994 resultados para mass-wind coupling


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The development of accurate mass spectrometry, enabling the identification of all the ions extracted from the ion source in a high current implanter is described. The spectrometry system uses two signals (x-y graphic), one proportional to the magnetic field (x-axes), taken from the high-voltage potential with an optic fiber system, and the other proportional to the beam current intensity (y-axes), taken from a beam-stop. The ion beam mass register in a mass spectrum of all the elements magnetically analyzed with the same radius and defined by a pair of analyzing slits as a function of their beam intensity is presented. The developed system uses a PC to control the displaying of the extracted beam mass spectrum, and also recording of all data acquired for posterior analysis. The operator uses a LabView code that enables the interfacing between an I/O board and the ion implanter. The experimental results from an ion implantation experiment are shown. (C) 2011 Elsevier B.V. All rights reserved.

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The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.

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Demand response is assumed an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed aims the minimization of the operation costs in a smart grid operated by a virtual power player. It is especially useful when actual and day ahead wind forecast differ significantly. When facing lower wind power generation than expected, RTP is used in order to minimize the impacts of such wind availability change. The proposed model application is here illustrated using the scenario of a special wind availability reduction day in the Portuguese power system (8th February 2012).

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This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

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The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

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This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

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As wind power generation undergoes rapid growth, new technical challenges emerge: dynamic stability and power quality. The influence of wind speed disturbances and a pitch control malfunction on the quality of the energy injected into the electric grid is studied for variable-speed wind turbines with different power-electronic converter topologies. Additionally, a new control strategy is proposed for the variable-speed operation of wind turbines with permanent magnet synchronous generators. The performance of disturbance attenuation and system robustness is ascertained. Simulation results are presented and conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

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The main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.

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In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

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Ensuring sustainable development conditions is presently world widely recognized as a critically important goal. This makes the use of electricity generation technologies based on renewable energy sources very relevant. Developing countries depend on an adequate availability of electrical energy to assure economic progress and are usually characterized by a high increase in electricity consumption. This makes sustainable development a huge challenge but it can also be taken as an opportunity, especially for countries which do not have fossil resources. This paper presents a study concerning the expansion of an already existent wind farm, located in Praia, the capital of Cape Verde Republic. The paper includes results from simulation studies that have been undertaken using PSCAD software and some economic considerations.

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Nowadays, there is a growing environmental concern about were the energy that we use comes from, bringing the att ention on renewable energies. However, the use and trade of renewable e nergies in the market seem to be complicated because of the lack of guara ntees of generation, mainly in the wind farms. The lack of guarantees is usually addressed by using a reserve generation. The aggregation of DG p lants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of wind generation technologies, making them valuable in electricity markets. This paper presents some resul ts obtained with a simulation tool (ViProd) developed to support VPPs in the analysis of their operation and management methods and of their strat egies effects.

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Lisbon is the largest urban area in the Western European coast. Due to this geographical position the Atlantic Ocean serves as an important source of particles and plays an important role in many atmospheric processes. The main objectives of this study were to (1) perform a chemical characterization of particulate matter (PM2.5) sampled in Lisbon, (2) identify the main sources of particles, (3) determine PM contribution to this urban area, and (4) assess the impact of maritime air mass trajectories on concentration and composition of respirable PM sampled in Lisbon. During 2007, PM2.5 was collected on a daily basis in the center of Lisbon with a Partisol sampler. The exposed Teflon filters were measured by gravimetry and cut into two parts: one for analysis by instrumental neutron activation analysis (INAA) and the other by ion chromatography (IC). Principal component analysis (PCA) and multilinear regression analysis (MLRA) were used to identify possible sources of PM2.5 and determine mass contribution. Five main groups of sources were identified: secondary aerosols, traffic, calcium, soil, and sea. Four-day backtracking trajectories ending in Lisbon at the starting sampling time were calculated using the HYSPLIT model. Results showed that maritime transport scenarios were frequent. These episodes were characterized by a significant decrease of anthropogenic aerosol concentrations and exerted a significant role on air quality in this urban area.

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The analysis of the Higgs boson data by the ATLAS and CMS Collaborations appears to exhibit an excess of h -> gamma gamma events above the Standard Model (SM) expectations, whereas no significant excess is observed in h -> ZZ* -> four lepton events, albeit with large statistical uncertainty due to the small data sample. These results (assuming they persist with further data) could be explained by a pair of nearly mass-degenerate scalars, one of which is an SM-like Higgs boson and the other is a scalar with suppressed couplings to W+W- and ZZ. In the two-Higgs-doublet model, the observed gamma gamma and ZZ* -> four lepton data can be reproduced by an approximately degenerate CP-even (h) and CP-odd (A) Higgs boson for values of sin (beta - alpha) near unity and 0: 70 less than or similar to tan beta less than or similar to 1. An enhanced gamma gamma signal can also arise in cases where m(h) similar or equal to m(H), m(H) similar or equal to m(A), or m(h) similar or equal to m(H) similar or equal to m(A). Since the ZZ* -> 4 leptons signal derives primarily from an SM-like Higgs boson whereas the gamma gamma signal receives contributions from two (or more) nearly mass-degenerate states, one would expect a slightly different invariant mass peak in the ZZ* -> four lepton and gamma gamma channels. The phenomenological consequences of such models can be tested with additional Higgs data that will be collected at the LHC in the near future. DOI: 10.1103/PhysRevD.87.055009.

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A multiresidue gas chromatographic method for the determination of six fungicides (captan, chlorthalonil, folpet, iprodione, procymidone and vinclozolin) and one acaricide (dicofol) in still and fortified wines was developed. Solid-phase microextraction (SPME) was chosen for the extraction of the compounds from the studied matrices and tandem mass spectrometry (MS/MS) detection was used. The extraction consists in a solvent free and automated procedure and the detection is highly sensitive and selective. Good linearity was obtained with correlation coefficients of regression (R2) > 0.99 for all the compounds. Satisfactory results of repeatability and intermediate precision were obtained for most of the analytes (RSD < 20%). Recoveries from spiked wine ranged from 80.1% to 112.0%. Limits of quantification (LOQs) were considerably below the proposedmaximumresidue limits (MRLs) for these compounds in grapes and below the suggested limits for wine (MRLs/10), with the exception of captan.

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We have developed a new method for single-drop microextraction (SDME) for the preconcentration of organochlorine pesticides (OCP) from complex matrices. It is based on the use of a silicone ring at the tip of the syringe. A 5 μL drop of n-hexane is applied to an aqueous extract containing the OCP and found to be adequate to preconcentrate the OCPs prior to analysis by GC in combination with tandem mass spectrometry. Fourteen OCP were determined using this technique in combination with programmable temperature vaporization. It is shown to have many advantages over traditional split/splitless injection. The effects of kind of organic solvent, exposure time, agitation and organic drop volume were optimized. Relative recoveries range from 59 to 117 %, with repeatabilities of <15 % (coefficient of variation) were achieved. The limits of detection range from 0.002 to 0.150 μg kg−1. The method was applied to the preconcentration of OCPs in fresh strawberry, strawberry jam, and soil.