Multilevel inverters for renewable energy systems

Voltage source inverters have become widely used in the last decade primarily due to the fact that the dangers and limitations of relying on fossil fuel based power generation have been seen and the long term effects felt especially with regards to climate change. Policies and targets have been implemented such as from the United Nations climate change conference (COPxx) concerning human activities that contribute to global warming from individual countries. The most effective way of reducing these greenhouse gases is to turn to renewable energy sources such as the solar, wind etc instead of coal. Converters play the crucial role of converting the renewable source dc power to ac single phase or multiphase. The advancement in research in renewable energy sources and energy storage has made it possible to do things more efficiently than ever before. Regular or 2 level inverters are adequate for low power low voltage applications but have drawbacks when being used in high power high voltage applications as switching components have to be rated upwards and also switch between very high potential differences. To lessen the constraints on the switching components and to reduce the filtering requirements, multilevel inverters (MLI's) are preferred over two level voltage source inverters (VSI's). This thesis discusses the implementation of various types of MLI's and compares four different pulse width modulation (pwm) techniques that are often used in MLI under consideration: three, five, seven and nine level inverters. Harmonic content of the output voltage is recorded across a range of modulation indices for each of the three popular topologies in literature. Output from the inverter is filtered using an L only and an LC filter whose design techniques are presented. A generalized prediction algorithm using machine learning techniques to give the value of the expected THD as the modulation index is varied for a specific topology and PWM switching method is proposed in this study. Simulation and experimental results are produced in five level form to verify and validate the proposed algorithm

Aprendizaje por refuerzo en espacios continuos: algoritmos y aplicación al tratamiento de la anemia renal

El aprendizaje por refuerzo es un paradigma de aprendizaje automático orientado a la resolución de problemas de decisión secuenciales. Este tipo de problemas aparece en aplicaciones pertenecientes a campos tan diversos como control automático, medicina, investigación operativa o economía. Los algoritmos clásicos de aprendizaje por refuerzo están fundamentados en la teoría matemática de la programación dinámica, donde se asume que el espacio de estados es discreto y se compone de un número manejable de estados. Desafortunadamente, en la mayoría de aplicaciones de interés práctico el espacio de estados es continuo, por lo que los algoritmos clásicos dejan de ser útiles. Para poder aplicar el aprendizaje por refuerzo en espacios continuos se requiere, por una parte, generalizar el comportamiento aprendido a partir de un conjunto limitado de experiencias a casos que no se hayan experimentado previamente y, por otra parte, representar las políticas de forma compacta. Ambos requisitos han sido ampliamente estudiados en el campo del aprendizaje supervisado, donde a menudo se necesita aproximar una función continua a partir de un conjunto de puntos discretos. La combinación de algoritmos de aprendizaje por refuerzo con técnicas de aproximación de funciones es actualmente un área de investigación activa. A pesar de los avances logrados en los últimos años, todavía hay aspectos que limitan la capacidad del aprendizaje por refuerzo en problemas complejos. Entre ellos destacan la escasa capacidad de escalabilidad a espacios definidos por un número elevado de dimensiones y la elevada cantidad de datos necesarios para aprender políticas útiles. En esta tesis doctoral se proponen algoritmos de aprendizaje por refuerzo enfocados a mejorar estos dos aspectos. Los resultados obtenidos en diversos experimentos demuestran que los algoritmos propuestos suponen un avance hacia métodos de aprendizaje por refuerzo más prácticos y efectivos en problemas complejos. Además de las aportaciones teóricas se ha desarrollado un sistema basado en aprendizaje por refuerzo para la optimización del tratamiento de la anemia asociada a la enfermedad renal crónica.Reinforcement learning is a machine learning paradigm aimed at solving sequential decision making problems. This kind of problems is commonly encountered in areas such as automatic control, medicine, operative research or economy. Classical reinforcement learning algorithms rely on the mathematical theory of dynamic programming, where it is assumed that the state space is discrete and it is composed by a reduced number of states. Unfortunately, in most of the practical applications, the classical algorithms are not useful because the state space is continuous. In order to apply reinforcement learning in continuous spaces is necessary, on the one hand, to generalize the behaviour learned from a limited set of experiences to previously unseen cases and, on the other hand, to represent the policies in a compact way. Both requirements have been widely studied in the supervised learning field, where it is common to approximate a continuous function from a set of discrete points. The combination of reinforcement learning algorithms with function approximation is currently an active field of research. In spite of significant advances over the last years, there are still many issues that limit the ability of reinforcement learning methods in complex domains. Prominent among them are the poor scalability and the high amount of data required to learn useful policies. This thesis proposes several reinforcement learning algorithms intended for improving those two issues. The results obtained in the experiments show that the proposed algorithms represent an important step forward toward more practical and effective methods in complex domains. In addition to the theoretical contributions, this thesis also shows a system based on reinforcement learning aimed to optimize the treatment of patients with secondary anemia to chronic kidney disease

A new machine learning paradigm for terrain reconstruction

Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM.Published versio