surge test to an operating machine. Various topologies of online surge testing are examined with regard to their practicability and their limitations. The most practical configuration is chosen for further analysis, implementation and development. Moreover, practical challenges are presented by the non-idealities of the induction machine like the eccentricity of the rotor and the rotor slotting, and have to be taken into account. Two solutions to eliminate the influence of the rotor position on the surge waveform are presented. Even though the basic concepts of online surge testing can be validated experimentally by a machine with a solid turn fault, it is preferable to use a machine with a deteriorated turn insulation. Therefore, a method, which does not require complex and expensive hardware, to experimentally emulate the turn insulation breakdown is implemented. The concepts at any stage of the work are supported by simulations and experimental results. In addition, the theory of surge testing is further developed by giving new definitions of the test's sensitivity, i.e., the frequency sensitivity and the error area ratio (EAR) sensitivity.Ph.D.Committee Chair: Thomas G. Habetler; Committee Member: Deepakraj M. Divan; Committee Member: J. Rhett Mayor; Committee Member: Linda S. Milor; Committee Member: Ronald G. Harle

Advanced Fault Detection Methods for Permanent Magnets Synchronous Machines

The trend in recent years of transport electrification has significantly increased the demand for reliability and availability of electric drives, particularly in those employing Permanent Magnet Synchronous Machines (PMSM), often selected due to their high efficiency and energy density. Fault detection has been identified as one of the key aspects to cover such demand. Stator winding faults are known to be the second most common type of fault, after bearing fault. An extensive literature review has shown that, although a number of methods has been proposed to address this type of fault, no tool of general application, capable of dealing effectively with fault detection under transient conditions unrelated to the fault, has been proposed up to date. This thesis has made contributions to modelling, real-time emulation and stator winding fault detection of PMSM. Fault detection has been carried out through model-based and signal-based methods with a specific aim at operation during transient conditions. Furthermore, fault classification methods already available have been implemented with features computed by proposed signal-based fault detection methods. The main conclusion drawn from this thesis is that model-based fault detection methods, particularly those based on residuals, appear to be better suited for transient conditions analysis, as opposed to signal-based fault detection methods. However, it is expected that a combination of the two (model/signal) would yield the best results