Hybrid electric vehicles significantly reduce exhaust emissions and increase fuel economy. Power packs are the most fundamental components in a series powertrain configuration of a hybrid vehicle, which produce the necessary power to run the vehicle. The aim of this project is to design a compact power pack for a series hybrid vehicle, using virtual prototyping. The hybrid electric vehicle characteristics and configurations are analysed, followed by an explanation of the principles of induction machines. A new type of rotary induction machine with an outer rotor construction is designed to be coupled with the novel rotary internal combustion engine with rotating crankcase in order to form the compact power unit for the hybrid vehicle. The starting and generation performance of the designed machine is analysed by an electric machine simulator, called JMAG. ADVISOR software is studied and utilised to simulate the overall vehicle performance, employing different categories of power packs in the powertrain. Results show that the proposed compact power pack has the best performance in terms of fuel economy, emissions and battery charging compared to the existing power unit options. Over the city cycle, fuel economy is increased by up to 47 % with emission reduced by up to 36 % and over the highway cycle, fuel economy is increased by up to 69 % with emission reduced by up to 42 %.

Stability and sustainability of operation of interconnected power converter systems has been an important focus of study in the field of power electronics and power systems. With ever-increasing application of electrical machines by means of electrification of vehicles, airplanes and shipboards, detailed study of the relating dynamics is very important to ensure the proper implementation and stable behavior of the overall system. In this work, the application of the black box approach study of the power converters has been expanded to the electrical machines. Using this modeling method, it is possible of have accurate behavior of electrical and mechanical terminals of the machine without the detailed information about the internal structure of the machine, material characteristics or topology of the machine. Instead, accurate model of electrical and mechanical terminals of the machine are achieved by measuring specific frequency responses of the machine to distinguish dynamic relation of the various electrical and mechanical quantities of the machine. The directly measured frequency responses, are coupled with the dynamics of the source and load in the electrical and mechanical terminals of the machine thus in order to decoupled the described couplings a mathematical process is used that results in decoupling of the controller and drive on the electrical side and the dynamics of the mechanical load and mechanical shaft at the mechanical terminal of the machine. Resulting model is the linear time invariant representation of the electrical machine at a specific operating point. Additionally, this work represents the application of this modeling method for accurate measurement of internal parameters of the machine such as inductances and mechanical inertia and characterization of the mechanical shaft coupler. Resulting unterminated model of the machine is a very important matter of information for system integrators and electrical and mechanical designs related to the application of the machine, to ensure the stable and sustainable operation of the machine. This work for the first time, represents the experimental implementation of this terminal behavioral modeling method for studying electrical machines as well as describes some of the practical limitations of this methodology. By incorporating and integrating a combination of commercially available devices such as frequency response analyzer, Hardware-In-The-Loop (HIL), Power-Hardware-In-The-Loop (PHIL), a test setup has been developed that is capable of control, operate and study arbitrary frame small-signal related measurements required for terminal behavioral study of the electrical machines. Resulting model of the machine that has been extracted from this modeling method is then used to compare in time domain with the real machine in the case of transient change in the mechanical load on the shaft to discover the validity of this modeling procedure. / Master of Science / According to the data from the International Energy Agency, around half of the electricity used globally is consumed by electric motors. Moreover, the growth in the electrical vehicle industry will increase their application even further, hence the development of high-fidelity models of electric machines for real-time emulation, system-level analyses, and stability studies still stands out as an important and needed research focus. New modeling concepts that go beyond the standard industry practice can be used at the design and integration stage to ensure the stable behavior of the overall system. Furthermore, convenient testing and identification pressures can help ensure the long-term operation of the system. Aligned with this trend, this thesis is studying permanent magnet synchronous machines (PMSM) using small-signal terminal-behavioral three-port networks. Having such a behavioral model of the machine available provides many opportunities for system integrators, and even enables an in-situ system observation and stability assessment at both the machine's electrical and mechanical interfaces. This capability can undoubtedly be of high importance in practice, as it is offering new insights into dynamic interactions of the electro-mechanical systems, the governor or turbine control design in ships, aircrafts, electrical vehicles, and even large synchronous machines in power plants. A so-called characterization testbed has been built that combines Hardware-In-The-Loop (HIL) and Power-Hardware-In-The-Loop (PHIL) environments, with sensor-interface boards that are used to properly scale measured signals for machine control. The Frequency-Response-Analyzer is used to sweep the proper electrical or mechanical terminal of the machine by perturbing the proper control signal within the machine controller running in PHIL and reading d-q currents, voltages, torque, and speed variables whose dynamic ratios are then obtained without the need for interrupting the normal operation of the electrical machine. The capability of acquiring such a detailed model of the machine while the machine is in operation is an important benefit of this modeling method, in comparison to the conventional identification methods widely applied in the industry. The resulting model is a linearized time invariant representation of the electrical machine at a specific operating point of interest, and can be used by system integrators to ensure the stability of the system using well known stability assessment methodologies. Furthermore, this modeling strategy has been experimentally verified for the first time on electrical machines, and the resulting model has been compared with the transient behavior of the machine in the presence of a step change in the mechanical load of the machine.

