Harmonic current sideband indicators (HCSBIs) for broken bar detection and diagnostics in cage induction motors

Induction motor bar breakages have been increasingly studied in the last decades because of economic interests in developing techniques that permit on-line, non-invasive, early detection of motor faults in power plants. This work is specifically focused on broken bar detection and fault severity assessment in three phase power cage motors fed by non-sinusoidal voltage sources. In this work some new fault indicators for rotor bar breakages detection in squirrel cage induction motors are proposed, mathematically developed and experimentally proved. They are based on the sidebands of phase current upper harmonics, and they are well suited especially for converter-fed induction motors. The ratios I(7-2s)f/I5f and I(5+2s)f/I7f , I(13-2s)f/I11f and I(11+2s)f/I13f are examples of such new indicators, and they are not dependent on load torque and drive inertia, as classical indicators do. Their frequency-dependence has been also examined both theoretically and experimentally, and it was found less remarkable with respect to other indicators. Moreover, their values increase linearly with the quantity of consecutive broken bars, almost for not too much advanced faults; on 4-poles motors they were found quietly like the per-unit number of broken bars (ratio on total bar number). An original formulation is presented for motor mathematical modeling, based on the Generalized Symmetrical Components Theory, for sidebands amplitude computation. A complete motor model (involving all the elementary machine electrical circuits, as stator belts and rotor mesh loops) has been used for computer simulations; the same model was then transformed by using some complex Fortescue’s matrices to obtain a steady-state linear solution, solvable for stator and rotor currents, in healthy and faulty conditions. By exploiting the model, the formal definition of a set of new broken bar indicators was finally obtained. Machine simulations carried out by running the complete numerical model confirmed the accuracy of the model, and the theoretical previsions. Experimental work was performed by using a square-wave inverter-fed motor with an appositely prepared cage, for easy testing with increasing number of broken bars and without motor dismounting. Moreover, extensive experimentation was carried out on three industrial motors with different power and poles number, with increasing load, frequency and fault gravity for methodology validation. Finally, the ideas exposed in this work led to a patent application, owned by the University of Rome “Sapienza”

Extension of Finite-Control Set Model-Based Predictive Control Techniques to Fault-Tolerant Multiphase Drives: Analysis and Contributions

Las máquinas eléctricas son una de las principales tecnologías que hacen posible las energías renovables y los vehículos eléctricos. La necesidad constante de incrementar la capacidad de potencia para generar más energía o para impulsar vehículos cada vez más grandes, ha motivado la investigación y el desarrollo en el área de las máquinas multifásicas las cuales, gracias a su número de fases, permiten no sólo manejar más potencia con menos pulsaciones de par y contenido armónico en la corriente que las máquinas trifásicas convencionales, sino que también permiten obtener una mayor tolerancia a fallos, aumentando el interés de su implementación en aplicaciones donde la fiabilidad juega un papel importante por razones económicas y de seguridad. La investigación más reciente en el área de sistemas multifásicos se centra en el desarrollo de técnicas que permitan explotar las características específicas y especiales de las máquinas multifásicas, viendo el incremento en el número de fases no como un aumento en la complejidad de implementación, sino como un mayor número de grados de libertad tanto en el diseño como en el control, permitiendo mejorar sus prestaciones y fiabilidad, haciéndolas más atractivas para su uso en aplicaciones industriales. Es así como se han desarrollado técnicas de control que permitan operar a alta velocidad o alto par, tolerancia a diferentes tipos de fallos y máquinas con diferentes conexionados de devanados o con sistemas formados por múltiples variadores y máquinas. El objetivo de esta tesis doctoral es la extensión del control predictivo para máquinas multifásicas (específicamente el control predictivo de estados finitos basado en modelo o FCS-MPC por sus siglas en inglés) a la operación tolerante a fallos, aprovechando la capacidad de tolerancia a fallos que las máquinas multifásicas poseen, asegurando su funcionamiento de una manera eficiente y controlada. Con este fin se estudió el modelo matemático de la máquina en condiciones de pre- y post- falta considerando diferentes tipos de faltas, permitiendo establecer el efecto que las condiciones de fallo tienen en el comportamiento del sistema. Se desarrollaron modelos de simulación de una máquina de inducción de cinco fases, considerando faltas de fase abierta y en el disparo de los IGBT’s de una fase, permitiendo el diseño y validación del controlador FCS-MPC tolerante a fallos, cuyos resultados obtenidos fueron presentados en diversos congresos internacionales. La posterior implementación y validación experimental del control tolerante a fallos propuesto dio lugar a la publicación de dos de los artículos científicos presentados en esta tesis. Del mismo modo, se desarrolló un control tolerante a fallos basado en controladores lineales (de tipo resonante), teniendo en cuenta los esquemas propuestos en publicaciones científicas recientes y se realizó una comparativa entre el control tolerante a fallos basado en FCS-MPC y el controlador resonante ante un fallo de fase abierta, mediante resultados de simulación y experimentales, dando lugar a la publicación en un congreso internacional y en un artículo de revista científica. Las contribuciones de esta tesis doctoral se han publicado en la revista científica IEEE Transactions on Industrial Electronics entre los años 2013/2015

