Overhead electricity line connector tester

Assessment of the condition of connectors in the overhead electricity network has traditionally relied on the heat dissipation or voltage drop from existing load current (50Hz) as a measurable parameter to differentiate between satisfactory and failing connectors. This research has developed a technique which does not rely on the 50Hz current and a prototype connector tester has been developed. In this system a high frequency signal is injected into the section of line under test and measures the resistive voltage drop and the current at the test frequency to yield the resistance in micro-ohms. From the value of resistance a decision as to whether a connector is satisfactory or approaching failure can be made. Determining the resistive voltage drop in the presence of a large induced voltage was achieved by the innovative approach of using a representative sample of the magnetic flux producing the induced voltage as the phase angle reference for the signal processing rather than the phase angle of the current, which can be affected by the presence of nearby metal objects. Laboratory evaluation of the connector tester has validated the measurement technique. The magnitude of the load current (50Hz) has minimal effect on the measurement accuracy. Addition of a suitable battery based power supply system and isolated communications, probably radio and refinement of the printed circuit board design and software are the remaining development steps to a production instrument.

Modeling, stability analysis and control of microgrid

With the increase in the level of global warming, renewable energy based distributed generators (DGs) will increasingly play a dominant role in electricity production. Distributed generation based on solar energy (photovoltaic and solar thermal), wind, biomass, mini-hydro along with use of fuel cells and micro turbines will gain considerable momentum in the near future. A microgrid consists of clusters of load and distributed generators that operate as a single controllable system. The interconnection of the DG to the utility/grid through power electronic converters has raised concern about safe operation and protection of the equipments. Many innovative control techniques have been used for enhancing the stability of microgrid as for proper load sharing. The most common method is the use of droop characteristics for decentralized load sharing. Parallel converters have been controlled to deliver desired real power (and reactive power) to the system. Local signals are used as feedback to control converters, since in a real system, the distance between the converters may make the inter-communication impractical. The real and reactive power sharing can be achieved by controlling two independent quantities, frequency and fundamental voltage magnitude. In this thesis, an angle droop controller is proposed to share power amongst converter interfaced DGs in a microgrid. As the angle of the output voltage can be changed instantaneously in a voltage source converter (VSC), controlling the angle to control the real power is always beneficial for quick attainment of steady state. Thus in converter based DGs, load sharing can be performed by drooping the converter output voltage magnitude and its angle instead of frequency. The angle control results in much lesser frequency variation compared to that with frequency droop. An enhanced frequency droop controller is proposed for better dynamic response and smooth transition between grid connected and islanded modes of operation. A modular controller structure with modified control loop is proposed for better load sharing between the parallel connected converters in a distributed generation system. Moreover, a method for smooth transition between grid connected and islanded modes is proposed. Power quality enhanced operation of a microgrid in presence of unbalanced and non-linear loads is also addressed in which the DGs act as compensators. The compensator can perform load balancing, harmonic compensation and reactive power control while supplying real power to the grid A frequency and voltage isolation technique between microgrid and utility is proposed by using a back-to-back converter. As utility and microgrid are totally isolated, the voltage or frequency fluctuations in the utility side do not affect the microgrid loads and vice versa. Another advantage of this scheme is that a bidirectional regulated power flow can be achieved by the back-to-back converter structure. For accurate load sharing, the droop gains have to be high, which has the potential of making the system unstable. Therefore the choice of droop gains is often a tradeoff between power sharing and stability. To improve this situation, a supplementary droop controller is proposed. A small signal model of the system is developed, based on which the parameters of the supplementary controller are designed. Two methods are proposed for load sharing in an autonomous microgrid in rural network with high R/X ratio lines. The first method proposes power sharing without any communication between the DGs. The feedback quantities and the gain matrixes are transformed with a transformation matrix based on the line R/X ratio. The second method involves minimal communication among the DGs. The converter output voltage angle reference is modified based on the active and reactive power flow in the line connected at point of common coupling (PCC). It is shown that a more economical and proper power sharing solution is possible with the web based communication of the power flow quantities. All the proposed methods are verified through PSCAD simulations. The converters are modeled with IGBT switches and anti parallel diodes with associated snubber circuits. All the rotating machines are modeled in detail including their dynamics.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Development of a composite line source emission model for traffic interrupted microenvironments and its application in particle number emissions at a bus station

