269 resultados para Synthetic traffic generation


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A topology for voltage-space phasor generation equivalent to a five-level inverter for an open-end winding induction motor is presented. The open-end winding induction motor is fed from both ends by two three-level inverters. The three-level inverters are realised by cascading two two-level inverters. This inverter scheme does not experience neutral-point fluctuations. Of the two three-level inverters only one will be switching at any instant in the lower speed ranges. In the multilevel carrier-based SPWM used for the proposed drive, a progressive discrete DC bias depending on the speed range is given to the reference wave to reduce the inverter switchings. The drive is implemented and tested with a 1 HP open-end winding induction motor and experimental results are presented.

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This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

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A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently pro- posed [1]. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.

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In developing countries, a high rate of growth in the demand for electric energy is felt, and so the addition of new generating units becomes inevitable. In deregulated power systems, private generating stations are encouraged to add new generations. Some of the factors considered while placing a new generating unit are: availability of esources, ease of transmitting power, distance from the load centre, etc. Finding the most appropriate locations for generation expansion can be done by running repeated power flows and carrying system studies like analyzing the voltage profile, voltage stability, loss analysis, etc. In this paper a new methodology is proposed which will mainly consider the existing network topology. A concept of T-index is introduced in this paper, which considers the electrical distances between generator and load nodes. This index is used for ranking the most significant new generation expansion locations and also indicates the amount of permissible generations that can be installed at these new locations. This concept facilitates for the medium and long term planning of power generation expansions within the available transmission corridors. Studies carried out on an EHV equivalent 10-bus system and IEEE 30 bus systems are presented for illustration purposes.

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There is a lot of pressure on all the developed and second world countries to produce low emission power and distributed generation (DG) is found to be one of the most viable ways to achieve this. DG generally makes use of renewable energy sources like wind, micro turbines, photovoltaic, etc., which produce power with minimum green house gas emissions. While installing a DG it is important to define its size and optimal location enabling minimum network expansion and line losses. In this paper, a methodology to locate the optimal site for a DG installation, with the objective to minimize the net transmission losses, is presented. The methodology is based on the concept of relative electrical distance (RED) between the DG and the load points. This approach will help to identify the new DG location(s), without the necessity to conduct repeated power flows. To validate this methodology case studies are carried out on a 20 node, 66kV system, a part of Karnataka Transco and results are presented.

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An all-digital technique is proposed for generating an accurate delay irrespective of the inaccuracies of a controllable delay line. A subsampling technique-based delay measurement unit (DMU) capable of measuring delays accurately for the full period range is used as the feedback element to build accurate fractional period delays based on input digital control bits. The proposed delay generation system periodically measures and corrects the error and maintains it at the minimum value without requiring any special calibration phase. Up to 40x improvement in accuracy is demonstrated for a commercial programmable delay generator chip. The time-precision trade-off feature of the DMU is utilized to reduce the locking time. Loop dynamics are adjusted to stabilize the delay after the minimum error is achieved, thus avoiding additional jitter. Measurement results from a high-end oscilloscope also validate the effectiveness of the proposed system in improving accuracy.

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We demonstrate 30 times enhanced flux of relativistic electrons by a silicon nanowire coated target excited by 30 fs, 800 nm laser pulses at an intensity of 3 x 10(18) W cm(-2). A measurement of the megaampere electron current via induced megagauss magnetic field supports the enhancement feature observed in the electron energy spectrum. The relativistic electrons generated at the front of nanowire coated surface are shown to travel efficiently over 500 mu m in the insulating substrate. The enhanced hot electron temperature is explained using a simple model and is supported by recent simulations. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4729010]

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In the context of the standard model with a fourth generation, we explore the allowed mass spectra in the fourth-generation quark and lepton sectors as functions of the Higgs mass. Using the constraints from unitarity and oblique parameters, we show that a heavy Higgs allows large mass splittings in these sectors, opening up new decay channels involving W emission. Assuming that the hints for a light Higgs do not yet constitute an evidence, we work in a scenario where a heavy Higgs is viable. A Higgs heavier than similar to 800 GeV would in fact necessitate either a heavy quark decay channel t' -> b'W/b' -> t'W or a heavy lepton decay channel tau' -> nu'W as long as the mixing between the third and fourth generations is small. This mixing tends to suppress the mass splittings and hence the W-emission channels. The possibility of the W-emission channel could substantially change the search strategies of fourth-generation fermions at the LHC and impact the currently reported mass limits.

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A concise approach of general utility toward mono- and di-geranylated PPAP frameworks employing `reconstructive aldol cyclization' as the key step is delineated. (C) 2012 Elsevier Ltd. All rights reserved.

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A concise stereo- and enantioselective approach to seco-prezizaane sesquiterpenoids, leading to the acquisition of two bicyclic fragments, is delineated. (c) 2012 Elsevier Ltd. All rights reserved.