The master’s thesis Improvement power parameter of small induction motors deals with issues of lowering the losses of small induction motors. The first part introduces with design and principles of operation of induction motors. Also introduces to theoretical problematic of losses, their lowering and measuring. In the practical part there are results of the measuring the losses in the induction motor ATAS Elektromotory Náchod a.s. T22VT512 (71-0512). There are proposed methods of increasing the efficiency of induction motor due to measuring and their verification in the Maxwell software. The last part is dedicated to measuring the losses of prototype motor from ATAS and comparison of results with previous motor.

O objetivo principal desta tese consiste no desenvolvimento de ferramentas matemáticas e computacionais dedicadas a um sistema de diagnóstico de barras quebradas no rotor de Motores de Indução Trifásicos. O sistema proposto é baseado em um método matemático de decomposição de sinais elétricos, denominado de Decomposição em Componentes Ortogonais, e ferramentas de aprendizagem de máquinas. Como uma das principais contribuições desta pesquisa, realizou-se um aprofundamento do entendimento da técnica de Decomposição em Componentes Ortogonais e de sua aplicabilidade como ferramenta de processamento de sinais para sistemas elétricos e eletromecânicos. Redes Neurais Artificiais e Support Vector Machines, tanto para classificação multi-classes quanto para detecção de novidades, foram configurados para receber índices advindos do processamento de sinais elétricos de motores, e a partir deles, identificar os padrões normais e os padrões com falhas. Além disso, a severidade da falha também é diagnosticada, a qual é representada pelo número de barras quebradas no rotor. Para a avaliação da metodologia, considerou-se o acionamento de motores de indução pela tensão de alimentação da rede e por inversores de frequência, operando sob diversas condições de torque de carga. Os resultados alcançados demonstram a eficácia das ferramentas matemáticas e computacionais desenvolvidas para o sistema de diagnóstico, sendo que os índices criados se mostraram altamente correlacionados com o fenômeno da falha. Mais especificamente, foi possível criar índices monotônicos com a severidade da falha e com baixa variabilidade, demonstrando-se que as ferramentas são eficientes extratores de características. / This doctoral thesis consists of the development of mathematical and computational tools dedicated to a diagnostic system for broken rotor bars in Three Phase Induction Motors. The proposed system is based on a mathematical method for decomposing electrical signals, named the Orthogonal Components Decomposition, and machine learning tools. As one of the main contributions of this research, an in-depth investigation of the decomposition technique and its applicability as a signal processing tool for electrical and electromechanical systems was carried-out. Artificial Neural Networks and Support Vector Machines for multi-class classification and novelty detection were configured to receive indices derived from the processing of electrical signals and then identify normal motors and faulty motors. In addition, the fault severity is also diagnosed, which is represented by the number of broken rotor bars. Experimental data was tested in order to evaluate the proposed method. Signals were obtained from induction motors operating with different torque levels and driven either directly by the grid or by frequency inverters. The results demonstrate the effectiveness of the mathematical and computational tools developed for the diagnostic system since the indices created are highly correlated with the fault phenomenon. More specifically, it was possible to create monotonic indices with the fault severity and with low variability, what supports that the solution is an efficient fault-specific feature extractor.

