Control of power electronic interfaces in distributed generation.

Renewable energy has gained popularity as an alternative resource for electric power generation. As such, Distributed Generation (DG) is expected to open new horizons to electric power generation. Most renewable energy sources cannot be connected to the load directly. Integration of the renewable energy sources with the load has brought new challenges in terms of the system’s stability, voltage regulation and power quality issues. For example, the output power, voltage and frequency of an example wind turbine depend on the wind speed, which fluctuate over time and cannot be forecasted accurately. At the same time, the nonlinearity of residential electrical load is steadily increasing with the growing use of devices with rectifiers at their front end. This nonlinearity of the load deviates both current and voltage waveforms in the distribution feeder from their sinusoidal shape, hence increasing the Total Harmonics Distortions (THD) and polluting the grid. Advances in Power Electronic Interfaces (PEI) have increased the viability of DG systems and enhanced controllability and power transfer capability. Power electronic converter as an interface between energy sources and the grid/load has a higher degree of controllability compared to electrical machine used as the generator. This controllability can be used to not only overcome the aforementioned shortfalls of integration of renewable energy with the grid/load but also to reduce THD and improve the power quality. As a consequence, design of a sophisticated controller that can take advantage of this controllability provided by PEIs to facilitate the integration of DG with the load and generate high quality power has become of great interest. In this study a set of nonlinear controllers and observers are proposed for the control of PEIs with different DG technologies. Lyapunov stability analysis, simulation and experimental results are used to validate the effectiveness of the proposed control solution in terms of tracking objective and meeting the THD requirements of IEEE 519 and EN 50160 standards for US and European power systems, respectively

Advanced Discrete-Time Control Methods for Industrial Applications

This thesis focuses on developing advanced control methods for two industrial systems in discrete-time aiming to enhance their performance in delivering the control objectives as well as considering the practical aspects. The first part addresses wind power dispatch into the electricity network using a battery energy storage system (BESS). To manage the amount of energy sold to the electricity market, a novel control scheme is developed based on discrete-time model predictive control (MPC) to ensure the optimal operation of the BESS in the presence of practical constraints. The control scheme follows a decision policy to sell more energy at peak demand times and store it at off-peaks in compliance with the Australian National Electricity Market rules. The performance of the control system is assessed under different scenarios using actual wind farm and electricity price data in simulation environment. The second part considers the control of overhead crane systems for automatic operation. To achieve high-speed load transportation with high-precision and minimum load swings, a new modeling approach is developed based on independent joint control strategy which considers actuators as the main plant. The nonlinearities of overhead crane dynamics are treated as disturbances acting on each actuator. The resulting model enables us to estimate the unknown parameters of the system including coulomb friction constants. A novel load swing control is also designed based on passivity-based control to suppress load swings. Two discrete-time controllers are then developed based on MPC and state feedback control to track reference trajectories along with a feedforward control to compensate for disturbances using computed torque control and a novel disturbance observer. The practical results on an experimental overhead crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page

Normas e estabilidade para modelos estocásticos cuja variação do controle e do estado aumentam a incerteza

Orientador: João Bosco Ribeiro do ValDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Essa dissertação de mestrado gira em torno da discussão sobre controle de sistemas incertos. Modelos matemáticos utilizados como base para o design de controladores automáticos são naturalmente uma representação aproximada do sistema real, o que, em conjunto com perturbações externas e dinâmica não modelada, gera incertezas a respeito dos sistemas estudados. Na literatura de controle, este tema vêm sendo discutido frequentemente, em particular nas sub-áreas de controle estocástico e controle robusto. Dentre as técnicas desenvolvidas dentro da teoria de controle estocástico, uma proposta recente se diferencia das demais por basear-se na idéia de que variações abruptas na política de controle possam acarretar em maiores incertezas a respeito do sistema. Matematicamente, essa noção é representada pelo uso de um ruído estocástico dependente do módulo da ação de controle, e a técnica foi apelidada de VCAI - acrônimo para variação do controle aumenta a incerteza. A definição da política de controle ótima correspondente, obtida por meio do método de programação dinâmica, mostra a existência de uma região ao redor do ponto de equilíbrio para a qual a política ótima é manter a ação de controle do equilíbrio inalterada, um resultado que parece particular à abordagem VCAI, mas que pode ser relacionado a políticas de gerenciamento cautelosas em áreas como economia e biologia. O problema de controle ótimo VCAI foi anteriormente resolvido ao adotar-se um critério de custo quadrático descontado e um horizonte de otimização infinito, e nessa dissertação nós utilizamos essa solução para atacar o problema de custo médio a longo prazo. Dada certa semelhança entre a estrutura do ruído estocástico na abordavem VCAI e modelos utilizados na teoria de controle robusto, discutimos ainda possíveis relações entre a abordagem proposta e controladores robustos. Discutimos ainda algumas possíveis aplicações do modelo propostoAbstract: This work discusses a new approach to the control of uncertain systems. Uncertain systems and their representation is a recurrent theme in control theory: approximate mathematical models, unmodeled dynamics and external disturbances are all sources of uncertainties in automated systems, and the topic has been extensively studied in the control literature, particularly within the stochastic and robust control research areas. Within the stochastic framework, a recent approach, named CVIU - control variation increases uncertainty, for short -, was recently proposed. The approach differs from previous models for assuming that a control action might actually increase the uncertainty about an unknown system, a notion represented by the use of stochastic noise depending on the absolute value of the control input. Moreover, the solution of the corresponding stochastic optimal control problem shows the existence of a region around the equilibrium point in which the optimal action is to keep the equilibrium control action unchanged. The CVIU control problem was previously solved by adopting a discounted quadratic cost formulation, and in this work we extend this previous result and study the corresponding long run average control problem. We also discuss possible relations between the CVIU approach and models from robust control theory, and present some potential applications of the theory presented hereMestradoAutomaçãoMestre em Engenharia Elétrica2016/02208-6, 2017/10340-4FAPES

