Optimal allocation and sizing of DGs in a distribution system using PSO

In this paper, the optimal allocation and sizing of distributed generators (DGs) in a distribution system is studied. To achieve this goal, an optimization problem should be solved in which the main objective is to minimize the DGs cost and to maximise the reliability simultaneously. The active power balance between loads and DGs during the isolation time is used as a constraint. Another point considered in this process is the load shedding. It means that if the summation of DGs active power in a zone, isolated by the sectionalizers because of a fault, is less than the total active power of loads located in that zone, the program start shedding the loads in one-by-one using the priority rule still the active power balance is satisfied. This assumption decreases the reliability index, SAIDI, compared with the case loads in a zone are shed when total DGs power is less than the total load power. To validate the proposed method, a 17-bus distribution system is employed and the results are analysed.

Rhetoric of landscape architecture and interior design discourses : preparation for cross-disciplinary practice

The rhetoric of the pedagogic discourses of landscape architectural students and interior design students is described as part of a doctoral study undertaken to document practices and orientations prior to cross-disciplinary collaboration. We draw on the theoretical framework of Basil Bernstein, an educational sociologist, and the rhetorical method of Kenneth Burke, a literary dramatist, to study the grammars of ‘landscape’ representation employed within these disciplinary examples. We investigate how prepared final year students are for working in a cross-disciplinary manner. The discursive interactions of their work, as illustrated by four examples of drawn images and written text, are described. Our findings suggest that we need to concern ourselves aspects of our pedagogic discourse that brings uniqueness and value to our disciplines ,as well as that shared discourses between disciplines.

A rights management approach to securing data distribution in coalitions

As network capacity has increased over the past decade, individuals and organisations have found it increasingly appealing to make use of remote services in the form of service-oriented architectures and cloud computing services. Data processed by remote services, however, is no longer under the direct control of the individual or organisation that provided the data, leaving data owners at risk of data theft or misuse. This paper describes a model by which data owners can control the distribution and use of their data throughout a dynamic coalition of service providers using digital rights management technology. Our model allows a data owner to establish the trustworthiness of every member of a coalition employed to process data, and to communicate a machine-enforceable usage policy to every such member.

Contribution of cross-functional teams to the improvement in operational performance

Purpose: The purpose of this paper is to explore the role of cross-functional teams in the alignment between system effectiveness and operational effectiveness after the implementation of enterprise information systems (EIS). In addition, it aims to explore the contribution of cross-functional teams to improvement in operational performance. ---------- Design/methodology/approach: The research uses a combination of qualitative and quantitative methods, in a two-stage methodological approach, to investigate the influence of cross-functional teams on the alignment between system effectiveness and operational effectiveness and the impact of the stated alignment on the improvement in operational performance. ---------- Findings: Initial findings suggest that factors stemming from system effectiveness and the performance objectives stemming from operational effectiveness are important and significantly well correlated factors that promote the alignment between the effectiveness of technological implementation and the effectiveness of operations. In addition, confirmatory factor analysis has been used to find the structural relationships and provide explanations for the stated alignment and the contribution of cross-functional teams to the improvement in operational performance. ---------- Research limitations/implications: The principal limitation of this study is its small sample size. ---------- Practical implications: Cross-functional teams have been used by many organisations as a way of involving expertise from different functional areas in the implementation of innovative technologies. An appropriate use of the dimensions that emerged from this research, in the context of cross-functional teams, will assist organisations to properly utilise cross-functional teams with the aim of improving operational performance. ---------- Originality/value: The paper presents a new approach to measure the effectiveness of EIS implementation by adding new dimensions to measure it.

Distribution of polycyclic aromatic hydrocarbons in urban stormwater in Queensland, Australia

This paper reports the distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in wash-off in urban stormwater in Gold Coast, Australia. Runoff samples collected from residential, industrial and commercial sites were separated into a dissolved fraction (<0.45µm), and three particulate fractions (0.45-75µm, 75-150µm and >150µm). Patterns in the distribution of PAHs in the fractions were investigated using Principal Component Analysis. Regardless of the land use and particle size fraction characteristics, the presence of organic carbon plays a dominant role in the distribution of PAHs. The PAHs concentrations were also found to decrease with rainfall duration. Generally, the 1- and 2-year average recurrence interval rainfall events were associated with the majority of the PAHs and the wash-off was a source limiting process. In the context of stormwater quality mitigation, targeting the initial part of the rainfall event is the most effective treatment strategy. The implications of the study results for urban stormwater quality management are also discussed.

