No / Due to high penetration of single-phase Photovoltaic (PV) cells into low voltage (LV) distribution networks, several impacts such as voltage unbalance, voltage rise, power losses, reverse power flow arise which leads to operational constraints violation in the network. In this paper, a time series Three Phase Optimal Power Flow (TPOPF) method is proposed to minimize the voltage unbalance in LV distribution networks with high penetration of residential PVs. TPOPF problem is formulated using the current injection method in which the PVs are modelled via a time-varying PV power profile with active and reactive power control. The proposed method is validated on a real LV distribution feeder. The results show that the reactive power management of the PVs helps mitigate the voltage unbalance significantly. Moreover, the voltage unbalance index reduced significantly compared to the case without voltage unbalance minimisation. / Innovate UK GCRF Energy Catalyst Pi-CREST project under Grant number 41358; British Academy GCRF COMPENSE project under Grant GCRFNGR3\1541; Mut’ah University, Jordan

Graphics processing units function well as high performance computing devices for scientific computing. The non-standard processor architecture and high memory bandwidth allow graphics processing units (GPUs) to provide some of the best performance in terms of FLOPS per dollar. Recently these capabilities became accessible for general purpose computations with the CUDA programming environment on NVIDIA GPUs and ATI Stream Computing environment on ATI GPUs. Many applications in computational science are constrained by memory access speeds and can be accelerated significantly by using GPUs as the compute engine. Using graphics processing units as a compute engine gives the personal desktop computer a processing capacity that competes with supercomputers. Graphics Processing Units represent an energy efficient architecture for high performance computing in flow simulations and many other fields. This document reviews the graphic processing unit and its features and limitations.

No description available.

No description available.

Yes / This paper proposes a probabilistic multi-objective optimization method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimizes the total energy losses of the lines from the point of view of distribution network operators (DNOs) and maximizes the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using ɛ-constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34- bus test feeders.

In this dissertation, a new non-global regression model - the partial linear threshold regression model (PLTRM) - is proposed. Various issues related to the PLTRM are discussed. In the first main section of the dissertation (Chapter 2), we define what is meant by the term “non-global regression model”, and we provide a brief review of the current literature associated with such models. In particular, we focus on their advantages and disadvantages in terms of their statistical properties. Because there are some weaknesses in the existing non-global regression models, we propose the PLTRM. The PLTRM combines non-parametric modelling with the traditional threshold regression models (TRMs), and hence can be thought of as an extension of the later models. We verify the performance of the PLTRM through a series of Monte Carlo simulation experiments. These experiments use a simulated data set that exhibits partial linear and partial nonlinear characteristics, and the PLTRM out-performs several competing parametric and non-parametric models in terms of the Mean Squared Error (MSE) of the within-sample fit. In the second main section of this dissertation (Chapter 3), we propose a method of estimation for the PLTRM. This requires estimating the parameters of the parametric part of the model; estimating the threshold; and fitting the non-parametric component of the model. An “unbalanced penalized least squares” approach is used. This involves using restricted penalized regression spline and smoothing spline techniques for the non-parametric component of the model; the least squares method for the linear parametric part of the model; together with a search procedure to estimate the threshold value. This estimation procedure is discussed for three mutually exclusive situations, which are classified according to the way in which the two components of the PLTRM “join” at the threshold. Bootstrap sampling distributions of the estimators are provided using the parametric bootstrap technique. The various estimators appear to have good sampling properties in most of the situations that are considered. Inference issues such as hypothesis testing and confidence interval construction for the PLTRM are also investigated. In the third main section of the dissertation (Chapter 4), we illustrate the usefulness of the PLTRM, and the application of the proposed estimation methods, by modelling various real-world data sets. These examples demonstrate both the good statistical performance, and the great application potential, of the PLTRM. / Graduate

This thesis introduces a new method especially designed to control the instantaneous power in voltage sourced converters operating under unbalanced conditions, including positive, negative and zero sequence content. A transformation technique, labelled mno transformation, was developed to enable the decomposition of the total instantaneous power flowing on three-phase transmission topologies into constant and oscillating terms. It is applied to three-wire and four-wire schemes, especially accommodating zero sequence unlike previous approaches. Classical and modern electric power theories are presented, particularly focusing on their definitions for adverse AC scenarios. The main mathematical transformations conceived to analyze such situations are summarized, showing their respective advantages and disadvantages. An enhanced instantaneous power theory is introduced. The novel proposed power equations, named mno instantaneous power components, expands the application of the p-q theory, which is attached to the αβ0 transformation. The mno instantaneous power theory is applied to develop an innovative power control method for grid connected voltage sourced converters in order to minimize power oscillations during adverse AC scenarios, particularly with zero sequence content. The method permits to sustain constant instantaneous three-phase power during unbalanced conditions by controlling independently the constant and the oscillating terms related to the instantaneous power. The effectiveness of the proposed control approach and the proposed power conditioning scheme was demonstrated using electromagnetic transient simulation of a VSC connected to an AC system. / May 2017

Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a “well-functioning” model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.