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Algorithmes quantiques pour la cryptanalyse et cryptographie symétrique post-quantique

Modern cryptography relies on the notion of computational security. The level of security given by a cryptosystem is expressed as an amount of computational resources required to break it. The goal of cryptanalysis is to find attacks, that is, algorithms with lower complexities than the conjectural bounds.With the advent of quantum computing devices, these levels of security have to be updated to take a whole new notion of algorithms into account. At the same time, cryptography is becoming widely used in small devices (smart cards, sensors), with new cost constraints.In this thesis, we study the security of secret-key cryptosystems against quantum adversaries.We first build new quantum algorithms for k-list (k-XOR or k-SUM) problems, by composing exhaustive search procedures. Next, we present dedicated cryptanalysis results, starting with a new quantum cryptanalysis tool, the offline Simon's algorithm. We describe new attacks against the lightweight algorithms Spook and Gimli and we perform the first quantum security analysis of the standard cipher AES.Finally, we specify Saturnin, a family of lightweight cryptosystems oriented towards post-quantum security. Thanks to a very similar structure, its security relies largely on the analysis of AES.La cryptographie moderne est fondée sur la notion de sécurité computationnelle. Les niveaux de sécurité attendus des cryptosystèmes sont exprimés en nombre d'opérations ; une attaque est un algorithme d'une complexité inférieure à la borne attendue. Mais ces niveaux de sécurité doivent aujourd'hui prendre en compte une nouvelle notion d'algorithme : le paradigme du calcul quantique. Dans le même temps,la délégation grandissante du chiffrement à des puces RFID, objets connectés ou matériels embarqués pose de nouvelles contraintes de coût.Dans cette thèse, nous étudions la sécurité des cryptosystèmes à clé secrète face à un adversaire quantique.Nous introduisons tout d'abord de nouveaux algorithmes quantiques pour les problèmes génériques de k-listes (k-XOR ou k-SUM), construits en composant des procédures de recherche exhaustive.Nous présentons ensuite des résultats de cryptanalyse dédiée, en commençant par un nouvel outil de cryptanalyse quantique, l'algorithme de Simon hors-ligne. Nous décrivons de nouvelles attaques contre les algorithmes Spook et Gimli et nous effectuons la première étude de sécurité quantique du chiffrement AES. Dans un troisième temps, nous spécifions Saturnin, une famille de cryptosystèmes à bas coût orientés vers la sécurité post-quantique. La structure de Saturnin est proche de celle de l'AES et sa sécurité en tire largement parti

Data-driven methods for statistical verification of uncertain nonlinear systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 277-290).Due to the increasing complexity of autonomous, adaptive, and nonlinear systems, engineers commonly rely upon statistical techniques to verify that the closed-loop system satisfies specified performance requirements at all possible operating conditions. However, these techniques require a large number of simulations or experiments to exhaustively search the set of possible parametric uncertainties for conditions that lead to failure. This work focuses on resource-constrained applications, such as preliminary control system design or experimental testing, which cannot rely upon exhaustive search to analyze the robustness of the closed-loop system to those requirements. This thesis develops novel statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks implement machine learning models to learn and predict the satisfaction of the requirements over the entire set of possible parameters from a small set of simulations or experiments. In order to maximize prediction accuracy, closed-loop verification techniques are developed to iteratively select parameter settings for subsequent tests according to their expected improvement of the predictions. Second, extensions of the deterministic verification frameworks redevelop these procedures for stochastic systems and these new stochastic frameworks achieve similar improvements. Lastly, the thesis details a method for transferring information between simulators or from simulators to experiments. Moreover, this method is introduced as part of a new failure-adverse closed-loop verification framework, which is shown to successfully minimize the number of failures during experimental verification without undue conservativeness. Ultimately, these data-driven verification frameworks provide principled approaches for efficient verification of nonlinear systems at all stages in the control system development cycle.by John Francis Quindlen.Ph. D