No description available.

Orientadores: Rangel Arthur, Francisco José Arnold / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia / Made available in DSpace on 2018-08-26T23:50:19Z (GMT). No. of bitstreams: 1 Pedrosa_JezerOliveira_M.pdf: 1689446 bytes, checksum: fa812fba987c6c905ee50de809f6f732 (MD5) Previous issue date: 2015 / Resumo: Neste trabalho é criada uma base de dados de sinais de corrente de cargas domésticas e é proposta uma técnica para a identificação dessas cargas, etapa necessária para a desagregação das cargas dentro do contexto SMART GRID. A técnica de desagregação proposta baseia-se no uso de redes neurais e na transformada wavelet. A identificação das cargas elétricas tem como objetivo a descoberta de qual equipamento está ligado na rede elétrica. Dessa forma é possível calcular separadamente quanto cada equipamento está consumindo de energia elétrica. Os resultados obtidos a partir das informações extraídas com o emprego dos algoritmos propostos são discutidos e apresentados. Os algoritmos de processamento e identificação das cargas via redes neurais e transformada wavelet foram desenvolvidos no ambiente do MATLAB. Os resultados encontrados comprovam a eficácia da técnica proposta / Abstract: This work aims to create a current signal database of domestic loads and proposes a technique for identifying such loads, necessary step for the disaggregation of loads in the Smart-grid context. The disaggregation of the proposed technique is based on the use of neural networks and wavelet transform. The identification of electrical loads aims to discover what equipment is connected to utility power. Thus it is possible to calculate separately for each device is consuming electricity. The results obtained from the information derived from the proposed algorithms are discussed and presented. The algorithms processing and load identification by wavelet and neural networks were developed using MATLAB environment. The results prove the efficiency of the proposed technique / Mestrado / Tecnologia e Inovação / Mestre em Tecnologia

The application of Internet of Things (IoT) where sensors and actuators embedded in physical objects are linked through wired and wireless networks has shown a rapid growth over the past years in various domains with the benefits of improving efficiency and productivity, reducing cost, providing mobility and agility, etc. This dissertation focuses on developing signal processing and machine learning based techniques in IoT with applications to 1) smart energy generation and 2) robust indoor localization in smart city. Smart grids, in contrast to legacy grids, facilitate more efficient electricity generation and consumption by allowing two-way information exchange among various components in the grid and the users based on the measurements from numerous sensors located at different places. Due to the introduction of information communications, a smart grid is faced with the risk of external attacks which is aimed to take control of the grid. In particular, electricity generation from photovoltaic (PV) systems is a mature power generation technology utilizing renewable resources, owning to its advantages in clean production, reduced cost and high flexibility. However, the performance of a PV system can be susceptible and unstable due to various physical failures and dynamic environments (internal circuit faults, partial shading, etc.). To safeguard the system security, fault or attack detection technologies are of great importance for PV systems and smart grids. Existing approaches on fault or attack detection either rely on the prediction by a predetermined system model which acts as reference data for comparison or can be applied only within a certain set of component (e.g., several PV strings) based on local statistical properties without the capability of generalization. Furthermore, the output performance of a PV system is dynamic under different environmental conditions (irradiance level, temperature, etc.), which can be optimized by the technique of maximum power point tracking (MPPT). However, previous studies on MPPT usually require prior knowledge of the system model or high computational complexity for iterative optimization. Smart city, as another important application of IoT, relies on analysis of the measurement data from sensors located at users and environments to provider intelligent solutions in our daily life. One of the fundamental tasks for advanced location-based services is to accurately localize the user in a certain environment, e.g., on a certain floor inside a building. Indoor localization is faced with challenges of moving users, limited availability of sensors and noisy measurements due to hardware constraints and external interferences. This dissertation first describes our advanced fault/attack detection and localization methods for PV systems and smart grids, then develops our enhanced MPPT techniques for PV systems, and finally presents our robust indoor localization methods for smartphone users, based on statistical signal processing and machine learning approaches. In Chapter 2 and Chapter 3, we proposes fault/attack detection method in PV systems and smart grids respectively in the framework of abrupt change detection utilizing sequential output measurements without assuming any prior knowledge of the system characteristics or particular faulty/attack patterns, such that an alarm will triggered regardless of the magnitude or the type of faulty/attack signals. Starting from the proposed fault detection method in Chapter 2, we present our fault localization method for PV systems in Chapter 4 where the central controller is able to identify the faulty PV strings without full knowledge of each local measurements. Chapter 5 studies the MPPT method under dynamic shading conditions where we adopt neural networks to assist the identification of the global maximum power point by depicting the relationship between the system output power and the operating voltage. In Chapter 6, to tackle the challenge of accurate and robust indoor localization for smart city when sensors provides noisy measurement data, we propose a cooperative localization method which exploits the readings of the received strengths of Wi-Fi signals at the smartphone users and the relative distances among neighboring users to combat the deterioration due to aggregated measurement errors. Throughout the dissertation, our proposed methods are followed by simulations (of a PV system or a grid under various operating conditions) or experiments (of localizing moving users with smartphones to record sensors' measurements). The results demonstrate that our proposed fault/attack detection and localization methods and MPPT schemes can achieve higher adaptivity and efficiency with robustness against various external conditions an lower computational complexity, and our cooperative localization methods have high localization accuracy even given large measurement errors and limited measurement data.