Dans le cadre du programme de croissance Européen 2020, la commission européenne a mis en place officiellement un chemin à long terme pour une économie à faible émission de carbone, en aspirant une réduction d’au moins 80% des émissions de gaz à effet de serre, d’ici 2050. Répondre à ces exigences ambitieuses, impliquera un changement majeur de paradigme, et notamment en ce qui concerne les infrastructures du réseau électrique. Les percées dans la technologie des semi-conducteurs et les avancées avec les nouvelles topologies d’électronique de puissance et leurs contrôle-commandes, ont contribué à l’impulsion donnée au processus en cours de réaliser un tel SuperGrid. Une percée technologique majeure a eu lieu en 2003, avec le convertisseur modulaire multi-niveaux (MMC ou M2C), présenté par le professeur Marquardt, et qui est actuellement la topologie d’électronique de puissance la plus adaptée pour les stations HVDC. Cependant, cette structure de conversion introduit également un certain nombre de défis relativement complexes tels que les courants “additionnels” qui circulent au sein du convertisseur, entrainant des pertes supplémentaires et un fonctionnement potentiellement instable. Ce projet de thèse vise à concevoir des stratégies de commande “de haut niveau” pour contrôler le MMC adaptées pour les applications à courant continue-haute tension (HVDC), dans des conditions de réseau AC équilibrés et déséquilibrés. La stratégie de commande optimale identifiée est déterminée via une approche pour la conception du type “de haut en bas”, inhérente aux stratégies d’optimisation, où la performance souhaitée du convertisseur MMC donne la stratégie de commande qui lui sera appliquée. Plus précisément, la méthodologie d’optimisation des multiplicateurs de Lagrange est utilisée pour calculer le signal minimal de référence du courant de circulation du MMC dans son repère naturel. / Following Europe’s 2020 growth program, the Energy Roadmap 2050 launched by the European Commission (EC) has officially set a long term path for a low-carbon economy, assuming a reduction of at least 80% of greenhouse gas emissions by the year 2050. Meeting such ambitious requirements will imply a major change in paradigm, including the electricity grid infrastructure as we know it.The breakthroughs in semi-conductor technology and the advances in power electronics topologies and control have added momentum to the on-going process of turning the SuperGrid into a reality. Perhaps the most recent breakthrough occurred in 2003, when Prof. Marquardt introduced the Modular Multilevel Converter (MMC or M2C) which is now the preferred power electronic topology that is starting to be used in VSC-HVDC stations. It does however, introduce a number of rather complex challenges such as “additional” circulating currents within the converter itself, causing extra losses and potentially unstable operation. In addition, the MMC will be required to properly balance the capacitive energy stored within its different arms, while transferring power between the AC and DC grids that it interfaces.The present Thesis project aimed to design adequate “high-level” MMC control strategies suited for HVDC applications, under balanced and unbalanced AC grid conditions. The resulting control strategy is derived with a “top-to-bottom” design approach, inherent to optimization strategies, where the desired performance of the MMC results in the control scheme that will be applied. More precisely, the Lagrange multipliers optimization methodology is used to calculate the minimal MMC circulating current reference signals in phase coordinates, capable of successfully regulating the capacitive arm energies of the converter, while reducing losses and voltage fluctuations, and effectively decoupling any power oscillations that would take place in the AC grid and preventing them from propagating into the DC grid.

Em aplicações de sensoriamento remoto, há diversos problemas nos quais há conhecimento predominante sobre uma categoria ou classe alvo, e pouco conhecimento sobre as demais categorias. Nesses casos, o treinamento de um classificador é prejudicado pelo desbalanceamento de classes. Assim, o estudo de características visuais para se definir o melhor subespaço de características pode ser uma alternativa viável para melhorar o desempenho dos classificadores. O uso de abordagens baseadas em detecção de anomalias também pode auxiliar por meio da modelagem da classe normal (comumente majoritária) enquanto todas as outras classes são consideradas como anomalias. Este estudo apresentou uma base de imagens de sensoriamento remoto, cuja aplicação é identificar entre regiões de cobertura vegetal e regiões de não cobertura vegetal. Para solucionar o problema de desbalanceamento entre as classes, foram realizados estudos das características visuais a fim de definir qual o conjunto de atributos que melhor representa os dados. Também foi proposta a criação de um pipeline para se tratar bases desbalanceadas de cobertura vegetal. Este pipeline fez uso de técnicas de seleção de características e aprendizado ativo. A análise de características apresentou que o subespaço usando o extrator BIC com o índice de vegetação ExG foi o que melhor distinguiu os dados. Além disso, a técnica de ordenação proposta mostrou bom desempenho com poucas dimensões. O aprendizado ativo também ajudou na criação de um modelo melhor, com resultados comparáveis com as melhores características visuais. / In remote sensing applications, there are several problems in which there is predominant knowledge about a target category or class, and little knowledge of the other categories. In such cases, the training of a classifier is hampered by the class imbalance. Thus, the study of visual characteristics to determine the best subspace characteristics may be a feasible alternative to improve the performance of classifiers. The use of anomaly detection-based approaches can also help through the normal class modeling (usually the major class) while considering all other classes as anomalies. This study presents a remote sensing image dataset, whose application is to classify regions of the image into vegetation coverage (related to plantation) and non-vegetation coverage. To solve the class imbalance problem, studies were conducted using several visual characteristics in order to define the set of attributes that best represent the data. A pipeline that deals with the vegetation classification problem and its class imbalance issues is also proposed. This pipeline made use of feature selection techniques and active learning. The visual features analysis showed that a subspace using the BIC extractor with EXG vegetation index was the best to distinguished the data. Also, and the proposed sorting-based feature selection achieved good results with a low dimensional subspaces. Furthermore, the active learning helped creating a better model, with results comparable with the best visual features.