Soluciones multivectoriales para controladores directos de máquinas nonafásicas simétricas
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La sociedad actual está cada vez más concienciada del impacto negativo que generan las emisiones de gases de efecto invernadero (GEI) en la atmósfera, derivadas principalmente de la industrialización y de la quema de combustibles fósiles para la producción de bienes y servicios. En respuesta a esta problemática, los gobiernos y las instituciones públicas están impulsando la transición hacia un modelo energético sostenible, en el que la energía eléctrica de origen renovable se convierta en el eje principal de funcionamiento de los sistemas industriales y de transporte.
En este contexto, las máquinas eléctricas adquieren un papel protagonista al sustituir a los motores de combustión interna alternativos (MCIA). Particularmente, las máquinas multifásicas (aquellas que cuentan con más de tres fases en el estátor) representan una opción prometedora gracias a ventajas como su inherente tolerancia a fallos (ante la pérdida de una o más fases) o su mejor distribución de corrientes de fase, entre otras. El campo de estudio relativo al diseño, modelado y control de las máquinas multifásicas continúa en expansión. Los sistemas pentafásicos y hexafásicos han sido los más investigados en los últimos años, mientras que los sistemas nonafásicos siguen siendo relativamente poco explorados, a pesar de su potencial para aplicaciones exigentes.
En este Trabajo de Fin de Máster se analiza el comportamiento dinámico de una máquina síncrona nonafásica de imanes permanentes mediante la implementación y comparación de dos estrategias de control directo: el Control Predictivo Basado en Modelo (Model Predictive Control, MPC) y el Control Directo de Par (Direct Torque Control, DTC). Se presentan y discuten los resultados obtenidos con ambos algoritmos, evaluando su desempeño en función de las principales variables eléctricas que condicionan su funcionamiento. Asimismo, se realiza un estudio complementario sobre la aplicación de soluciones univectoriales y multivectoriales —también conocidas como vectores virtuales o sintéticos— en los esquemas de control analizados. Este análisis permite valorar el impacto de dichas soluciones en la calidad del control, proporcionando una visión más detallada de las posibilidades que ofrecen estas técnicas en sistemas multifásicos.
Modern society is increasingly aware of the negative impact that greenhouse gas (GHG) emissions have on the atmosphere, mainly resulting from industrialization and the burning of fossil fuels to produce goods and services. In response to this issue, governments and public institutions are promoting the transition towards a sustainable energy model, in which electricity from renewable sources becomes the main pillar for the operation of industrial and transportation systems. In this context, electric machines play a key role by replacing internal combustion engines (ICE). In particular, multiphase machines (those which have more than three phases at the stator) represent a promising alternative due to advantages such as their inherent fault tolerance (in the event of the loss of one or more phases) or their improved phase current distribution, among others. The field of study related to the design, modelling and control of these multiphase machines continues to expand. Five-phase and six-phase systems have been the most thoroughly researched in recent years, while nine-phase systems remain relatively unexplored, despite their potential for demanding applications. This Master’s Thesis analyses the dynamic behaviour of a nine-phase permanent magnet synchronous machine through the implementation and comparison of two direct control strategies: Model Predictive Control (MPC) and Direct Torque Control (DTC). The results obtained with both algorithms are discussed and analysed, evaluating their performance based on the main electrical variables that determine their operation. A complementary study is also carried out on the application of single-vector and multivector solutions —also known as virtual or synthetic vectors— within the analysed control schemes. This analysis makes it possible to assess the impact of such solutions on control quality, providing a more detailed insight into the capabilities offered by these techniques in multiphase systems.
Modern society is increasingly aware of the negative impact that greenhouse gas (GHG) emissions have on the atmosphere, mainly resulting from industrialization and the burning of fossil fuels to produce goods and services. In response to this issue, governments and public institutions are promoting the transition towards a sustainable energy model, in which electricity from renewable sources becomes the main pillar for the operation of industrial and transportation systems. In this context, electric machines play a key role by replacing internal combustion engines (ICE). In particular, multiphase machines (those which have more than three phases at the stator) represent a promising alternative due to advantages such as their inherent fault tolerance (in the event of the loss of one or more phases) or their improved phase current distribution, among others. The field of study related to the design, modelling and control of these multiphase machines continues to expand. Five-phase and six-phase systems have been the most thoroughly researched in recent years, while nine-phase systems remain relatively unexplored, despite their potential for demanding applications. This Master’s Thesis analyses the dynamic behaviour of a nine-phase permanent magnet synchronous machine through the implementation and comparison of two direct control strategies: Model Predictive Control (MPC) and Direct Torque Control (DTC). The results obtained with both algorithms are discussed and analysed, evaluating their performance based on the main electrical variables that determine their operation. A complementary study is also carried out on the application of single-vector and multivector solutions —also known as virtual or synthetic vectors— within the analysed control schemes. This analysis makes it possible to assess the impact of such solutions on control quality, providing a more detailed insight into the capabilities offered by these techniques in multiphase systems.
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