Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Machine model based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: A Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Robust fault tolerant control of induction motor system

Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types.In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types.To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity.In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments

Development of Motion Control Systems for Hydraulically Actuated Cranes with Hanging Loads

Automation has been used in industrial processes for several decades to increase efficiency and safety. Tasks that are either dull, dangerous, or dirty can often be performed by machines in a reliable manner. This may provide a reduced risk to human life, and will typically give a lower economic cost. Industrial robots are a prime example of this, and have seen extensive use in the automotive industry and manufacturing plants. While these machines have been employed in a wide variety of industries, heavy duty lifting and handling equipment such as hydraulic cranes have typically been manually operated. This provides an opportunity to investigate and develop control systems to push lifting equipment towards the same level of automation found in the aforementioned industries. The use of winches and hanging loads on cranes give a set of challenges not typically found on robots, which requires careful consideration of both the safety aspect and precision of the pendulum-like motion. Another difference from industrial robots is the type of actuation systems used. While robots use electric motors, the cranes discussed in this thesis use hydraulic cylinders. As such, the dynamics of the machines and the control system designmay differ significantly. In addition, hydraulic cranes may experience significant deflection when lifting heavy loads, arising from both structural flexibility and the compressibility of the hydraulic fluid. The work presented in this thesis focuses on motion control of hydraulically actuated cranes. Motion control is an important topic when developing automation systems, as moving from one position to another is a common requirement for automated lifting operations. A novel path controller operating in actuator space is developed, which takes advantage of the load-independent flow control valves typically found on hydraulically actuated cranes. By operating in actuator space the motion of each cylinder is inherently minimized. To counteract the pendulum-like motion of the hanging payload, a novel anti-swing controller is developed and experimentally verified. The anti-swing controller is able to suppress the motion from the hanging load to increase safety and precision. To tackle the challenges associated with the flexibility of the crane, a deflection compensator is developed and experimentally verified. The deflection compensator is able to counteract both the static deflection due to gravity and dynamic de ection due to motion. Further, the topic of adaptive feedforward control of pressure compensated cylinders has been investigated. A novel adaptive differential controller has been developed and experimentally verified, which adapts to system uncertainties in both directions of motion. Finally, the use of electro-hydrostatic actuators for motion control of cranes has been investigated using numerical time domain simulations. A novel concept is proposed and investigated using simulations.publishedVersio

Speed Sensorless Control with ANN and Fuzzy PI Adaptation Mechanism for Induction Motor Drive

In the speed sensorless induction motor drives system, the Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most common strategy. It suffers from parameter sensitivity and flux pure integration problems. As a result, it leads to the deterioration of speed estimation. Simultaneously, the traditional PI parameters design may cause speed estimation instability or have gross errors in the regenerative mode. To overcome above-mentioned problems, a suitable Artificial Neural Networks (ANN) based on Ant Colony Optimization (ACO) is presented to replace the reference model of the RF-MRAS. Furthermore, the ANN learning by the modified ACO can enhance the ANN convergence speed and avoids the trap of local minimum value of algorithm. In the meantime, a fuzzy PI adaptation mechanism is also put forward, so the proportional coefficient kp and the integral coefficient ki can be adjusted dynamically to adapt the speed variations. Finally, the simulation results suggest that the speed estimation is more accurate in both the dynamic and static process, and the stability of speed estimation in regenerative mode was improved

Metoda za estimaciju magnetskog toka rotora kaveznih asinkronih generatora bez mjernog člana brzine vrtnje temeljena na fazno spregnutoj petlji

