89 resultados para real power loss


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The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly-fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This paper presents a measurement based method for the early detection of power system oscillations, with attention to mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet transform and support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in different frequency bands, while SVDD is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude or that are resonant can be alarmed to the system operator, to reduce the risk of system instability. Method evaluation is exemplified used real data from a chosen wind farm.

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This paper considers the enhancement of loss-of-mains detection by use of a differential rate-of-change-of-frequency relay to reduce nuisance tripping and improve sensitivity to small excursions in frequency. The telecommunications media which might carry the differential ROCOF signal are reviewed with a focus on channel latency, bandwidth and security.

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This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

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This paper presents the design of a novel 8-way power-combining transformer for use in mm-wave power amplifier (PA). The combiner exhibits a record low insertion loss of 1.25 dB at 83.5 GHz. A complete circuit comprised of a power splitter, two-stage cascode PA array, a power combiner and input/output matching elements was designed and realized in SiGe technology. Measured gain of at least 16.8 dB was obtained from 76.4 GHz to 85.3 GHz with a peak 19.5 dB at 83 GHz. The prototype delivered 12.5 dBm OP and 14 dBm saturated output power when operated from a 3.2 V DC supply voltage at 78 GHz. © 2013 IEEE.

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This paper presents the design and implementation of a differential 4-way power-combining amplifier operating at E-band. The proposed 4-way power combiner (4WPC) facilitates short interconnects to the PA cells, thereby resulting in reduced loss. Simple C-L-C and L-C networks are deployed in order to compensate inductive loading due to the routing lines that would otherwise introduce mismatch and subsequently increase overall loss. Realized in SiGe technology, the PA prototype delivered 13.2 dBm output-referred 1-dB compression point and 14.3 dBm saturated output power when operated from a single 3.3 V DC supply at 75 GHz.

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The relationship between retention loss in single crystal PbTiO3 ferroelectric thin films and leakage currents is demonstrated by piezoresponse and conductive atomic force microscopy measurements. It was found that the polarization reversal in the absence of an electric field followed a stretched exponential behavior 1-exp[-(t/k)(d)] with exponent d>1, which is distinct from a dispersive random walk process with d <. The latter has been observed in polycrystalline films for which retention loss was associated with grain boundaries. The leakage current indicates power law scaling at short length scales, which strongly depends on the applied electric field. Additional information of the microstructure, which contributes to an explanation of the presence of leakage currents, is presented with high resolution transmission electron microscopy analysis.

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Abstract—Power capping is an essential function for efficient power budgeting and cost management on modern server systems. Contemporary server processors operate under power caps by using dynamic voltage and frequency scaling (DVFS). However, these processors are often deployed in non-uniform memory
access (NUMA) architectures, where thread allocation between cores may significantly affect performance and power consumption. This paper proposes a method which maximizes performance under power caps on NUMA systems by dynamically optimizing two knobs: DVFS and thread allocation. The method selects the optimal combination of the two knobs with models based on artificial neural network (ANN) that captures the nonlinear effect of thread allocation on performance. We implement
the proposed method as a runtime system and evaluate it with twelve multithreaded benchmarks on a real AMD Opteron based NUMA system. The evaluation results show that our method outperforms a naive technique optimizing only DVFS by up to
67.1%, under a power cap.

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Loss-of-mains protection is an important component of the protection systems of embedded generation. The role of loss-of-mains is to disconnect the embedded generator from the utility grid in the event that connection to utility dispatched generation is lost. This is necessary for a number of reasons, including the safety of personnel during fault restoration and the protection of plant against out-of-synchronism reclosure to the mains supply. The incumbent methods of loss-of-mains protection were designed when the installed capacity of embedded generation was low, and known problems with nuisance tripping of the devices were considered acceptable because of the insignificant consequence to system operation. With the dramatic increase in the installed capacity of embedded generation over the last decade, the limitations of current islanding detection methods are no longer acceptable. This study describes a new method of loss-of-mains protection based on phasor measurement unit (PMU) technology, specifically using a low cost PMU device of the authors' design which has been developed for distribution network applications. The proposed method addresses the limitations of the incumbent methods, providing a solution that is free of nuisance tripping and has a zero non-detection zone. This system has been tested experimentally and is shown to be practical, feasible and effective. Threshold settings for the new method are recommended based on data acquired from both the Great Britain and Ireland power systems.