O presente trabalho desenvolve uma topologia de controle para o gerador de indução duplamente alimentado (GIDA) em condições normais e em condições de falta monofásica. O sistema de controle é dividido em três partes principais: sistema de identificação de faltas, controle em condições normais e controle em condições de falta monofásica. A primeira parte, o sistema de identificação (SI) de faltas, é responsável pela seleção da topologia de controle da máquina. O SI é composto por uma combinação entre redes neurais artificiais (RNA) e a Fast Fourier Transform (FFT). As RNA são responsáveis pela identificação do estado atual da rede, se possui falta ou não. Os dados de entrada das RNA são as correntes de linha do estator que passam por um pré-processamento por meio da FFT. Alguns conteúdos harmônicos de saída da FFT irrelevantes no processo de identificação são eliminados por um método similar ao Principal Components Analysis (PCA). A segunda parte do trabalho é o controle em condições normais, sendo ativado quando o SI aponta a ausência de faltas. A topologia de controle vetorial é utilizada nesta condição para manter a tensão e frequência constante com a velocidade mecânica do eixo variável. A última parte do trabalho é o controle em condições adversas, que é ativado quando o SI detecta uma falta monofásica. A topologia de controle nesta condição utiliza as transformações ortogonais para reduzir o fluxo concatenado no enrolamento do estator com falta. A utilização deste novo controle reduz a corrente do estator quando comparado com o controle vetorial em condições de falta, sendo que a tensão do estator nas fases sem falta é mantida dentro de uma faixa de operação. O trabalho possui resultados de simulação das três principais partes do sistema de controle. Primeiramente, resultados do controle vetorial de tensão e frequência do GIDA sob condições de velocidade do eixo variável e cortes de carga são apresentados. Logo após, apresenta-se os resultados do SI na identificação de faltas monofásicas na fase B e o seu comportamento sob condições adversas como desequilíbrio de carga e cortes de cargas. Finalmente, alguns resultados do controle em condições de falta sobre uma falta fase-neutro na fase B são apresentados. / This paper presents a control topology for doubly fed induction generator (DFIG) in normal and single fault conditions. The control system is divided into three main parts: fault identification system, control in normal condition and control in single fault conditions. In the first part, the system of identification (SI) is responsible for selecting the topology of the control. The SI is composed by a combination of artificial neural networks (ANN) and Fast Fourier Transform (FFT). The ANN is responsible for identifying the current state of the grid, if has fault or not. The inputs of the ANN are stator currents line through of a pre-processing by means of FFT. Some harmonic contents are irrelevant in the identification process and they are eliminated by a method similar to Principal Components Analysis (PCA). The second part of the paper is the control under normal conditions, activated when the SI indicates the absence of faults. The topology of vector control in this condition is used to maintain the voltage and frequency constant, where the speed of the mechanical axis variable. The last part of the work is the control in adverse conditions, which is activated when the SI detects a singlephase fault. The control topology in this condition uses the orthogonal transformations to reduce the mutual flux in the stator winding with fault. The use of this new control reduces the stator current as compared to vector control in fault conditions, and the stator voltage in the stages without fault is maintained within an operating range. The paper has simulation results of three main parts of the control system. First, the results of the vector control voltage and frequency of DFIG under conditions of variable shaft speed and load sections are provided. Soon after, the results of the SI in identifying faults in the phase B under conditions such as load imbalance and cutting loads are shown. Finally, some results of control in fault condition in the phase B are shown.

Les moteurs électriques sont responsables de 67% de la consommation d’électricité dans l’industrie. Remplacer les moteurs installés par des entrainements plus efficients requiert de statuer sur leur adéquation avec les charges qu’ils entrainent. Une contrainte forte est de les analyser « on-line » et sans mesures intrusives ni consignations des installations.Cette thèse répond à un triple objectif. Premièrement, un dispositif de diagnostic « non-invasif » facilement intégrable en milieu industriel a été développé avec quatre méthodes d’évaluation du niveau de charge des moteurs asynchrones directement connectés au réseau. Deux de ces méthodes, existantes et basées sur la mesure du courant et du flux magnétique de dispersion, font l’objet d’améliorations significatives qui les portent à un niveau TRL7. Les deux autres méthodes exploitent la mesure seule du flux de dispersion. Leur applicabilité est vérifiée pour une alimentation par un système de tensions, équilibré ou non, avec des variations permanentes ou aléatoires. Une étude plus exploratoire montre que l’estimation non-invasive du courant absorbé par les machines asynchrones alimentées par convertisseurs électroniques est possible par exploitation du flux rayonné. Deuxièmement, le dispositif de diagnostic énergétique et des algorithmes de recherche de motorisation adaptée à un cycle de fonctionnement défini ont été appliqués à des exemples concrets d’optimisation énergétique sur un site industriel très énergivore, une aluminerie. Troisièmement, cette étude propose une réflexion sur la gestion d’un parc moteurs et, notamment, sur l'analyse des performances des moteurs neufs comparés à ceux ayant subi un rebobinage. / In the industry, electrical motors are responsible for 67% of electricity consumption. Replacing installed motors by more efficient ones requires the knowledge of their suitability with the loads that they drive. Analyzing the load variations without intrusive measurements or installations consignments is a strong constraint.That is why this thesis has a threefold purpose. Firstly, a “noninvasive” diagnostic device has been developed with four methods for evaluating the load of grid-connected induction motors. Two of these methods, based on the measurement of the current and the magnetic stray flux, have been significantly improved up to TRL7. The two other methods exploit only the measurement of the stray flux. Their applicability is checked for balanced and unbalanced supply voltage systems with permanent or random variations. A more exploratory study shows that the noninvasive estimation of the current for inverter-fed induction machines is possible using the radiated external flux. Secondly, the energy diagnosis device and search algorithms adapted to an operating cycle motorization have been applied to practical examples of energy optimization in an electro-intensive industrial plant, an aluminum smelter. Thirdly, a reflection on the management of a motor fleet is proposed, in particular, on the performance analysis between new motors and rewounded ones.

