1,721,108 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Novel Forecasting and Scheduling for Microgrid Energy Management System
No full textThe high penetration of renewable energy resources brought new challenges to the modern grid; therefore, new solutions and concepts need to be developed. The idea of a microgrid (MG) has been introduced to overcome the upcoming issues in modern grids. MG is a small-scale grid composed of renewable energy resources, energy storage, and load demand. MG makes decisions by itself and can operate in grid-connected or islanded mode depending on functionality. The microgrid energy management system (M-EMS) is the decision-making centre of MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered as the major modules among the four of them. The forecasting module is required in the M-EMS to predict the future power generation and consumption. The forecast data is the input to the scheduling module of M-EMS. Employing forecasting system in the M-EMS would increase the accuracy of the scheduling module. The scheduling module is responsible for controlling the power flow from/to the main grid. Additionally, it performs optimal day-ahead scheduling of available power generation resources to feed the load demand in a grid-connected MG for economical operation. Consequently, this research work presents four contributions in the area of M-EMS for grid-connected MG.
The first contribution of this research is to presents a hybrid strategy for short-term forecasting of load demand in M-EMS, which is a combination of best-basis stationary wavelet packet transform and the Harris hawks algorithm-based feedforward neural network. The Harris hawks algorithm is applied to the feedforward neural network as an alternative learning algorithm to optimized the weights and biases of neurons. The proposed model is applied for load demand prediction of the Queensland electric market and compared with existing competitive models. The simulation results prove the effectiveness of the proposed method.
The second contribution of this research is to design and proposed an ensemble forecasting strategy for solar PV power forecasting. The proposed ensemble strategy is based on a systematic combination of the tunicate swarm algorithm (TSA)-based multilayer perceptron neural network (TSA-MLPNN), TSA based least-square support vector machine (TSA-LSSVM), whales optimization algorithm (WOA) based MLPNN (WOAMLPNN), and WOA based LSSVM (WOA-LSSVM). The output of all the models is combined using the Bayesian model averaging method. The proposed ensemble strategy is validated through simulation of the real-time data of building N-78 Griffith University, Queensland. The simulation results demonstrated that the proposed strategy shows excellent performance in comparison with several existing competitive approaches.
The third contribution of this research is to propose an optimum scheduling strategy, using a weighted salp swarm algorithm for M-EMS, to perform the optimal scheduling of available power resources to meet consumer demand and minimize the operating cost of grid-connected MG. The performance of the proposed scheduling strategy is validated through simulation using MATLAB and compared with standard particle swarm optimization (PSO) based scheduling strategy. The comparison shows that the proposed strategy outperforms the PSO based strategy.
The final contribution of this research is to propose an M-EMS using an ensemble forecasting strategy and grey wolf optimization (GWO). In the proposed M-EMS, an ensemble forecasting strategy is used to accomplish short-term forecasting of PV power and load while the GWO is applied to perform the optimum scheduling of available power resources in grid-connected MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via python programming language to validate the effectiveness of the proposed M-EMS. The experimental results of the proposed M-EMS for the selected case prove the effectiveness of the proposed M-EMS. In summary, several forecasting and scheduling strategies have been proposed and validated for the M-EMS of a grid-connected MG.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Eng & Built EnvScience, Environment, Engineering and TechnologyFull Tex
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dynamic Voltage Stability Assessment and Enhancement of Islanded Microgrid with High Renewable Energy Penetration
No full textRenewable energy has become the primary electrical power source in the last decade because of its capability to produce clean, inexhaustible, and increasingly competitive energy. Most importantly, they produce neither greenhouse gases, which cause climate change, nor polluting emissions. Additionally, their potential to use anywhere globally and falling costs have made renewable energy more attractive. Among various renewable energy sources (RESs), the growth of solar photovoltaic (PV) has been elevated due to the significant cost reduction, diverse application, low maintenance cost, and distributed nature as it can be installed close to the load centre. Solar PV technology has been drastically improved over the last few years and complemented by battery energy storage systems (BESSs), turning solar into a significantly more efficient source of clean energy. Hence, to facilitate the isolated areas, such as oceanic islands, remote villages, and military operations, with adequate electricity supply, the development and implementation of RESs, particularly solar PV dominated islanded microgrid (MG), has been drawing huge attention. The stability of MGs is crucial for reliable operation and maintaining the quality of power supply. However, due to distinct and inherent characteristics of islanded MGs such as size, feeder types, a high share of RESs, converter-interfaced components, low inertia and poor short-circuit power cause particular stability issues and complexities in the secure operation compared to conventional power systems. Among various stability issues, securing dynamic voltage stability (DVS) of RES dominated MGs following faults is a significant challenge owing to the low short circuit current contribution and poor ride through capability of inverter-based sources. Additionally, a high proportion of induction motor (IM) loads have made the MGs more vulnerable to short-term dynamic voltage instability. Acknowledging these issues, this research is aimed to assess the DVS of RES dominated islanded MGs and propose countermeasures to mitigate a probable dynamic voltage instability. Firstly, rigorous assessment of DVS of RES dominated islanded MGs with a high proportion of induction motor load is conducted. The impacts of various proportions of IM loads as well as different combinations of generation systems on the DVS of islanded MG are investigated. Note that various network service providers have updated their grid codes to integrate the low-voltage ride-through (LVRT) with reactive power support capability within the inverters. Therefore, the DVS of islanded MG is examined with conventional reactive power support. Finally, a new optimised reactive power support strategy is proposed to enhance the DVS of MGs.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Eng & Built EnvScience, Environment, Engineering and TechnologyFull Tex
Networked Model Predictive Control for Microgrids with Distributed PV Generators
No full textMore and more renewable energy sources are being integrated into microgrids—and while this causes many control challenges for microgrids, it can also yield numerous economic and environmental benefits. Therefore, it is necessary to develop proper control schemes for microgrids to address the different control issues in their hierarchical structure while adapting to the different time scales of the three control levels. Conversely, because model predictive control (MPC) has significant advantages—the inclusion of forecasts, the simplicity of the algorithm, and the flexibility to handle hard constraints—it has attracted significant attention in industrial control systems. Motivated by these factors, this research focuses on implementing MPC techniques in microgrids, which are solely supplied by photovoltaic (PV) generators, to address different control problems.
