889 resultados para Trajectory Sensitivity, TCSC, Power System Stability, TCSC Control


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Esse trabalho compara os algoritmos C4.5 e MLP (do inglês “Multilayer Perceptron”) aplicados a avaliação de segurança dinâmica ou (DSA, do inglês “Dynamic Security Assessment”) e em projetos de controle preventivo, com foco na estabilidade transitória de sistemas elétricos de potência (SEPs). O C4.5 é um dos algoritmos da árvore de decisão ou (DT, do inglês “Decision Tree”) e a MLP é um dos membros da família das redes neurais artificiais (RNA). Ambos os algoritmos fornecem soluções para o problema da DSA em tempo real, identificando rapidamente quando um SEP está sujeito a uma perturbação crítica (curto-circuito, por exemplo) que pode levar para a instabilidade transitória. Além disso, o conhecimento obtido de ambas as técnicas, na forma de regras, pode ser utilizado em projetos de controle preventivo para restaurar a segurança do SEP contra perturbações críticas. Baseado na formação de base de dados com exaustivas simulações no domínio do tempo, algumas perturbações críticas específicas são tomadas como exemplo para comparar os algoritmos C4.5 e MLP empregadas a DSA e ao auxílio de ações preventivas. O estudo comparativo é testado no sistema elétrico “New England”. Nos estudos de caso, a base de dados é gerada por meio do programa PSTv3 (“Power System Toolbox”). As DTs e as RNAs são treinada e testadas usando o programa Rapidminer. Os resultados obtidos demonstram que os algoritmos C4.5 e MLP são promissores nas aplicações de DSA e em projetos de controle preventivo.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY Y-MPZE/232, and the performance results are discussed.

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A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

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In this paper, a new algebraic-graph method for identification of islanding in power system grids is proposed. The proposed method identifies all the possible cases of islanding, due to the loss of a equipment, by means of a factorization of the bus-branch incidence matrix. The main features of this new method include: (i) simple implementation, (ii) high speed, (iii) real-time adaptability, (iv) identification of all islanding cases and (v) identification of the buses that compose each island in case of island formation. The method was successfully tested on large-scale systems such as the reduced south Brazilian system (45 buses/72 branches) and the south-southeast Brazilian system (810 buses/1340 branches). (C) 2011 Elsevier Ltd. All rights reserved.

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In the current market system, power systems are operated at higher loads for economic reasons. Power system stability becomes a genuine concern in such operating conditions. In case of failure of any larger component, the system may become stressed. These events may start cascading failures, which may lead to blackouts. One of the main reasons of the major recorded blackout events has been the unavailability of system-wide information. Synchrophasor technology has the capability to provide system-wide real time information. Phasor Measurement Units (PMUs) are the basic building block of this technology, which provide the Global Positioning System (GPS) time-stamped voltage and current phasor values along with the frequency. It is being assumed that synchrophasor data of all the buses is available and thus the whole system is fully observable. This information can be used to initiate islanding or system separation to avoid blackouts. A system separation strategy using synchrophasor data has been developed to answer the three main aspects of system separation: (1) When to separate: One class support machines (OC-SVM) is primarily used for the anomaly detection. Here OC-SVM was used to detect wide area instability. OC-SVM has been tested on different stable and unstable cases and it is found that OC-SVM has the capability to detect the wide area instability and thus is capable to answer the question of “when the system should be separated”. (2) Where to separate: The agglomerative clustering technique was used to find the groups of coherent buses. The lines connecting different groups of coherent buses form the separation surface. The rate of change of the bus voltage phase angles has been used as the input to this technique. This technique has the potential to exactly identify the lines to be tripped for the system separation. (3) What to do after separation: Load shedding was performed approximately equal to the sum of power flows along the candidate system separation lines should be initiated before tripping these lines. Therefore it is recommended that load shedding should be initiated before tripping the lines for system separation.