O objetivo principal desta tese consiste no desenvolvimento de ferramentas matemáticas e computacionais dedicadas a um sistema de diagnóstico de barras quebradas no rotor de Motores de Indução Trifásicos. O sistema proposto é baseado em um método matemático de decomposição de sinais elétricos, denominado de Decomposição em Componentes Ortogonais, e ferramentas de aprendizagem de máquinas. Como uma das principais contribuições desta pesquisa, realizou-se um aprofundamento do entendimento da técnica de Decomposição em Componentes Ortogonais e de sua aplicabilidade como ferramenta de processamento de sinais para sistemas elétricos e eletromecânicos. Redes Neurais Artificiais e Support Vector Machines, tanto para classificação multi-classes quanto para detecção de novidades, foram configurados para receber índices advindos do processamento de sinais elétricos de motores, e a partir deles, identificar os padrões normais e os padrões com falhas. Além disso, a severidade da falha também é diagnosticada, a qual é representada pelo número de barras quebradas no rotor. Para a avaliação da metodologia, considerou-se o acionamento de motores de indução pela tensão de alimentação da rede e por inversores de frequência, operando sob diversas condições de torque de carga. Os resultados alcançados demonstram a eficácia das ferramentas matemáticas e computacionais desenvolvidas para o sistema de diagnóstico, sendo que os índices criados se mostraram altamente correlacionados com o fenômeno da falha. Mais especificamente, foi possível criar índices monotônicos com a severidade da falha e com baixa variabilidade, demonstrando-se que as ferramentas são eficientes extratores de características. / This doctoral thesis consists of the development of mathematical and computational tools dedicated to a diagnostic system for broken rotor bars in Three Phase Induction Motors. The proposed system is based on a mathematical method for decomposing electrical signals, named the Orthogonal Components Decomposition, and machine learning tools. As one of the main contributions of this research, an in-depth investigation of the decomposition technique and its applicability as a signal processing tool for electrical and electromechanical systems was carried-out. Artificial Neural Networks and Support Vector Machines for multi-class classification and novelty detection were configured to receive indices derived from the processing of electrical signals and then identify normal motors and faulty motors. In addition, the fault severity is also diagnosed, which is represented by the number of broken rotor bars. Experimental data was tested in order to evaluate the proposed method. Signals were obtained from induction motors operating with different torque levels and driven either directly by the grid or by frequency inverters. The results demonstrate the effectiveness of the mathematical and computational tools developed for the diagnostic system since the indices created are highly correlated with the fault phenomenon. More specifically, it was possible to create monotonic indices with the fault severity and with low variability, what supports that the solution is an efficient fault-specific feature extractor.