A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

Sustainable urban infrastructure development in south east Asia : evidence from Hong Kong, Kuala Lumpur and Singapore

Many South East Asian cities have experienced substantial physical, economic and social transformations during the past several decades. The rapid pace of globalisation and economic restructuring has resulted in these cities receiving the full impact of urbanisation pressures. In an attempt to ease these pressures, cities such as Singapore, Hong Kong and Kuala Lumpur have advocated growth management approaches focussing especially on urban infrastructure sustainability. These approaches aim to achieve triple bottom line sustainability by balancing economic and social development, and environmental protection. This chapter evaluates three Asia-Pacific city cases, Singapore, Hong Kong and Kuala Lumpur, and assesses their experiences in managing their urban forms and infrastructure whilst promoting sustainable patterns of urban development.

Empathy, neutrality and emotional intelligence: a balancing act for the emotional Einstein

Emotions play a central role in mediation as they help to define the scope and direction of a conflict. When a party to mediation expresses (and hence entrusts) their emotions to those present in a mediation, a mediator must do more than simply listen - they must attend to these emotions. Mediator empathy is an essential skill for communicating to a party that their feelings have been heard and understood, but it can lead mediators into trouble. Whilst there might exist a theoretical divide between the notions of empathy and sympathy, the very best characteristics of mediators (caring and compassionate nature) may see empathy and sympathy merge - resulting in challenges to mediator neutrality. This article first outlines the semantic difference between empathy and sympathy and the role that intrapsychic conflict can play in the convergence of these behavioural phenomena. It then defines emotional intelligence in the context of a mediation, suggesting that only the most emotionally intelligent mediators are able to emotionally connect with the parties, but maintain an impression of impartiality – the quality of remaining ‘attached yet detached’ to the process. It is argued that these emotionally intelligent mediators have the common qualities of strong self-awareness and emotional self-regulation.

Primary students' success on the structured number line

Number lines are part of our everyday life (e.g., thermometers, kitchen scales) and are frequently used in primary mathematics as instructional aids, in texts and for assessment purposes on mathematics tests. There are two major types of number lines; structured number lines, which are the focus of this paper, and empty number lines. Structured number lines represent mathematical information by the placement of marks on a horizontal or vertical line which has been marked into proportional segments (Figure 1). Empty number lines are blank lines which students can use for calculations (Figure 2) and are not discussed further here (see van den Heuvel-Panhuizen, 2008, on the role of empty number lines). In this article, we will focus on how students’ knowledge of the structured number line develops and how they become successful users of this mathematical tool.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Inheritance of Resistance to Rice Tungro Spherical Virus in a Near-Isogenic Line Derived from Utri Merah and in Rice Cultivar TKM6

Resistance to rice virus diseases is an important requirement in many Southeast Asian rice breeding programs. Inheritance of resistance to rice tungro spherical virus (RTSV) in TW5, a near-isogenic line derived from Indonesian rice cultivar Utri Merah, was compared to that in TKM6, an Indian rice cultivar. Both TKM6 and Utri Merah are cultivars resistant to RTSV infections. Crosses were made between TKM6 and TN1, a susceptible cultivar, and between TW5 and TN1, and F3 lines were evaluated for their resistance to RTSV using two RTSV inoculum sources and a serological assay (ELISA). In TKM6, the resistance to the mixture of RTSV-V + RTBV inoculum source was controlled by a single recessive gene, whereas in TW5, the resistance was controlled by two recessive genes. A single recessive gene, however, controlled the resistance in TW5 when another RTSV variant, RTSV-VI, was used, suggesting that the resistance in TW5 depends on the nature of the RTSV inoculum used. RT-PCR, sequence, and phylogenetic analyses confirmed that RTSV-VI inoculum differs from RTSV-V inoculum and accurate phenotyping of the resistance to RTSV requires the use of a genetic marker.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.