Modernized and advanced electricity transmission and distribution infrastructure ensures reliable, efficient, and affordable delivery of electric power. The complexity of fault location problem increases with the proliferation of unusual topologies and with the advent of renewable energy-based power generation in the smart grid environment. The proliferation of new Intelligent Electronic Devices (IEDs) provides a venue for the implementation of more accurate and intelligent fault location methods. This dissertation focuses on intelligent fault location methods for smart power grids and it aims at improving fault location accuracies and decreasing the cost and the mean time to repair damaged equipment in major power outages subsequently increasing the reliability of the grid. The developed methods utilize wavelet transformation to extract the traveling wave information in the very fast voltage and current transients which are initiated immediately after a fault occurs, support vector machines to classify the fault type and identify the faulted branches and finally Bewley diagrams to precisely locate the fault. The approach utilizes discrete wavelet transformation (DWT) for analysis of transient voltage and current measurements. The transient wavelet energies are calculated and utilized as the input for support vector machine (SVM) classifiers. SVM learns the mapping between inputs (i.e. transient voltages and/or currents wavelet energies) and desired outputs (i.e. faulty phase and/or faulty section) through processing a set of training cases. This dissertation presents the proposed methodologies applied to three complex power transmission systems. The first transmission system is a three-terminal (teed) three-phase AC transmission network, a common topology in high- and extra high-voltage networks. It is used to connect three substations that are wide apart from each other through long transmission lines with a tee-point, which is not supported by a substation nor equipped with a measuring device. The developed method overcomes the difficulties introduced by the discontinuity: the tee point. The second topology is a hybrid high voltage alternative current (HVAC) transmission line composed of an overhead line combined with an underground cable. The proposed fault location method is utilized to overcome the difficulties introduced by the discontinuity at the transition point from the overhead line to the underground cable and the different traveling wave velocities along the line and the cable. The third topology is a segmented high voltage direct current (HVDC) transmission line including an overhead line combined with an underground cable. This topology is widely utilized to transmit renewable energy-based electrical power from remote locations to the load centers such as from off-shore wind farms to on-shore grids. This dissertation introduces several enhancements to the existing fault type and fault location algorithms: improvement in the concept of fault type classification and faulty section identification by using SVMs with smaller inputs and improvements in the fault location in the complex configurations by utilizing less measurements from the terminals. / Ph. D.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance ($VC_{SIS}$) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible ($SIS$) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, $VC_{SIS}$ provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barab\'si-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric $VC_$ is introduced to assess the robustness of networks with respect to the spread of susceptible/infected/recovered ($SIR$) epidemics. To compute $VC_$, we propose a novel individual-based approach to model the spread of $SIR$ epidemics in networks, which captures the infection size for a given effective infection rate. Thus, $VC_$ quantitatively integrates the infection strength with the corresponding infection size. To optimize the $VC_$ metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering in the School of Electrical and Information Engineering, July 2017 / This dissertation presents the design and testing of controllers intended to provide au- tonomous demand response, through the use of water heater loads and grid frequency measurements. The controllers use measured frequency as an indication of the strain on a utility grid, which allows demand side management to be isolated from any form of central control. Water heaters can operate as exible loads because their power consump- tion can be dispatched or deferred without directly impacting users. These properties make it possible to control individual water heaters based on the functioning of the grid, rather than end user input. The purpose of this research is to ultimately provide a low- cost alternative to a traditional Smart Grid, that will improve the resilience of a grid without negatively impacting users. The controllers presented here focus on ensuring that users receive hot water, while attempting to reduce any imbalance between power generated and power consumed on the grid. Simulations of these controllers in various situations highlight that while the controllers developed respond suitably to variations in the grid frequency and adequately ensure end users receive hot water, the practical bene t of the controllers depends largely on the intrinsic characteristics of the grid. / CK2018

