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.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available. 

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Measuring icon complexity: An automated analysis

Measures of icon designs rely heavily on surveys of the perceptions of population samples. Thus, measuring the extent to which changes in the structure of an icon will alter its perceived complexity can be costly and slow. An automated system capable of producing reliable estimates of perceived complexity could reduce development costs and time. Measures of icon complexity developed by Garcia, Badre, and Stasko (1994) and McDougall, Curry, and de Bruijn (1999) were correlated with six icon properties measured using Matlab (MathWorks, 2001) software, which uses image-processing techniques to measure icon properties. The six icon properties measured were icon foreground, the number of objects in an icon, the number of holes in those objects, and two calculations of icon edges and homogeneity in icon structure. The strongest correlates with human judgments of perceived icon complexity (McDougall et al., 1999) were structural variability (r(s) = .65) and edge information (r(s) =.64).

Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm

The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the Heavy-Ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has proof of the convergence rate of O(k-2), where k is the number of iterations. We demonstrate the superior convergence performance of the scaled H-B method on both synthetic and real 3D images.

Finite Element Analysis of Drilling of Carbon Fibre Reinforced Composites

Despite the increased applications of the composite materials in aerospace due to their exceptional physical and mechanical properties, the machining of composites remains a challenge. Fibre reinforced laminated composites are prone to different damages during machining process such as delamination, fibre pull-out, microcracks, thermal damages. Optimization of the drilling process parameters can reduces the probability of these damages. In the current research, a 3D finite element (FE) model is developed of the process of drilling in the carbon fibre reinforced composite (CFC). The FE model is used to investigate the effects of cutting speed and feed rate on thrust force, torque and delamination in the drilling of carbon fiber reinforced laminated composite. A mesoscale FE model taking into account of the different oriented plies and interfaces has been proposed to predict different damage modes in the plies and delamination. For validation purposes, experimental drilling tests have been performed and compared to the results of the finite element analysis. Using Matlab a digital image analysis code has been developed to assess the delamination factor produced in CFC as a result of drilling. © Springer Science+Business Media B.V. 2011.

Calculation of Critical Clearing Time of Embedded Generation for Relay Setting Based on Optimisation Method

his paper proposes an optimisation-based method to calculate the critical slip (speed) of dynamic stability and critical clearing time (CCT) of a self-excited induction generator (SEIG). A simple case study using the Matlab/Simulink environment has been included to exemplify the optimisation method. Relationships between terminal voltage, critical slip and reactance of transmission line, CCT and inertial constant have been determined, based on which analysis of impact on relaying setting has been further conducted for another simulation case.

Comportamento flessionale di nodi esterni in calcestruzzo fibrorinforzato

SOMMARIO – Si presenta un macro modello di tipo reticolare in grado di riprodurre il comportamento in presenza di taglio e momento di nodi esterni trave-colonna di telai in calcestruzzo fibrorinforzato con fibre di acciaio
uncinato ed ordinario. Il caricamento del sistema è di tipo monotono come nel caso dell’analisi di pushover. Il modello considera la presenza di armature orizzontali e verticali della regione nodale e tiene in conto delle modalità
di rottura legate allo snervamento delle barre e allo schiacciamento delle regioni compresse in regime di sforzi pluriassiali. Il modello include le deformazioni flessionali della trave e della colonna in presenza di sforzo normale costante e restituisce la risposta del sistema colonna-nodo-trave (sub-assembralggio) tramite le curve carico-freccia all’estremità della semitrave. Per i singoli costituenti (trave, colonna e nodo) si è considerata la prima fessurazione, lo snervamento e lo schiacciamento delle regioni compresse e si sono fornite precise indicazioni sulla sequenza degli eventi che come è noto sono di fondamentale importanza per lo sviluppo di un progetto plastico che rispetti la gerarchia delle resistenze. Con l’uso del modello il controllo della gerarchia delle resistenze avviene a livello sezionale (lo snervamento delle barre deve avvenire prima dello schiacciamento delle regioni compresse) o di macro elemento (nella regione nodale lo snervamento delle staffe precede la crisi dei puntoni) e dell’intero elemento
sub-assemblaggio trave debole, colonna forte e nodo sovraresistente.
La risposta ottenuta con i modello proposto è in buon accordo con le risposte sperimentali disponibili in letteratura (almeno in termini di resistenza del sub-assemblaggio). Il modello è stato ulteriormente validato con analisi
numeriche agli elementi finiti condotte con il codice ATENA-2D. Le analisi numeriche sono state condotte utilizzando per il calcestruzzo fibroso adeguate leggi costitutive proposte dagli autori ed in grado di cogliere gli effetti
di softening e di resistenza residua a trazione legati alla presenza di fibre. Ulteriori sviluppi del modello saranno indirizzati a includere gli effetti di sfilamento delle barre d’armatura della trave e del conseguente degrado delle
tensioni d’aderenza per effetto di carichi monotonici e ciclici.

