Coordinated Control of DFIG and FSIG-based Wind Farms under Unbalanced Grid Conditions

This paper investigates the control and operation of doubly-fed induction generator (DFIG) and fixed-speed induction generator (FSIG) based wind farms under unbalanced grid conditions. A DFIG system model suitable for analyzing unbalanced operation is developed, and used to assess the impact of an unbalanced supply on DFIG and FSIG operation. Unbalanced voltage at DFIG and FSIG terminals can cause unequal heating on the stator windings, extra mechanical stresses and output power fluctuations. These problems are particularly serious for the FSIG-based wind farm without a power electronic interface to the grid. To improve the stability of a wind energy system containing both DFIG and FSIG based wind farms during network unbalance, a control strategy of unbalanced voltage compensation by the DFIG systems is proposed. The DFIG system compensation ability and the impact of transmission network impedance are illustrated. The simulation results implemented in Matlab/Simulink show that the proposed DFIG control system improves not only its own performance, but also the stability of the FSIG system with the same grid connection point during network unbalance.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Output Additionality of Public Support for Innovation: Evidence for Irish Manufacturing Plants

Public support for private R&D and innovation is part of most national and regional innovation support regimes. In this article, we estimate the effect of public innovation support on innovation outputs in Ireland and Northern Ireland. Three dimensions of output additionality are considered: extensive additionality, in which public support encourages a larger proportion of the population of firms to innovate; improved product additionality, in which there is an increase in the average importance of incremental innovation; new product additionality, in which there is an increase in the average importance of more radical innovation. Using an instrumental variable approach, our results are generally positive, with public support for innovation having positive, and generally significant, extensive, improved and new product additionality effects. These results hold both for all plants and indigenously owned plants, a specific target of policy in both jurisdictions. The suggestion is that grant aid to firms can be effective in both encouraging firms to initiate new innovation and improve the quality and sophistication of their innovation activity. Our results also emphasize the importance for innovation of in-house R&D, supply-chain linkages, skill levels and capital investment, all of which may be the focus of complementary policy initiatives.

Potential output in a rapidly developing economy: the case of China and a comparison with the United States and the European Union

The authors use a growth accounting framework to examine growth of the rapidly developing Chinese economy. Their findings support the view that, although feasible in the intermediate term, China's recent pattern of extensive growth is not sustainable in the long run. The authors believe that China will be able to sustain a growth rate of 8 to 9 percent for an extended period if it moves from extensive to intensive growth. They next compare potential growth in China with historical developments in the United States and the European Union. They discuss the differences in production structure and level of development across the three economies that may explain the countries' varied intermediate-term growth prospects. Finally, the authors provide an analysis of "green" gross domestic product and the role of natural resources in China's growth. © 2009, The Federal Reserve Bank of St. Louis.

Neural network forecasts of input-output technology

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.

Dynamics of Gompertzian tumour growth under environmental fluctuations

We investigate the effect of correlated additive and multiplicative Gaussian white noise oil the Gompertzian growth of tumours. Our results are obtained by Solving numerically the time-dependent Fokker-Planck equation (FPE) associated with the stochastic dynamics. In Our numerical approach we have adopted B-spline functions as a truncated basis to expand the approximated eigenfunctions. The eigenfunctions and eigenvalues obtained using this method are used to derive approximate solutions of the dynamics under Study. We perform simulations to analyze various aspects, of the probability distribution. of the tumour cell populations in the transient- and steady-state regimes. More precisely, we are concerned mainly with the behaviour of the relaxation time (tau) to the steady-state distribution as a function of (i) of the correlation strength (lambda) between the additive noise and the multiplicative noise and (ii) as a function of the multiplicative noise intensity (D) and additive noise intensity (alpha). It is observed that both the correlation strength and the intensities of additive and multiplicative noise, affect the relaxation time.

Transport, atom blockade and output coupling in a Tonks-Girardeau gas

Recent landmark experiments have demonstrated how quantum mechanical impurities can be created within strongly correlated quantum gases and used to probe the coherence properties of these systems. Here we present a theoretical model to simulate such an output coupler for a Tonks- Girardeau gas that shows excellent agreement with the experimental results for atom transport and output coupling. The solid theoretical basis our model provides allows us to explore non-equilibrium transport phenomena in ultra-cold quantum gases and leads us to predict a regime of atom blockade, where the impurity component becomes localised in the parent cloud despite the presence of gravity. We show that this provides a stable mixed-species quantum gas in the strongly correlated limit.

Thermal and quantum fluctuations in chains of ultracold polar molecules

Ultracold polar molecules, in highly anisotropic traps and interacting via a repulsive dipolar potential, may form one-dimensional chains at high densities. According to classical theory, at low temperatures there exists a critical value of the density at which a second-order phase transition from a linear to a zigzag chain occurs. We study the effect of thermal and quantum fluctuations on these self-organized structures using classical and quantum Monte Carlo methods, by means of which we evaluate the pair correlation function and the static structure factor. Depending on the parameters, these functions exhibit properties typical of a crystalline or of a liquid system. We compare the thermal and the quantum results, identifying analogies and differences. Finally, we discuss experimental parameter regimes where the effects of quantum fluctuations on the linear-zigzag transition can be observed.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.

Wind generation output during cold weather-driven electricity demand peaks in Ireland

Recent cold winters and prolonged periods of low wind speeds have prompted concerns about the increasing penetration of wind generation in the Irish and other northern European power systems. On the combined Republic of Ireland and Northern Ireland system there was in excess of 1.5 GW of installed wind power in January 2010. As the penetration of these variable, non-dispatchable generators increases, power systems are becoming more sensitive to weather events on the supply side as well as on the demand side. In the temperate climate of Ireland, sensitivity of supply to weather is mainly due to wind variability while demand sensitivity is driven by space heating or cooling loads. The interplay of these two weather-driven effects is of particular concern if demand spikes driven by low temperatures coincide with periods of low winds. In December 2009 and January 2010 Ireland experienced a prolonged spell of unusually cold conditions. During much of this time, wind generation output was low due to low wind speeds. The impacts of this event are presented as a case study of the effects of weather extremes on power systems with high penetrations of variable renewable generation.