A research report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg in partial fulfilment of the requirements for the degree of Master of Science in Engineering, Johannesburg, August 2017 / The objective of this research work is to investigate the use of a high reliability micro grid to supply emergency electrical power to a nuclear facility following loss of offsite power (LOOP) accident. Most of the nuclear facilities around the world utilize diesel generators and battery banks as emergency power to back up the grid power supply. This power supply configuration represents the concept of the micro-grid system. The research work proposes reliability improvement of the emergency power supply by introducing diverse energy sources and energy storage systems. The energy sources and storage systems that were investigated include renewable energy sources, decay heat recovery system and large scale energy storage systems. The investigation results presented include information on the suitable energy sources and energy storage system, establishment of the reliable architectural layout and evaluation of the micro-grid system in terms of capacity adequacy and reliability. / XL2018

Thesis (PhD (Electrical Engineering))--Cape Peninsula University of Technology, 2018. / Electrical energy can be easily used and converted to other forms of energy for various applications. Technological advancement increases the dependency on electricity to a great extent. Various internal and external factors are responsible for the bad quality of power in power systems. The performance of the system is greatly affected by the presence of harmonics, as well as voltage and frequency variations, which leads to the malfunctioning of the device and decline of power quality and supply at load side. The reactive power compensation is carried out for better power quality. The literature survey is done to find the best and efficient scheme for reactive power compensation and mitigation of various power quality problems. The devices which are used to measure various power quality factors are discussed. Various mitigating schemes are surveyed in order to compensate reactive power and to improve the power quality at the distribution end. The integration of the most widely used renewable energy, wind energy in the distribution system creates technical issues like stability of the grid, harmonic distortion, voltage regulation, active and reactive power compensation etc. which are restricted to IEC and IEEE standards. One of the topics this thesis addresses is regulation in the reactive power generated along with voltage regulation by using an effective power electronics device known as a STATCOM. The main power quality factors like overvoltage and voltage flickers are mitigated by establishing STATCOMs in small wind farms. The wind farms are equipped with three wind turbines. These three wind turbines found in the wind farm can be operated together or one after another with an introduced delay. A glitch in even a little piece of a power grid can result in loss of efficiency, income and at times even life. In this manner, it is basic to outline a system which can distinguish the faults of the power system and take a faster response to recover it back to required reactive power. Two devices STATCOM and D-STATCOM are used for this purpose in this thesis. The D-STATCOM circuit and operating principle are also discussed in thesis. Different topologies of D-STATCOM discussed with their benefits and shortcomings. The voltage, current and hybrid technologies of D-STATCOM are also discussed.

Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018. / A smart grid is an intelligent power delivery system integrating traditional and advanced control, monitoring, and protection systems for enhanced reliability, improved efficiency, and quality of supply. To achieve a smart grid, technical challenges such as voltage instability; power loss; and unscheduled power interruptions should be mitigated. Therefore, future smart grids will require intelligent solutions at transmission and distribution levels, and optimal placement & sizing of grid components for optimal steady state and dynamic operation of the power systems. At distribution levels, feeder reconfiguration and Distributed Generation (DG) can be used to improve the distribution network performance. Feeder reconfiguration consists of readjusting the topology of the primary distribution network by remote control of the tie and sectionalizing switches under normal and abnormal conditions. Its main applications include service restoration after a power outage, load balancing by relieving overloads from some feeders to adjacent feeders, and power loss minimisation for better efficiency. On the other hand, the DG placement problem entails finding the optimal location and size of the DG for integration in a distribution network to boost the network performance. This research aims to develop Particle Swarm Optimization (PSO) algorithms to solve the distribution network feeder reconfiguration and DG placement & sizing problems. Initially, the feeder reconfiguration problem is treated as a single-objective optimisation problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation and load balancing). Similarly, the DG placement problem is treated as a single-objective problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation, voltage deviation minimisation, Voltage stability Index maximisation). The developed PSO algorithms are implemented and tested for the 16-bus, the 33-bus, and the 69-bus IEEE distribution systems. Additionally, a parallel computing method is developed to study the operation of a distribution network with a feeder reconfiguration scheme under dynamic loading conditions.

Orientador: Luiz Carlos Pereira da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T01:49:18Z (GMT). No. of bitstreams: 1 Geraldi_Douglas_M.pdf: 3918130 bytes, checksum: a6b640b4707af6b9173674deab29eece (MD5) Previous issue date: 2013 / Resumo: Existe atualmente um consenso de que as Redes Inteligentes favoreçam a solução de diversos problemas presentes no dia a dia das concessionárias distribuidoras de energia elétrica, tais como a gestão inteligente do carregamento e o gerenciamento automático na recuperação do fornecimento de energia (self-healing). Por outro lado, a implantação de tecnologias de redes inteligentes também pode criar novas dificuldades para as distribuidoras. Especial atenção deve ser dada à facilidade do acesso ao sistema elétrico por parte da microgeração - quer seja solar, eólica, micro turbinas a gás, etc. - possibilitada pela substituição dos medidores atuais por medidores inteligentes e por legislação específica recentemente publicada pela ANEEL. Neste trabalho busca-se apontar e quantificar alguns impactos técnicos relacionados à injeção de potência em um circuito secundário de distribuição. Através do estudo de cenários com crescente nível de penetração de microgeradores fotovoltaicos residenciais (tetos solares) são analisados os impactos na curva de carga do prossumidor, no perfil de tensão da rede, nas perdas elétricas e no desequilíbrio de tensão. As simulações dos cenários supracitados são realizadas através do software livre Gridlab-D, desenvolvido pela Pacific Northwest National Laboratory (PNNL) para estudos de aspectos de redes inteligentes via simulação computacional. Uma das vantagens desse pacote é a integração com base de dados meteorológicos, permitindo, por exemplo, a estimativa da geração fotovoltaica mês a mês para um determinado ano constante na base de dados / Abstract: Nowadays, there is a consensus that the Smart Grid can promote the solution of various problems present in distribution utilities, such as intelligent load management and self-healing. How-ever, the deployment of smart grid technologies can also create new difficulties. Special attention should be given to the open access to the electrical grid from the micro-generation plants, such as solar photovoltaic, wind turbines and gas micro-turbines, which will be possible with the re-placement of the current meters for smart meters and by specific regulation recently published by ANEEL. This work intends to identify and quantify some technical impacts related to power injection from micro-generators in a secondary distribution circuit. Through the study of scenarios with increasing penetration of residential photovoltaic micro-generators (solar roofs) some impacts are analyzed: impacts on the load profile of the prosumer; impacts on the voltage profile of the network; impacts on the electrical losses and voltage imbalance. The simulations of the above scenarios are performed by using Gridlab-D, free software developed by Pacific Northwest National Laboratory (PNNL) to study aspects of smart grids via computer simulation. One ad-vantage of this package is the integration with meteorological database, enabling, for example, the estimation of photovoltaic generation every month for a given year contained in the database / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica