The diploma thesis deals with the simulation of induction motor control with respect to high efficiency. The theory of an induction motor is described here, with emphasis on its losses. Scalar and vector control are also described here. Vector control is optimized for higher efficiency. Subsequently, the creation of a model in the program MATLAB - Simulink is described here, for the comparison of vector control with and without optimization.

The conventional approach to vehicle design is restrictive, limited, andbiased. This often leads to sub-optimal utilisation of vehicle capabilities and allocated resources and ultimately entails the repercussions of designing andlater on an using an inefficient vehicle. To overcome these limitations, it is important to gain a deeper understanding of the interaction effects at component,subsystem, and system level. In this thesis, the research is focused on identifying appropriate methods and developing robust models to facilitate the interaction analysis. To scrutinise and identify appropriate methods, criteria were developed.Initially, the Design Structure Matrix (DSM) and its variations were examined.While DSM proved to be fundamental for capturing interaction effects,it lacked the ability to answer questions about the structure and behaviour ofinteractions and to predict unintended effects. Therefore, network theory wasexplored as a complementary method to DSM which was capable of providing insights into interaction structures and identifying influential variables. Subsequently, two criteria were established to identify subsystems significant to interaction analysis: high connectivity to other subsystems and multidisciplinary composition. The traction motor was observed to satisfyboth criteria as it had higher connectivity with other subsystems and was composed of multiple disciplines. Therefore, a detailed model of an induction motor was developed to enable the interaction analysis. The induction motor model was integrated into a cross-scalar design tool.The tool employed a two-step process: translating operational parametersto motor inputs using Newtonian equations and deriving physical attributes,performance characteristics, and performance attributes of the motor. Comparing the obtained performance characteristics curve against existing studiesvalidated the model’s reliability and capabilities. The design tool demonstrated adaptability to different drive cycles and the ability to modify motor performance without affecting operational parameters. Thus validating the capability of the design tool to capture cross-scalar and intra-subsystem interaction effects. To examine inter-subsystem interaction, a thermal model of an inverter was developed, capturing temperature variations in the power electronics based on motor inputs. The design tool successfully captured interaction effects between motor and inverter designs, highlighting the interplay with operational parameters. Thus, this thesis identifies methods for interaction analysis and develops robust subsystem models. The integrated design tool effectively captures intra-subsystem, inter-subsystem, and cross-scalar interaction effects. The research presented contributes to the overarching project goal of developing methods and models that capture interaction effects and in turn serve as a guiding tool for designers to understand the consequences of their design choices. / Det konventionella tillvägagångssättet för fordonsdesign är restriktiv, begränsat och partiskt. Detta leder ofta till en suboptimal användning av fordonets kapacitet och tilldelade resurser och innebär i slutändan att konsekvenserna blir att använda ett ineffektivt fordon. För att övervinna dessa begränsningar är det viktigt att få en djupare förståelse för interaktionseffekterna på komponent-, delsystem- och systemsnivå. I denna avhandling fokuserar forskningen på att identifiera lämpliga metoder och utveckla robusta modeller för att underlätta interaktionsanalysen. För att granska och identifiera lämpliga metoder utvecklades kriterier. Till att börja med undersöktes Design Structure Matrix (DSM) och dess variationer. Medan DSM visade sig vara grundläggande för att fånga interaktionseffekter, saknade den förmågan att besvara frågor om interaktionsstrukturer och beteende samt förutsäga oavsiktliga effekter. Därför utforskades nätverksteori som en kompletterande metod till DSM, vilket kunde ge insikter i interaktionsstrukturer och identifiera inflytelserika variabler. Därefter etablerades två kriterier för att identifiera delsystem som är betydelsefulla för interaktionsanalysen: hög anslutning till andra delsystem och mångdisciplinär sammansättning. Dragkraftmotorn observerades uppfylla båda kriterierna eftersom den hade högre anslutning till andra delsystem och var sammansatt av flera discipliner. Därför utvecklades en detaljerad modell av en induktionsmotor för att möjliggöra interaktionsanalysen. Induktionsmotormodellen integrerades i ett tvärskaligt designverktyg. Verktyget använde en tvåstegsprocess: att översätta operativa parametrar till motorinsatser med hjälp av Newtons ekvationer och härleda fysiska egenskaper, prestandakaraktäristik och prestandaattribut hos motorn. Jämförelse av den erhållna prestandakaraktäristikkurvan med befintliga studier validerade modellens tillförlitlighet och förmågor. Designverktyget visade anpassningsbarhet till olika körcykler och förmågan att modifiera motorprestanda utan att påverka operativa parametrar. Detta validerade designverktygets förmåga att fånga tvärskaliga och intra-subsystem interaktionseffekter. För att undersöka inter-subsysteminteraktion utvecklades en termisk modell av en inverter, som fångade temperaturvariationer i kraftelektroniken baserat på motorns styrning. Designverktyget fångade framgångsrikt interaktionseffekter mellan motor- och inverterdesign och belyste samspelet med operativa parametrar. Därmed identifierar denna avhandling metoder för interaktionsanalys och utvecklar robusta delsystemmodeller. Det integrerade designverktyget fångar effektivt intra-subsystem-, inter-subsystem- och tvärskaliga interaktionseffekter. Den presenterade forskningen bidrar till det övergripande projektets mål att utveckla metoder och modeller som fångar interaktionseffekter och i sin tur fungerar som ett vägledande verktyg för designers att förstå konsekvenserna av sina designval. / <p>QC 231003</p>

Yes / This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 sam-ples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window sam-ples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Infor-mation in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequen-cy WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed meth-od, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the pro-posed method is independent of the types of motors used and their ages.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity / Ministry of Higher Education, Libya; Switchgear & Instruments Ltd.

In the global, competitive market of energy transformation, increased operational expenses and depletion of raw materials have resulted in companies pursuing alternate materials to reduce consumer costs. In electrical applications, energy is transformed using materials with high electrical conductive properties. The conductive material used to transmit a signal is called conductor wire and is comprised of any material that has the ability to move charged particles from one point to another without propagation or delay. The conductor wire in many applications is encapsulated in epoxy resin called enamel. The enamel is the insulation system that provides necessary dielectric clearances to prevent voltage leakage. The most common form of energy transformation is the electric motor. Both copper and aluminum conductor wire are commonly used in electric motors, but copper is preferred due to thermal and electrical properties. However, there is a significant economic incentive to convert to aluminum conductor wire. Limited white papers are available comparing the performances of the two materials; the research is limited to physical and electrical performances of the raw material and does not take into considering the insulation. The conductor wire, which includes the insulation, is susceptible to damage during the manufacturing process and is an inherent risk if not fully understood. During the blocking process, the conductor wire is pushed and compressed into lamination slots. This process changes the conductor wire outer diameter to accommodate void spaces within the lamination slots. The percentage of slot area occupied by the conductor wire is known as slot fill. The higher the slot fill, the more wire occupying the available space. The higher the slot fill, the more force required to fill the slots. High slot fill motor designs provide a performance advantage with little associated cost. The more wire pressed into the slot, the higher the potential efficiency gains. However, high slot fill motors are more susceptible to damage. The study is designed to evaluate and measure the durability of aluminum and copper conductor wire under simulated compression stresses. Utilizing this information, electric motor manufacturers can push current design limits without affecting conductor wire quality and reliability.

This paper deals with the modeling of asynchronous motor. Ccomparing the simulation date to reality. And attempts to find an algorithm for calculating the mechanical torque assist state estimation engine.