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The aim of this paper is to develop a new generation of extruder control system for recycled materials which has ability to automatically maintain constant a polymer melt viscosity of mixed recycled polymers during extrusion, regardless of variations in the Melt Flow Index (MFI) of recycled mixed grade high density polyethylene (HDPE) feedstock. The variations in MFI are due to differences in the source of the recycled material used. The work describes how melt viscosity for specific extruder/die system is calculated in real time using the rheological properties of the materials, the pressure drop through the extruder die and the actual throughput measurements using a gravimetric loss-in-weight hopper feeder. A closed-loop controller is also developed to automatically regulate screw speed and barrel temperature profile to achieve constant viscosity and enable consistent processing of variable grade recycled HDPE materials. Such a system will improve processability of mixed MFI polymers may also reduce the risk of polymer melt degradation, reduce producing large volumes of scrap/waste and lead to improvement in product quality. The experimental results of real time viscosity measurement and control using a 38 mm single screw extruder with different recycled HDPEs with widely different MFIs are reported in this work.

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A new method is presented for transmission loss allocation based on the separation of transmission loss caused by load and the loss due to circulating currents between generators. The theoretical basis for and derivation of the loss formulae are presented using simple systems. The concept is then extended to a general power system using the Ybus model. Details of the application of the proposed method to a typical power system are presented along with results from the IEEE 30 bus test system. The results from both the small system and the standard IEEE test system demonstrate the validity of the proposed method.

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Torrefaction based co-firing in a pulverized coal boiler has been proposed for large percentage of biomass co-firing. A 220 MWe pulverized coal-power plant is simulated using Aspen Plus for full understanding the impacts of an additional torrefaction unit on the efficiency of the whole power plant, the studied process includes biomass drying, biomass torrefaction, mill systems, biomass/coal devolatilization and combustion, heat exchanges and power generation. Palm kernel shells (PKS) were torrefied at same residence time but 4 different temperatures, to prepare 4 torrefied biomasses with different degrees of torrefaction. During biomass torrefaction processes, the mass loss properties and released gaseous components have been studied. In addition, process simulations at varying torrefaction degrees and biomass co-firing ratios have been carried out to understand the properties of CO2 emission and electricity efficiency in the studied torrefaction based co-firing power plant. According to the experimental results, the mole fractions of CO 2 and CO account for 69-91% and 4-27% in torrefied gases. The predicted results also showed that the electrical efficiency reduced when increasing either torrefaction temperature or substitution ratio of biomass. A deep torrefaction may not be recommended, because the power saved from biomass grinding is less than the heat consumed by the extra torrefaction process, depending on the heat sources. 

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The char oxidation of a torrefied biomass and its parent material was carried out in an isothermal plug flow reactor (IPFR), which is able to rapidly heat the biomass particles to a maximum temperature of 1400 °C at a heating rate of 104 °C/s, similar to the real conditions found in power plant furnaces. During each char oxidation test, the residues of biomass particles were collected and analyzed to determine the weight loss based on the ash tracer method. According to the experimental results, it can be concluded that chars produced from a torrefied biomass are less reactive than the ones produced, under the same conditions, from its raw material. The apparent kinetics of the torrefied biomass and its parent material are determined by minimizing the difference between the modeled and the experimental results. The predicted weight loss during char oxidation, using the determined kinetics, agrees well with experimental results

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A PSS/E 32 model of a real section of the Northern Ireland electrical grid was dynamically controlled with Python 2.5. In this manner data from a proposed wide area monitoring system was simulated. The area is of interest as it is a weakly coupled distribution grid with significant distributed generation. The data was used to create an optimization and protection metric that reflected reactive power flow, voltage profile, thermal overload and voltage excursions. Step changes in the metric were introduced upon the operation of special protection systems and voltage excursions. A wide variety of grid conditions were simulated while tap changer positions and switched capacitor banks were iterated through; with the most desirable state returning the lowest optimization and protection metric. The optimized metric was compared against the metric generated from the standard system state returned by PSS/E. Various grid scenarios were explored involving an intact network and compromised networks (line loss) under summer maximum, summer minimum and winter maximum conditions. In each instance the output from the installed distributed generation is varied between 0 MW and 80 MW (120% of installed capacity). It is shown that in grid models the triggering of special protection systems is delayed by between 1 MW and 6 MW (1.5% to 9% of capacity), with 3.5 MW being the average. The optimization and protection metric gives a quantitative value for system health and demonstrates the potential efficacy of wide area monitoring for protection and control.

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Energy consumption has become an important area of research of late. With the advent of new manycore processors, situations have arisen where not all the processors need to be active to reach an optimal relation between performance and energy usage. In this paper a study of the power and energy usage of a series of benchmarks, the PARSEC and the SPLASH- 2X Benchmark Suites, on the Intel Xeon Phi for different threads configurations, is presented. To carry out this study, a tool was designed to monitor and record the power usage in real time during execution time and afterwards to compare the r

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Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.