Estimating the impact of reduced inertia on frequency stability due to large-scale wind penetration in Australian electricity network

Large-scale integration of non-inertial generators such as wind farms will create frequency stability issues due to reduced system inertia. Inertia based frequency stability study is important to predict the performance of power system with increased level of renewables. This paper focuses on the impact large-scale wind penetration on frequency stability of the Australian Power Network. MATLAB simulink is used to develop a frequency based dynamic model utilizing the network data from a simplified 14-generator Australian power system. The loss of generation is modeled as the active power disturbance and minimum inertia required to maintain the frequency stability is determined for five-area power system.

Doubly Fed Induction Generators : Overview and Intelligent Control Strategies for Wind Energy Conversion Systems

Adjustable speed induction generators, especially the Doubly-Fed Induction Generators (DFIG) are becoming increasingly popular due to its various advantages over fixed speed generator systems. A DFIG in a wind turbine has ability to generate maximum power with varying rotational speed, ability to control active and reactive by integration of electronic power converters such as the back-to-back converter, low rotor power rating resulting in low cost converter components, etc, DFIG have become very popular in large wind power conversion systems. This chapter presents an extensive literature survey over the past 25 years on the different aspects of DFIG. Application of H8 Controller for enhanced DFIG-WT performance in terms of robust stability and reference tracking to reduce mechanical stress and vibrations is also demonstrated in the chapter.

Sectional analysis of stormwater treatment performance of a constructed wetland

Constructed wetlands are among the most common Water Sensitive Urban Design (WSUD) measures for stormwater treatment. These systems have been extensively studied to understand their performance and influential treatment processes. Unfortunately, most past studies have been undertaken considering a wetland system as a lumped system with a primary focus on the reduction of the event mean concentration (EMC) values of specific pollutant species or total pollutant load removal. This research study adopted an innovative approach by partitioning the inflow runoff hydrograph and then investigating treatment performance in each partition and their relationships with a range of hydraulic factors. The study outcomes confirmed that influenced by rainfall characteristics, the constructed wetland displays different treatment characteristics for the initial and later sectors of the runoff hydrograph. The treatment of small rainfall events (<15 mm) is comparatively better at the beginning of runoff events while the trends in pollutant load reductions for large rainfall events (>15 mm) are generally lower at the beginning and gradually increase towards the end of rainfall events. This highlights the importance of ensuring that the inflow into a constructed wetland has low turbulence in order to achieve consistent treatment performance for both, small and large rainfall events.

Model refinement through high-performance computing: an agent-based HIV example

Background Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights.

Performance evaluation of cloud-based parallel computing

As computational models in fields such as medicine and engineering get more refined, resource requirements are increased. In a first instance, these needs have been satisfied using parallel computing and HPC clusters. However, such systems are often costly and lack flexibility. HPC users are therefore tempted to move to elastic HPC using cloud services. One difficulty in making this transition is that HPC and cloud systems are different, and performance may vary. The purpose of this study is to evaluate cloud services as a means to minimise both cost and computation time for large-scale simulations, and to identify which system properties have the most significant impact on performance. Our simulation results show that, while the performance of Virtual CPU (VCPU) is satisfactory, network throughput may lead to difficulties.

Automotive modal lock-in: The role of path dependence and large socio-economic regimes in market failure

This paper addresses less recognised factors which influence the diffusion of a particular technology. While an innovation’s attributes and performance are paramount, many fail because of external factors which favour an alternative. This paper, with theoretic input from diffusion, lock-in and path-dependency, presents a qualitative study of external factors that influenced the evolution of transportation in USA. This historical account reveals how one technology and its emergent systems become dominant while other choices are overridden by socio-political, economic and technological interests which include not just the manufacturing and service industries associated with the automobile but also government and market stakeholders. Termed here as a large socio-economic regime (LSER),its power in ensuring lock-in and continued path-dependency is shown to pass through three stages, weakening eventually as awareness improves. The study extends to transport trends in China, Korea, Indonesia and Malaysia and they all show the dominant role of an LSER. As transportation policy is increasingly accountable to address both demand and environmental concerns and innovators search for solutions, this paper presents important knowledge for innovators, marketers and policy makers for commercial and societal reasons, especially when negative externalities associated with an incumbent transportation technology may lead to market failure.

Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens-to-hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma, and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and 7 control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to sub-nanogram/mL sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and inter-laboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy isotope labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an inter-laboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality c`ontrol measures, enables sensitive, specific, reproducible and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Determining reverse salient types and evolutionary dynamics of technology systems with performance disparities

Technological system evolution is marked by the uneven evolution of constituent sub-systems. Subsequently, system evolution is hampered by the resulting state of unevenness, or reverse salience, which results from the presence of the sub-system that delivers the lowest level of performance with respect to other sub-systems, namely, the reverse salient. In this paper, we develop absolute and proportional performance gap measures of reverse salience and, in turn, derive a typology of reverse salients that distinguishes alternative dynamics of change in the evolving system. We subsequently demonstrate the applicability of the measures and the typology through an illustrative empirical study of the PC (personal computer) technological system that functions as a gaming platform. Our empirical analysis demonstrates that patterns of temporal dynamics can be distinguished with the measurement of reverse salience, and that distinct paths of technological system evolution can be identified as different types of reverse salients emerge over time.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

Application of simulated annealing to data distribution for all-to-all comparison problems in homogeneous systems

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop's data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions.

Distributed computing of all-to-all comparison problems in heterogeneous systems

The requirement of distributed computing of all-to-all comparison (ATAC) problems in heterogeneous systems is increasingly important in various domains. Though Hadoop-based solutions are widely used, they are inefficient for the ATAC pattern, which is fundamentally different from the MapReduce pattern for which Hadoop is designed. They exhibit poor data locality and unbalanced allocation of comparison tasks, particularly in heterogeneous systems. The results in massive data movement at runtime and ineffective utilization of computing resources, affecting the overall computing performance significantly. To address these problems, a scalable and efficient data and task distribution strategy is presented in this paper for processing large-scale ATAC problems in heterogeneous systems. It not only saves storage space but also achieves load balancing and good data locality for all comparison tasks. Experiments of bioinformatics examples show that about 89\% of the ideal performance capacity of the multiple machines have be achieved through using the approach presented in this paper.

Network security metrics and performance for healthcare systems management

While enhanced cybersecurity options, mainly based around cryptographic functions, are needed overall speed and performance of a healthcare network may take priority in many circumstances. As such the overall security and performance metrics of those cryptographic functions in their embedded context needs to be understood. Understanding those metrics has been the main aim of this research activity. This research reports on an implementation of one network security technology, Internet Protocol Security (IPSec), to assess security performance. This research simulates sensitive healthcare information being transferred over networks, and then measures data delivery times with selected security parameters for various communication scenarios on Linux-based and Windows-based systems. Based on our test results, this research has revealed a number of network security metrics that need to be considered when designing and managing network security for healthcare-specific or non-healthcare-specific systems from security, performance and manageability perspectives. This research proposes practical recommendations based on the test results for the effective selection of network security controls to achieve an appropriate balance between network security and performance

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.