Les diverses normes internationales obligent les constructeurs automobiles à optimiser les chaînes de propulsion conventionnelles mais surtout à développer d’autres alternatives de motorisation dont l’une des plus prometteuses est le véhicule électrique. Néanmoins ces nouvelles propulsions doivent garantir les mêmes performances et le même niveau de sureté de fonctionnement (fiabilité et sécurité en l’occurrence). La chaîne de propulsion électrique est conçue autour d’un nombre important de constituants (machine électrique, capteur(s), convertisseur(s) de l’électronique de puissance, etc.) qui peuvent être le siège de défauts. La détection et la localisation de ces défauts sont indispensables mais pas suffisantes pour assurer la sureté de fonctionnement du système. En effet pour assurer un fonctionnement en mode dégradé, il faut mettre en œuvre une architecture de commande tolérante aux fautes. L'objectif principal de cette thèse est de proposer des nouvelles architectures de commande tolérante aux défauts (Fault Tolerant Control en anglais) d’un véhicule électrique propulsé par une machine asynchrone, en présence de plusieurs types de défaut du capteur mécanique. Cette thèse est organisée en 4 chapitres.Le Chapitre 1 est un état de l’art exhaustif avec une analyse critique des architectures et des systèmes de contrôle commande tolérants aux fautes des chaînes de traction électrique ainsi qu'un état de l'art des différents défauts qui peuvent apparaitre dans la chaine de traction électrique. Le Chapitre 2 propose deux architectures de commande tolérante aux fautes : l’Hybride FTC et le GIMC (Generalised Internal Model Control) ; l’approche Hybride FTC est une combinaison de deux contrôleurs, le premier est un régulateur PI pour le mode sain et le second un correcteur robuste H infini pour le mode défaillant. L’architecture GIMC permet la restructuration de la loi de commande d'une manière adaptative. Elle est conçue afin d’assurer une bonne robustesse du système en présence de défaut grâce à une boucle interne faisant office de boucle de correction et de diagnostic.Le Chapitre 3 est consacré exclusivement à la commande tolérante aux fautes à base d'algorithme de vote, en faisant une étude comparative de 4 algorithmes avec trois topologies différentes : une première structure est proposée sur la sortie du système, une deuxième structure est appliquée sur la commande du système, et la troisième structure est une hybridation des deux précédentes.Le Chapitre 4 est dédié à la validation expérimentale des architectures décrites précédemment. Les résultats obtenus montrent l’efficacité des approches FTC proposées. / The various international standards require automakers to optimize conventional power train but mainly to develop other alternatives to drive, one of the most promising is the electric vehicle. However, these new drives should guarantee the same performance and the same level of dependability (reliability and security in this case).Electric power train is built around a large number of components (electrical machine, the sensor, the converter, power electronics, etc.) which may be affected by defects. The detection and localization of these defects are essential but not sufficient to ensure the dependability of the system. Indeed, to ensure operation in degraded mode, you must implement architecture of fault tolerant control (FTC). The main objective of this thesis is to propose new fault tolerant control architectures of an electric vehicle induction machine power train in the presence of several types of mechanical sensor failure. This thesis is organized into four chapters.Chapter 1 is a comprehensive state of the art with a critical analysis of architectures and control systems tolerant to faults of electric powertrains and a state of the art of the various defects that may occur in the chain of electric traction.Chapter 2 proposes two architectures of fault tolerant control: Hybrid FTC and GIMC (Generalised Internal Model Control); Hybrid FTC approach is a combination of two controllers, the first is a PI controller for the sound mode and the second a robust controller for the failed H infinity mode. The architecture allows GIMC restructuring the control law in an adaptive manner. It is designed to ensure robustness of the system in the presence of a fault with an inner loop acting loop correction and diagnosis.Chapter 3 is devoted exclusively to the algorithm voting fault tolerant control based on comparative study of four algorithms with three different topologies: a first structure is proposed on the system output, a second structure is applied system control, and the third structure is a two preceding hybridization.Chapter 4 is devoted to the experimental validation of the architecture described above. The results show the effectiveness of the approaches proposed FTC.

O objetivo desta tese consiste na implementação de sistemas inteligentes para monitoramento e diagnósticos de falhas ocorrentes em motores de indução trifásicos. Para tanto, desenvolveu-se uma bancada de experimentos que visa ensaios de falhas relacionados a curto-circuito entre as bobinas do enrolamento de estator, quebras nas barras da gaiola de esquilo do rotor e, finalmente, rolamentos defeituosos. Mais especificamente, o enfoque principal consiste na proposição de uma abordagem neural de detecção de quebras nas barras de rotores de motores de indução trifásicos mediante a análise do espectro de frequência e aplicação de técnicas de análise das componentes principais. Considerou-se o acionamento do motor de indução tanto pela tensão de alimentação da rede quanto por inversor trifásico em diferentes frequências, operando sob diversas condições de torque de carga para a avaliação da metodologia. / The objective of this thesis consists of the implementation of intelligent systems for three-phase induction motors fault diagnosis and condition monitoring. Therefore, an experimental test stand for stator winding inter-turn short circuit faults, broken rotor bar in squirrel cage and, finally, defective wheel bearing has been designed. The main focus is to propose a neural network approach, which uses spectral frequency analysis and principal component analysis techniques to detect broken rotor bar in squirrel cage induction motor. Induction motor operating at different load torque conditions and supplied with sinusoidal voltage supply and three-phase inverter at different frequency was considered in the experiment for methodology evaluation.

