Parameter Identification And Fault Detection For Reliable Control Of Permanent Magnet Motors

The objective of this dissertation is to develop new fault detection, identification, estimation and control algorithms that will be used to detect winding stator fault, identify the motor parameters and optimally control machine during faulty condition. Quality or proposed algorithms for Fault detection, parameter identification and control under faulty condition will validated through analytical study (Cramer-Rao bound) and simulation. Simulation will be performed for three most applied control schemes: Proportional-Integral-Derivative (PID), Direct Torque Control (DTC) and Field Oriented Control (FOC) for Permanent Magnet Machines. New detection schemes forfault detection, isolation and machine parameter identification are presented and analyzed. Different control schemes as PID, DTC, FOC for Control of PM machines have different control loops and therefore it is probable that fault detection and isolation will have different sensitivity. It is very probable that one of these control schemes (PID, DTC or FOC) are more convenient for fault detection and isolation which this dissertation will analyze through computer simulation and, if laboratory condition exist, also running a physical models

Modeling induction machine winding faults for diagnosis

International audienceMonitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives. This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit. Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations. Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance

Modeliranje i simulacija međuzavojskog kvara asinkronog motora upotrebom SSFR testa za dijagnostičke svrhe

This paper presents a new idea to establish a simplified model of the short-circuit turns (SCT), in the stator winding of the squirrel-cage induction motor (IM) using standstill frequency response test (SSFR). This method may offer more precision in parameters estimation independent of variations in motor or load operating conditions since it is at standstill test. However, high-performance field-oriented control, or diagnosis purpose of the IM requires accurate knowledge of the electrical parameters. Furthermore, we propose to model the IM by a multiple cage equivalent circuit (EC) that enables us to take into account the deep bar effect with accuracy. The specific advantage of the proposed method that we can create a true SCT at several levels using fault simulator in order to estimate the EC model parameters in each case of fault severity, with a low probability of risk to the machine being tested, and a relatively modest expense. At the first time the healthy machine is identified and experimentally validated, then the models have been successfully used to study the transient and steady-state behavior of the IM with SCT fault, which a practically oriented scientific value.U radu je predstavljena nova ideja za uspostavljanje pojednostavljenog modela kratkospojenih zavoja (SCT) u namotu statora kaveznog asinkronog motora (IM) upotrebom testa frekevencijskog odziva u mirovanju (SSFR). Ova metoda moguće pruža precizniju procjenu parametara neovisno o varijaciji motorskih ili teretnih radnih uvjeta jer se testiranje provodi u mirovanju. Ipak, vektorsko upravljanje visokih performansi ili dijagnostička IM-a zahtijevaju točno poznavanje električkih parametara. Nadalje, predlažemo model IM-a s višekaveznom nadomjesnom shemom (EC) koja nam omogućuje da u obzir uzmemo točan efekt duboko pozicioniranih kaveznih štapova. Posebna prednost predložene metode je što možemo načiniti vjerodostojni SCT model na nekoliko razina upotrebom simulatora kvara da bismo procjenili parametre EC modela u različitim stadijima kvara, s malom vjerojatnosti rizika za korišteni stroj te relativno umjerene troškove. Prvo se identificira i eksperimentalno provjeri neoštećeni stroj, a zatim se modeli uspješno koriste za proučavanje dinamičkog i stacionarnog ponašanja IM-a s SCT kvarom što posjeduje praktično-orijentiranu znanstvenu vrijednost

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

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

On the identifiability, parameter identification and fault diagnosis of induction machines

PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical solutions representing the machine parameters, depending on the employed machine model. It is therefore crucial to investigate such possibility of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being used for parameter identification and fault diagnosis purposes. This project focuses on the identifiability of induction machine models and their use in parameter identification and fault diagnosis. Two commonly used steady-states induction machine models namely T-model and inverse Γ- model have been considered in this thesis. The classical transfer function and bond graph identifiability analysis approaches, which have been previously employed for the T-model, are applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm, the Alternating Conditional Expectation, is employed here for the first time to study the identifiability of both the T- and inverse Γ-models of the induction machine. The results obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel are non-identifiable while those of the inverse Γ-model are uniquely identifiable when using external measurements. The identifiability analysis results are experimentally verified by the particle swarm optimization and Levenberg-Marquardt model-based parameter identification approaches developed in this thesis. To overcome the non-identifiability problem of the T-model, a new technique for induction machine parameter estimation from external measurements based on a combination of the induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and inverter-fed operations show the success of the technique in identifying the parameters of the machine using only readily available measurements of steady-state machine current, voltage and speed, without the need for extra hardware. ii A diagnosis scheme to detect stator winding faults in induction machines is also proposed in this thesis. The scheme uses time domain features derived from 3-phase stator currents in conjunction with particle swarm optimization algorithm to check characteristic parameters of the machine and detect the fault accordingly. The validity and effectiveness of the proposed technique has been evaluated for different common faults including interturn short-circuit, stator winding asymmetry (increased resistance in one or more stator phases) and combined faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the accuracy of the proposed technique and it is ability to detect the presence of the fault and provide information about its type and location. Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out to verify the identifiability analysis and show the effectiveness of the proposed parameter identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase test induction machine coupled to a dynamometer loading unit and driven by a variable frequency inverter that allows operation at different speeds. All the experiment analyses provided in the thesis are based on terminal voltages, stator currents and rotor speed that are usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata- Misurata Universit

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