SUMMARY – A softened strut-and-tie macro model able to reproduce the flexural behavior of external beam-tocolumn joints with the presence of horizontal and vertical steel bars, including softening of compressed struts and yielding of main and secondary steel bars, is presented, to be used for the pushover analysis. The model proposed is able to calculate also the flexural response of fibrous reinforced concrete (FRC) beam-to-column sub-assemblages in term of a multilinear load-deflection curves. The model is able to take into account of the tensile behavior of main bars embedded in the surrounding concrete and of the softening of the compressed strut, the arrangement and percentage of the steel bars, the percentage and the geometry of steel fibers. First cracking, yielding of main steel and crushing of concrete were identified to determine the corresponding loads and displacement and to plot the simplified monotonic load-deflection curves of the sub-assemblages subjected in the column to constant vertical
load and at the tip of the beam to monotonically increasing lateral force. Through these load-delfection curves the component (beam, joint and column) that first collapse can be recognized and the capacity design can be verified.
The experimental results available in the literature are compared with the results obtained through the proposed model. Further, a validation of the proposed model is numerically made by using a non linear finite element program (ATENA-2D) able to analyze the flexural behavior of sub-assemblages.

A review of ADP processing software

Data processing is an essential part of Acoustic Doppler Profiler (ADP) surveys, which have become the standard tool in assessing flow characteristics at tidal power development sites. In most cases, further processing beyond the capabilities of the manufacturer provided software tools is required. These additional tasks are often implemented by every user in mathematical toolboxes like MATLAB, Octave or Python. This requires the transfer of the data from one system to another and thus increases the possibility of errors. The application of dedicated tools for visualisation of flow or geographic data is also often beneficial and a wide range of tools are freely available, though again problems arise from the necessity of transferring the data. Furthermore, almost exclusively PCs are supported directly by the ADP manufacturers, whereas small computing solutions like tablet computers, often running Android or Linux operating systems, seem better suited for online monitoring or data acquisition in field conditions. While many manufacturers offer support for developers, any solution is limited to a single device of a single manufacturer. A common data format for all ADP data would allow development of applications and quicker distribution of new post processing methodologies across the industry.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.

Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure. © Springer-Verlag Berlin Heidelberg 2013.

New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs) provide much promise in reducing greenhouse gas emissions and, thus, are a focal point of research and development. Existing on-board charging capacity is effective but requires the use of several power conversion devices and power converters, which reduce reliability and cost efficiency. This paper presents a novel three-phase switched reluctance (SR) motor drive with integrated charging functions (including internal combustion engine and grid charging). The electrical energy flow within the drivetrain is controlled by a power electronic converter with less power switching devices and magnetic devices. It allows the desired energy conversion between the engine generator, the battery, and the SR motor under different operation modes. Battery-charging techniques are developed to operate under both motor-driving mode and standstill-charging mode. During the magnetization mode, the machine's phase windings are energized by the dc-link voltage. The power converter and the machine phase windings are controlled with a three-phase relay to enable the use of the ac-dc rectifier. The power converter can work as a buck-boost-type or a buck-type dc-dc converter for charging the battery. Simulation results in MATLAB/Simulink and experiments on a 3-kW SR motor validate the effectiveness of the proposed technologies, which may have significant economic implications and improve the PHEVs' market acceptance

Split Converter-Fed SRM Drive for Flexible Charging in EV/HEV Applications

Electric vehicles (EVs) and hybrid EVs are the way forward for green transportation and for establishing low-carbon economy. This paper presents a split converter-fed four-phase switched reluctance motor (SRM) drive to realize flexible integrated charging functions (dc and ac sources). The machine is featured with a central-tapped winding node, eight stator slots, and six rotor poles (8/6). In the driving mode, the developed topology has the same characteristics as the traditional asymmetric bridge topology but better fault tolerance. The proposed system supports battery energy balance and on-board dc and ac charging. When connecting with an ac power grid, the proposed topology has a merit of the multilevel converter; the charging current control can be achieved by the improved hysteresis control. The energy flow between the two batteries is balanced by the hysteresis control based on their state-of-charge conditions. Simulation results in MATLAB/Simulink and experiments on a 150-W prototype SRM validate the effectiveness of the proposed technologies, which may provide a solution to EV charging issues associated with significant infrastructure requirements.