Neste trabalho é apresentado um estudo sobre o controle de velocidade sensorless aplicado ao Motor de Indução Trifásico (MIT) visando sobretudo, melhorar o desempenho da máquina de indução em aplicações na região de baixa frequência de operação. Para tanto, propõe-se uma abordagem utilizando a técnica de controle Direct Torque Control (DTC) com Space Vector Modulation (SVM) em conjunto com o Observador de Fluxo com Modelo de Tensão e Corrente (OFMTC), o que possibilita a estimação precisa da posição do vetor de fluxo do estator em baixa velocidade. Para a estimativa da velocidade do rotor é utilizado a estratégia Model Reference Adaptive System (MRAS) com mecanismo de adaptação fuzzy, a fim de se diminuir a sensibilidade aos parâmetros da máquina. O desempenho da estratégia de controle de velocidade sensorless proposta é testada em simulações com diferentes condições de operação buscando reproduzir alguns dos problemas encontrados em aplicações práticas, utilizando o software Matlab. Os resultados obtidos foram condizentes com resultados encontrados na literatura, comprovando o bom desempenho do sistema de controle de velocidade proposto. / This work deals with the sensorless speed control for Three-phase Induction Motor in order to improve its operation at low speed and low frequency. At first, a Direct Torque Control employing Space Vector Pulse with Modulation (SVPWM) based on Flux Observer (voltage and current model) is studied and evaluated due to its high accuracy of estimating the stator-flux at low speed. The Model Adaptive Reference (MRAS) with Fuzzy Controler is chosen in order to estimate the rotor speed. This choice is based on its capability of high performance during Three-phase Induction Motor operation even if the machines parameters and the load change during the operation. At last, some simulations with diferent operational conditions at low speed are carried out in order to test the sensorless control. The results show a good perfomance and agreed with the results shown in recent papers.

O estudo das condições de operação de um motor de indução em um ambiente industrial é indispensável, tendo em vista que eventuais problemas podem contribuir para um prejuízo na produção, ou ainda para custos adicionais relacionados à falta de manutenção dos equipamentos. Uma das principais falhas que podem ocorrer em um motor de indução do tipo gaiola de esquilo durante sua operação é o rompimento de uma ou mais barras que compõem o seu rotor. Apresenta-se neste trabalho um novo método para auxiliar na detecção de barras quebradas em um rotor tipo gaiola de esquilo, para um motor de grande porte, durante sua operação em regime permanente. A partir de um modelo matemático foi possível avaliar o rompimento de barras do rotor, detectando em uma posição específica, a variação da densidade de fluxo magnético resultante, produzida pela contribuição do fluxo de dispersão de cada barra do rotor, bem como pelo fluxo criado pelas correntes do estator. Um sensor de efeito Hall é instalado entre duas bobinas do estator, a fim de representar a posição onde é realizado o cálculo da densidade de fluxo magnético resultante pela modelagem matemática proposta. O sinal gerado pelo sensor a partir de uma falha é comparado com aquele obtido a partir do rotor saudável, para posterior análise. O trabalho sugere ainda a aplicação do método de detecção da falha em conjunto com uma técnica de inteligência artificial baseada nas redes neurais artificiais, a fim de contribuir para o diagnóstico da falha e estimativa do número de barras rompidas. Os resultados obtidos da simulação, bem como os dados obtidos durante o ensaio são apresentados e usados na validação do modelo matemático desenvolvido. / The study of operational conditions of an induction motor in an industrial environment is indispensable, once eventual problems can contribute for production losses, or still for additional costs related to the lack of equipments maintenance. Among the principal faults, in a squirrel cage induction motor can occur the breaking of one or more rotor bars. This work presents a new method in aid of detection of broken bars in a large squirrel cage induction motor during its operation in steady-state. A mathematical model is used to evaluate the broken rotor bars, detecting in a specific point, the resulting magnetic flux density produced by the leakage flux created by the rotor and stator currents. The Hall effect sensor is installed between two stator coils, in order to represent the position where the resulting magnetic flux density is calculated by the proposed mathematical model. The signal detected in the sensor during a fault, is compared to the obtained result of the magnetic flux density from a healthy rotor for analysis. The work still suggests the application of the artificial intelligence technique, based on artificial neural networks in the mathematical model, in order to aid on the fault detection and estimate of the number of broken bars. The simulation and experimental results are presented in order to validate the developed mathematical model.