Cross talk between physical activity and appetite control: does physical activity stimulate appetite?

Physical activity has the potential to modulate appetite control by improving the sensitivity of the physiological satiety signalling system, by adjusting macronutrient preferences or food choices and by altering the hedonic response to food. There is evidence for all these actions. Concerning the impact of physical activity on energy balance, there exists a belief that physical activity drives up hunger and increases food intake, thereby rendering it futile as a method of weight control.

Deposition of hydroxyapatite and calcium oxalate dihydrate on a heat exchanger tube

Most studies on the characterisation of deposits on heat exchangers have been based on bulk analysis, neglecting the fine structural features and the compositional profiles of layered deposits. Attempts have been made to fully characterise a fouled stainless steel tube obtained from a quintuple Roberts evaporator of a sugar factory using X-ray diffraction and scanning electron microscopy techniques. The deposit contains three layers at the bottom of the tube and two layers on the other sections and is composed of hydroxyapatite, calcium oxalate dihydrate and an amorphous material. The proportions of these phases varied along the tube height. Energy-dispersive spectroscopy and XRD analysis on the surfaces of the outermost and innermost layers showed that hydroxyapatite was the major phase attached to the tube wall, while calcium oxalate dihydrate (with pits and voids) was the major phase on the juice side. Elemental mapping of the cross-sections of the deposit revealed the presence of a mineral, Si-Mg-Al-Fe-O, which is probably a silicate mineral. Reasons for the defects in the oxalate crystal surfaces, the differences in the crystal size distribution from bottom to the top of the tube and the composite fouling process have been postulated.

Distribution of forces during fulcrum bending radiographs on adolescent idiopathic scoliosis patients

At the Mater Children’s Hospital, approximately 80% of patients presenting with Adolescent Idiopathic Scoliosis requiring corrective surgery receive a fulcrum bending radiograph. The fulcrum bending radiograph provides a measurement of spine flexibility and a better indication of achievable surgical correction than lateral-bending radiographs (Cheung and Luk, 1997; Hay et al 2008). The magnitude and distribution of the corrective force exerted by the bolster on the patient’s body is unknown. The objective of this pilot study was to measure, for the first time, the forces transmitted to the patient’s ribs through the bolster during the fulcrum bending radiograph.

Cross-cultural comparison of anxiety symptoms in Colombian and Australian children

Introduction: This cross-cultural study compared both the symptoms of anxiety and their severity in a community sample of children from Colombia and Australia. Method: The sample comprised 516 children (253 Australian children and 263 Colombian children), aged 8 to 12-years-old. The Spence Children’s Anxiety Scale (SCAS) was used to measure both the symptoms and levels of anxiety. Results: The results showed a significant difference in the severity of the symptoms between the children in the two countries. In general, Colombian children reported more severe symptoms than their Australian peers, however there were no difference in the types of symptoms reported by the children in the two countries. Discussion and Conclusion: The implications of these findings and their importance to cross-cultural research are discussed.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

A new method for improving reliability and line loss in distribution networks

In this paper, both Distributed Generators (DG) and capacitors are allocated and sized optimally for improving line loss and reliability. The objective function is composed of the investment cost of DGs and capacitors along with loss and reliability which are converted to the genuine dollar. The bus voltage and line current are considered as constraints which should be satisfied during the optimization procedure. Hybrid Particle Swarm Optimization as a heuristic based technique is used as the optimization method. The IEEE 69-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate that the lowest cost planning is found by optimizing both DGs and capacitors in distribution networks.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.

Fold back current control and admittance protection scheme for a distribution network containing distributed generators

Islanded operation, protection, reclosing and arc extinguishing are some of the challenging issues related to the connection of converter interfaced distributed generators (DGs) into a distribution network. The isolation of upstream faults in grid connected mode and fault detection in islanded mode using overcurrent devices are difficult. In the event of an arc fault, all DGs must be disconnected in order to extinguish the arc. Otherwise, they will continue to feed the fault, thus sustaining the arc. However, the system reliability can be increased by maximising the DG connectivity to the system: therefore, the system protection scheme must ensure that only the faulted segment is removed from the feeder. This is true even in the case of a radial feeder as the DG can be connected at various points along the feeder. In this paper, a new relay scheme is proposed which, along with a novel current control strategy for converter interfaced DGs, can isolate permanent and temporary arc faults. The proposed protection and control scheme can even coordinate with reclosers. The results are validated through PSCAD/EMTDC simulation and MATLAB calculations.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.