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Before entering the central nervous system (CNS) immune cells have to penetrate any one of its barriers, namely either the endothelial blood-brain barrier, the epithelial blood-cerebrospinal fluid barrier or the tanycytic barrier around the circumventricular organs, all of which maintain homeostasis within the CNS. The presence of these barriers in combination with the lack of lymphatic vessels and the absence of classical MHC-positive antigen presenting cells characterizes the CNS as an immunologically privileged site. In multiple sclerosis a large number of inflammatory cells gains access to the CNS parenchyma. Studies performed in experimental autoimmune encephalomyelitis (EAE), a rodent model for multiple sclerosis, have enabled us to understand some of the molecular mechanisms involved in immune cell entry into the CNS. In particular, the realization that /alpha4-integrins play a predominant role in leukocyte trafficking to the CNS has led to the development of a novel drug for the treatment of relapsing-remitting multiple sclerosis, which targets /alpha4-integrin mediated immune cell migration to the CNS. At the same time, the involvement of other adhesion and signalling molecules in this process remains to be investigated and novel molecules contributing to immune cell entry into the CNS are still being identified. The entire process of immune cell trafficking into the CNS is strictly controlled by the brain barriers not only under physiological conditions but also during neuroinflammation, when some barrier properties are lost. Thus, immune cell entry into the CNS critically depends on the unique characteristics of the brain barriers maintaining CNS homeostasis.

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Photovoltaic power has become one of the most popular research area in new energy field. In this report, the case of household solar power system is presented. Based on the Matlab environment, the simulation is built by using Simulink and SimPowerSystem. There are four parts in a household solar system, solar cell, MPPT system, battery and power consumer. Solar cell and MPPT system are been studied and analyzed individually. The system with MPPT generates 30% more energy than the system without MPPT. After simulating the household system, it is can be seen that the power which generated by the system is 40.392 kWh per sunny day. By combining the power generated by the system and the price of the electric power, 8.42 years are need for the system to achieve a balance of income and expenditure when weather condition is considered.

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Principal Component Analysis (PCA) is a popular method for dimension reduction that can be used in many fields including data compression, image processing, exploratory data analysis, etc. However, traditional PCA method has several drawbacks, since the traditional PCA method is not efficient for dealing with high dimensional data and cannot be effectively applied to compute accurate enough principal components when handling relatively large portion of missing data. In this report, we propose to use EM-PCA method for dimension reduction of power system measurement with missing data, and provide a comparative study of traditional PCA and EM-PCA methods. Our extensive experimental results show that EM-PCA method is more effective and more accurate for dimension reduction of power system measurement data than traditional PCA method when dealing with large portion of missing data set.

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Renewable energy sources are believed to reduce drastically greenhouse gas emissions that would otherwise be generated from fossil fuels used to generate electricity. This implies that a unit of renewable energy will replace a unit of fossil-fuel, with its CO2 emissions, on an equivalent basis (with no other effects on the grid). But, the fuel economy and emissions in the existing power systems are not proportional with the electricity production of intermittent sources due to cycling of the fossil fuel plants that make up the balance of the grid (i.e. changing the power output makes thermal units to operate less efficiently). This study focuses in the interactions between wind generation and thermal plants cycling, by establishing the levels of extra fuel use caused by decreased efficiencies of fossil back-up for wind electricity in Spain. We analyze the production of all thermal plants in 2011, studying different scenarios where wind penetration causes major deviations in programming, while we define a procedure for quantifying the carbon reductions by using emission factors and efficiency curves from the existing installations. The objectives are to discuss the real contributions of renewable energies to the environmental targets as well as suggest alternatives that would improve the reliability of future power systems.

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A Wearable Power System (WPS) is a portable power source utilized primarily to power the modern soldier’s electronic equipment. Such a system has to satisfy output power demands in the range of 20 W...200 W, specified as a 4-day mission profile and has a weight limit of 4 kg. To meet these demands, an optimization of a WPS, comprising an internal combustion (IC) engine, permanent magnetic three-phase electrical motor/generator, inverter, Li-batteries, DC-DC converters, and controller, is performed in this paper. The mechanical energy extracted from the fuel by IC engine is transferred to the generator that is used to recharge the battery and provide the power to the electrical output load. The main objectives are to select the engine, fuel and battery type, to match the weight of fuel and the number of battery cells, to find the optimal working point of engine and to minimize the system weight. To provide the second output voltage level of 14 VDC, a separate DC-DC converter is connected between the battery and the load, and optimized for the specified mission profile. A prototype of the WPS based on the optimization presented in the paper results in a total system weight of 3.9 kg and fulfils the mission profile.

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This report analyzes the basis of hydrogen and power integration strategies, by using water electrolysis processes as a means of flexible energy storage at large scales.