O presente trabalho desenvolve uma topologia de controle para o gerador de indução duplamente alimentado (GIDA) em condições normais e em condições de falta monofásica. O sistema de controle é dividido em três partes principais: sistema de identificação de faltas, controle em condições normais e controle em condições de falta monofásica. A primeira parte, o sistema de identificação (SI) de faltas, é responsável pela seleção da topologia de controle da máquina. O SI é composto por uma combinação entre redes neurais artificiais (RNA) e a Fast Fourier Transform (FFT). As RNA são responsáveis pela identificação do estado atual da rede, se possui falta ou não. Os dados de entrada das RNA são as correntes de linha do estator que passam por um pré-processamento por meio da FFT. Alguns conteúdos harmônicos de saída da FFT irrelevantes no processo de identificação são eliminados por um método similar ao Principal Components Analysis (PCA). A segunda parte do trabalho é o controle em condições normais, sendo ativado quando o SI aponta a ausência de faltas. A topologia de controle vetorial é utilizada nesta condição para manter a tensão e frequência constante com a velocidade mecânica do eixo variável. A última parte do trabalho é o controle em condições adversas, que é ativado quando o SI detecta uma falta monofásica. A topologia de controle nesta condição utiliza as transformações ortogonais para reduzir o fluxo concatenado no enrolamento do estator com falta. A utilização deste novo controle reduz a corrente do estator quando comparado com o controle vetorial em condições de falta, sendo que a tensão do estator nas fases sem falta é mantida dentro de uma faixa de operação. O trabalho possui resultados de simulação das três principais partes do sistema de controle. Primeiramente, resultados do controle vetorial de tensão e frequência do GIDA sob condições de velocidade do eixo variável e cortes de carga são apresentados. Logo após, apresenta-se os resultados do SI na identificação de faltas monofásicas na fase B e o seu comportamento sob condições adversas como desequilíbrio de carga e cortes de cargas. Finalmente, alguns resultados do controle em condições de falta sobre uma falta fase-neutro na fase B são apresentados. / This paper presents a control topology for doubly fed induction generator (DFIG) in normal and single fault conditions. The control system is divided into three main parts: fault identification system, control in normal condition and control in single fault conditions. In the first part, the system of identification (SI) is responsible for selecting the topology of the control. The SI is composed by a combination of artificial neural networks (ANN) and Fast Fourier Transform (FFT). The ANN is responsible for identifying the current state of the grid, if has fault or not. The inputs of the ANN are stator currents line through of a pre-processing by means of FFT. Some harmonic contents are irrelevant in the identification process and they are eliminated by a method similar to Principal Components Analysis (PCA). The second part of the paper is the control under normal conditions, activated when the SI indicates the absence of faults. The topology of vector control in this condition is used to maintain the voltage and frequency constant, where the speed of the mechanical axis variable. The last part of the work is the control in adverse conditions, which is activated when the SI detects a singlephase fault. The control topology in this condition uses the orthogonal transformations to reduce the mutual flux in the stator winding with fault. The use of this new control reduces the stator current as compared to vector control in fault conditions, and the stator voltage in the stages without fault is maintained within an operating range. The paper has simulation results of three main parts of the control system. First, the results of the vector control voltage and frequency of DFIG under conditions of variable shaft speed and load sections are provided. Soon after, the results of the SI in identifying faults in the phase B under conditions such as load imbalance and cutting loads are shown. Finally, some results of control in fault condition in the phase B are shown.

Les effets d’extrémité jouent un rôle important dans la modélisation et la commande de la Machine Linéaire à Induction (MLI). Ces phénomènes augmentent significativement la non-linéarité du modèle de la machine et génèrent plusieurs difficultés pour contrôler et observer ses états avec de bonnes performances. Cette thèse aborde trois problématiques distinctes : la commande robuste de la MLI, l’estimation de la vitesse et du flux de la MLI et le contrôle robuste à base d’observateur en utilisant la théorie du mode glissant d’ordre supérieur.Dans la première partie de la thèse, trois contrôleurs robustes assurant la poursuite de trajectoire de la vitesse et du flux pour la MIL ont été développés : le Super Twisting (ST), le Super Twisting Adaptatif (STA) et le Twisting Adaptatif (TA). Ces commandes ont été testées en simulations et leurs performances ont été démontrées. Ainsi, le ST assure un contrôle continu avec convergence à temps fini de l’erreur à zéro malgré les perturbations, sous l’hypothèse que les bornes des incertitudes sont connues. Cette hypothèse est relaxée dans le cas du TA et du STA grâce à leurs propriétés adaptatives.Dans la deuxième partie de la thèse, un nouveau modèle du MLI a été proposé et son observabilité a été démontrée. Ensuite un Observateur par Mode Glissant d’Ordre Deux (MGOD) et un Observateur par Mode Glissant d’Ordre Supérieur (MGOS) ont été synthétisé afin d’estimer la vitesse et le flux du MLI, uniquement en utilisant la mesure des tensions et des courants statorique. La stabilité des deux observateurs a été prouvée par une approche de Lyapunov et leurs performances ont été démontrées à travers des simulations.Dans la dernière partie de la thèse, deux commandes par rejet actif des perturbations sont synthétisées. Ainsi et dans un premier temps, le modèle de la MLI est décomposé en deux sous-systèmes du second ordre. Ensuite, deux contrôleurs (le twisting et le super-twsiting) ont été synthétisés afin d’assurer la poursuite du flux et de la vitesse. Le MGOS est utilisé pour estimer les dérivées du flux et de la vitesse, ainsi que pour l’estimation en temps réel de la perturbation. Les contrôleurs quant à eux assurent la compensation des perturbations et la poursuite des trajectoires du flux et de la vitesse. La stabilité et la convergence des deux commandes proposées ont été prouvées et leurs performances démontrées par simulation. / Dynamic end effects play an important role in the Linear Induction Machine (LIM) control. They increase significantly the nonlinearity of the machine model and generate several difficulties to control and observe states with good performances. This thesis addresses three distinctissues: LIM robust control, LIM speed and flux estimation and observer-based robust control using higher order sliding mode theory.In the first part, to achieve speed and flux tracking,Super Twisting Controller (STC), Adaptive Super Twisting Controller (ASTC), and Adaptive Twisting Controller (ATC) were proposed and implemented into LIM system with great performance, i.e. finite time convergence and robustness properties. Among them, STC ensures continuous control with finite time convergence of the error to zero despite disturbances, under the assumption that their bounds are known. ATC and ASTC can deal with unknown bounded disturbance thanks to their adaptive properties.In the second part, a novel simplified LIM model was proposed and its observability has been proved. Then, Second Order Sliding Mode Observer (SOSMO) and Adaptive High Order Sliding Mode Observer (HOSMO) were proposed to estimate LIM speed, only by using the measured stator voltages and stator currents. SOSMO observer is based on the super twisting algorithm and its stability has been proved with Lyapunov’s theory, which can guarantee finite time convergence with less chattering. Adaptive HOSMO strategy combines speed adaptive algorithm and HOSMO method together to estimate rotor fluxes and speed simultaneously.In the third part, the LIM is viewed as two second order subsystems. Moreover, only the speed and the flux are supposed to be measured. Based on that two differentcontrollers based on HOSMO were presented in order to achieve flux and speed tracking. In both controllers, the idea of active disturbance rejection control is applied. Hence, the HOSMO is used to estimate the derivatives of the flux and the speed, as well as the disturbance. Then, in order to deal with the uncertainty in the measured variables, two different SM controllers are proposed. Firstly, the TC is applied in the LIM. However, the control signal in this case is discontinuous. Then, in order to provide a continuous control signal, the TC is replaced with STC. The stability and convergence of proposed TC-HOSMO and STC-HOSMO approaches were given and simulation validated their performances.

This thesis describes new methods of evaluating the efficiency of three-phase induction machines using synthetic loading. Synthetic loading causes the induction machine to draw full-load current without the need to connect a mechanical load to the machine's drive shaft. The synthetic loading methods cause the machine to periodically accelerate and decelerate, producing an alternating motor-generator action. This action causes the machine, on average over each synthetic loading cycle, to operate at rated rms current, rated rms voltage and full-load speed, thereby producing rated copper losses, iron loss and friction and windage loss. The excitation voltages are supplied from a PWM inverter with a large capacity DC bus capable of supplying rated rms voltage. The synthetic loading methods of efficiency evaluation are verified in terms of the individual losses in the machine by using a new dynamic model that accounts for iron loss and all parameter variations. The losses are compared with the steady-state loss distribution determined using very accurate induction machine parameters. The parameters were identified using a run-up-to-speed test at rated voltage and the locked rotor and synchronous speed tests conducted with a variable voltage supply. The latter tests were used to synthesise the variations in stator leakage reactance, magnetising reactance and the equivalent iron loss resistance over the induction machine's speed range. The run-up-to-speed test was used to determine the rotor resistance and leakage reactance variations over the same speed range. The test method results showed for the first time that the rotor leakage reactance varied in the same manner as the stator leakage and magnetising reactances with respect to current. When all parameter variations are taken into account there is good agreement between theoretical and measured results for the synthetic loading methods. The synthetic loading methods are applied to three-phase induction machines with both single- and double-cage rotors to assess the effect of rotor parameter variations in the method. Various excitation waveforms for each method were used and the measured and modelled efficiencies compared to conventional efficiency test results. The results verify that it is possible to accurately evaluate the efficiency of three-phase induction machines using synthetic loading.