For primary control of the microgrid hierarchy, which is mainly responsible for the inner control of the local distributed generation units, MPC can be applied to control of the power converters that serve as interfaces between the sources and the loads. Therefore, in this control level, a novel output-feedback MPC technique based on ellipsoidal set-membership state estimation is designed for a direct current to direct current (DC-DC) converter, considering the unknown-but-bounded external disturbances. A long-horizon finite-states (FS) MPC strategy is designed for the direct current to alternating current (DC-AC) inverter to reduce the sampling and switching frequency through a multi-step implementation approach and a control sequence rearrangement method.
For secondary control, which is in charge of the compensation for the frequency and voltage deviations and is usually communication-based, the distributed MPC strategy can be used to realize the desired cooperative control among the geographically dispersed units. Thus, a novel distributed model predictive controller is developed to enhance system performance.
It takes into account the fact that the distributed controllers’ communication network might be subject to switching topology due to the disconnection and reconnection of controllers, random failures, and recoveries of the links between controllers. A Markov chain with a time-varying probability transition matrix is used to describe the stochastic topology evolution of the control network.
Tertiary control is used to coordinate the power flow between the microgrid and the utility grid and offers economic operations for microgrids. Since the integration of renewable energy sources causes low inertia and power fluctuation in microgrids, battery energy storage is essential to addressing these issues. To coordinate the charging/discharging schedule of the battery storage units, a networked MPC strategy can be adopted to realize the communication between different microgrid components and make use of the forecasts for PV power generation and load demand. The multi-microgrid system is considered subject to partial fault because of non-functional generators, batteries, or even transmission lines in this research. Hence, both the connection status of the electrical grid and the communication network are incorporated into the system modeling. In addition, the set-membership estimation is adopted to deal with the possible state unavailability caused by non-functional batteries or communication failures.
In the theoretical section of this thesis, different sufficient conditions are established to ensure the stability of the investigated systems, and the optimal control inputs are obtained by solving the corresponding optimization problems. For easy implementation with MATLAB solvers, all the constraints and conditions of the optimization problems are transformed into linear matrix inequalities. Different recursive MPC algorithms are designed to control the target systems, and some extended algorithms are also developed to assist with the computation to determine the optimal solutions. In the demonstration section of this thesis, the designed controllers are all implemented in the numerical simulations or Simulink tests to verify their effectiveness, and an experimental test based on Raspberry Pi is conducted to demonstrate the wireless communication employing the designed networked model predictive controller.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Eng & Built EnvScience, Environment, Engineering and TechnologyFull Tex
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Controlling a Blood-Pump for Dilating a Vein
No full textFor patients requiring haemodialysis, clinical practice guidelines strongly recommend the arteriovenous fistula (AVF) as the preferred vascular access. However, there are instances when fistula creation is not possible for the vein, or the fistula does not mature. The Artio Medical Inc. Amplifi™ vein Dilation System is being developed for use prior to AVF creation to enhance the suitability of the vein for fistula creation and raise its probability of maturation. The Amplifi™ is a wearable motor-pump that extracts non-pulsatile blood from the right atrium and achieves vein dilation through a controlled increase in blood flow rates and thus in shear stress at the vessel’s wall. These higher flow rates must be within a range to produce approximately 4 pascals of shear stress on the vessel’s wall to achieve the desired dilatation and wall thickness to promote fistula development. The aims of this project were: 1) to estimate wall shear stress (WSS) during vein dilation (for the “External” control algorithm) and 2) to optimise the Amplifi™ System’s controller (the “Internal” control algorithm) for minimum vibration and maximum power efficiency. Apart from the vibration and power consumption aims, additional optimisation goals were to implement an increased speed range and ramped speed function in flow (from 0.1 L/min to 1 L/min) over a 14-day period. […]Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Eng & Built EnvScience, Environment, Engineering and TechnologyFull Tex
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