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

Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded

Sensorless Control of Switched-Flux Permanent Magnet Machines

This thesis investigates the sensorless control strategies of permanent magnet synchronous machines (PMSMs), with particular reference to switched-flux permanent magnet (SFPM) machines, based on high-frequency signal injection methods for low speed and standstill and the back-EMF based methods for medium and high speeds

Open-Phase Fault Operation on Multiphase Induction Motor Drives

Multiphase machines have been recognized in the last few years like an attractive alternative to conventional three-phase ones. This is due to their usefulness in a niche of applications where the reduction in the total power per phase and, mainly, the high overall system reliability and the ability of using the multiphase machine in faulty conditions are required. Electric vehicle and railway traction, all-electric ships, more-electric aircraft or wind power generation systems are examples of up-to-date real applications using multiphase machines, most of them taking advantage of the ability of continuing the operation in faulty conditions. Between the available multiphase machines, symmetrical five-phase induction machines are probably one of the most frequently considered multiphase machines in recent research. However, other multiphase machines have also been used in the last few years due to the development of more powerful microprocessors. This chapter analyzes the behavior of generic n-phase machines (being n any odd number higher than 3) in faulty operation (considering the most common faulty operation, i.e. the open-phase fault). The obtained results will be then particularized to the 5-phase case, where some simulation and experimental results will be presented to show the behavior of the entire system in healthy and faulty conditions. The chapter will be organized as follows: First, the different faults in a multiphase machine are analyzed. Fault conditions are detailed and explained, and the interest of a multiphase machine in the management of faults is stated. The effect of the open-phase fault operation in the machine model is then studied. A generic n-phase machine is considered, being n any odd number greater than three. The analysis is afterwards particularized to the 5-phase machine, where the open-phase fault condition is managed using different control methods and the obtained results are compared. Finally, the conclusions are presented in the last section of the chapter

Open-phase fault operation on multiphase induction 3 motor drives

Hugo Guzman, Ignacio Gonzalez, Federico Barrero and Mario Durán (2015). Open-Phase Fault Operation on Multiphase Induction Motor Drives, Induction Motors - Applications, Control and Fault Diagnostics, Dr. Raul Gregor (Ed.), ISBN: 978-953-51-2207-4, InTech, DOI: 10.5772/60810. Available from: http://www.intechopen.com/books/induction-motors-applications-control-and-fault-diagnostics/open-phase-fault-operation-on-multiphase-induction-motor-drivesMultiphase machines have been recognized in the last few years like an attractive alternative to conventional three-phase ones. This is due to their usefulness in a niche of applications where the reduction in the total power per phase and, mainly, the high overall system reliability and the ability of using the multiphase machine in faulty conditions are required. Electric vehicle and railway traction, all-electric ships, more-electric aircraft or wind power generation systems are examples of up-to-date real applications using multiphase machines, most of them taking advantage of the ability of continuing the operation in faulty conditions. Between the available multiphase machines, symmetrical five-phase induction machines are probably one of the most frequently considered multiphase machines in recent research. However, other multiphase machines have also been used in the last few years due to the development of more powerful microprocessors. This chapter analyzes the behavior of generic n-phase machines (beingn any odd number higher than 3) in faulty operation (considering the most common faulty operation, i.e. the open phase fault). The obtained results will be then particularized to the 5-phase case, where some simulation and experimental results will be presented to show the behavior of the entire system in healthy and faulty conditions. The chapter will be organized as follows: First, the different faults in a multiphase machine are analyzed. Fault conditions are de tailed and explained, and the interest of a multiphase machine in the management of faults is stated. The effect of the open-phase fault operation in the machine model is then studied. A generic n-phase machine is considered, being n any odd number greater than three. The analysis is afterwards particularized to the 5-phase machine, where the open phase fault condition is managed using different control methods and the obtained results are compared. Finally, the conclusions are presented in the last section of the chapter