This paper presents a new rotor flux estimation method for sensorless vector controlled squirrel-cage inductiongenerators used in wind power applications. The proposed method is based on a phase-locked loop (PLL) and theorthogonality between the rotor flux space vector and its back electromotive force (EMF) space vector. Rotor flux isestimated using stator voltage equations without integrating the back EMF components in the stationary referenceframe and the well-known difficulties with the implementation of pure integrators are thus avoided. Moreover, theproposed method ensures successful magnetization of a speed-sensorless squirrel-cage induction generator at nonzerospeeds which makes it suitable for wind power applications. Experimental results on a 560 kW squirrel-cageinduction generator are presented to confirm the effectiveness and the feasibility of the proposed method.U radu je predstavljena nova metoda za estimaciju magnetskog toka rotora vektorski upravljanih kaveznih asinkronih vjetrogeneratora. Predložena metoda se temelji na fazno spregnutoj petlji i ortogonalnosti između prostornog vektora magnetskog toka rotora i njemu pripadajućeg prostornog vektora induciranog napona. Magnetski tok rotora se estimira korištenjem naponskih jednadžbi statora u koordinatnom sustavu statora bez integracije komponenti induciranog napona čime su izbjegnuti dobro poznati problemi implementacije integratora. Osim toga, predložena metoda omogućava uspješno magnetiziranje pri vrtnji kaveznog asinkronog generatora bez mjernog člana brzine vrtnje pa je pogodna za primjenu na vjetrogeneratorima. U radu su prikazani eksperimentalni rezultati za kavezni asinkroni generator snage 560~kW koji potvrđuju izvedivost i učinkovitost predložene metode

Fault Diagnostic System for Cascaded H-bridge Multilevel Inverter Drives Based on Artificial Intelligent Approaches Incorporating a Reconfiguration Technique

A fault diagnostic and reconfiguration system in a multilevel inverter drive (MLID) using artificial intelligent based techniques is developed in this dissertation. Output phase voltages of a MLID can be used as valuable information to diagnose faults and their locations. It is difficult to diagnose a MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train a NN, and the reduced noise may improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The comparison among MLP neural network (NN), principal component neural network (PC-NN), and genetic algorithm based selective principal component neural network (PC-GA-NN) are performed. Proposed neural networks are evaluated with simulation test set and experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points, whereas PC-GA-NN has better overall classification performance from NN by about 7.5% points. Therefore, the application of a genetic algorithm improves the classification from PC-NN by about 2.5% point. The overall classification performance of the proposed networks is more than 90%. A reconfiguration technique is also developed. The effects of using the developed reconfiguration technique at high modulation index are addressed. The developed fault diagnostic system is validated with experimental results. The developed fault diagnostic system requires about 6 cycles at 60 Hz to clear an open circuit and about 9 cycles at 60 Hz to clear a short circuit fault. The experimental results show that the developed system performs satisfactorily to detect the fault type, fault location, and reconfiguration

Artificial intelligence applied to speed sensorless induction motor drives

During the last two decades there has been considerable development of sensorless vector controlled induction motor drives for high performance industrial applications. Such control strategies reduce the drive's cost, size and maintenance requirements while increasing the system's reliability and robustness. Parameter sensitivity, high computational effort and instability at low and zero speed can be the main shortcomings of sensorless control. Sensorless drives have been successfully applied for medium and high speed operation, but low and zero speed operation is still a critical problem. Much recent research effort is focused on extending the operating region of sensorless drives near zero stator frequency. Several strategies have been proposed for rotor speed estimation in sensorless induction motor drives based on the machine fundamental excitation model. Among these techniques Model Reference Adaptive Systems (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. Rotor flux-MRAS is the most popular MRAS strategy and significant attempts have been made to improve the performance of this scheme at low speed. Artificial Intelligence (AI) techniques have attracted much attention in the past few years as powerful tools to solve many control problems. Common AI strategies include neural networks, fuzzy logic and genetic algorithms. The mam purpose of this work is to show that AI can be used to improve the sensorless performance of the well-established MRAS observers in the critical low and zero speed region of operation. This thesis proposes various novel methods based on AI combined with MRAS observers. These methods have been implemented via simulation but also on an experimental drive based around a commercial induction machine. Detailed simulations and experimental tests are carried out to investigate the performance of the proposed schemes when compared to the conventional rotor fluxMRAS. Various schemes are implemented and tested in real time using a 7.5 kW induction machine and a dSP ACE DS 1103 controller board. The results presented for these new schemes show the great improvement in the performance of the MRAS observer in both open loop and sensorless modes of operation at low and zero speed.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, Arab Republic of EgyptGBUnited Kingdo

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces