Violeta Holmes

Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure

This paper presents a design and development of a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods. The approach has been validated using an experimental database of environment and electrical parameters from a 1.98 kip plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this research is, therefore, to devise a Virtual Instrument capable of simulating theoretical performances of PV systems and deploying statistical analysis of PV real-time data. The fault detection is based on the comparison between measured and theoretical output power using t-test statistical analysis. The obtained results indicate that the proposed method can detect the faults of the grid-connected PV system, and can be used for continuous monitoring of PV system status.

Orchestrating Docker Containers in the HPC Environment

Linux container technology has more than proved itself useful in cloud computing as a lightweight alternative to virtualisation, whilst still offering good enough resource isolation. Docker is emerging as a popular runtime for managing Linux containers, providing both management tools and a simple file format. Research into the performance of containers compared to traditional Virtual Machines and bare metal shows that containers can achieve near native speeds in processing, memory and network throughput. A technology born in the cloud, it is making inroads into scientific computing both as a format for sharing experimental applications and as a paradigm for cloud based execution. However, it has unexplored uses in traditional cluster and grid computing. It provides a run time environment in which there is an opportunity for typical cluster and parallel applications to execute at native speeds, whilst being bundled with their own specific (or legacy) library versions and support software. This offers a solution to the Achilles heel of cluster and grid computing that requires the user to hold intimate knowledge of the local software infrastructure. Using Docker brings us a step closer to more effective job and resource management within the cluster by providing both a common definition format and a repeatable execution environment. In this paper we present the results of our work in deploying Docker containers in the cluster environment and an evaluation of its suitability as a runtime for high performance parallel execution. Our findings suggest that containers can be used to tailor the run time environment for an MPI application without compromising performance, and would provide better Quality of Service for users of scientific computing.

Agent-Mediated Information Exchange: Child Safety Online

This paper presents a tool for agent-mediated information exchange between children while chatting online. The Internet plays a significant role in the lives of children today by opening up a whole new world It provides excellent educational opportunities, access to a huge range of information and can be fun. However, it can also be used as a tool to enable abuse of children in a variety of ways. There is the potential for pedophiles to misuse modern technology to gain a childs trust by attempting to contact them through chat rooms and pretending to be a child themselves. Hence, there is a need to automate the process of monitoring information exchange when children chat on-line. An agent mediated autonomous system has been developed that is able to automatically block the transmission of personal data, such as addresses and telephone numbers to other users, if such data is detected in a message. It is proposed that this work is further developed to detect and prevent attempts by users to arrange meetings with other users. The overall aim of the system is to make it safer for children to chat online.

The ELIHE High-Performance Cluster

Summary form only given. In this poster, we present our experience in implementing a high performance computing cluster for teaching parallel computing theory and development of parallel applications. In teaching parallel and high performance computing, there is often a gap between potential performance taught in the lectures and those practically experienced in exercises in the laboratory. The development of the ELIHE cluster provides us with an opportunity take a hands on approach in teaching programming environments, tools, and libraries for development of parallel applications, parallel computation, architectures, message passing and shared memory paradigms using MPI and OpenMP, etc, at both undergraduate and graduate level. The ELIHE HP cluster consists of 9 computational nodes and a master node. All the nodes in the cluster are commodity systems - PCs, running commodity software - Linux, and CLIC Mandrake. Creating the ELIHE cluster has fulfilled two important goals: to design and implement a HP cluster for teaching parallel computing architectures in the School of Science and Technology, and to promote the use of high performance computer technology for research to faculty members and students

Fault detection algorithm for multiple GCPV array configurations

In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations.

Comparison of evolutionary algorithms for LPDA antenna optimization

A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.

Developing High Performance Computing Resources for Teaching Cluster and Grid Computing Courses

Abstract High-Performance Computing (HPC) and the ability to process large amounts of data are of paramount importance for UK business and economy as outlined by Rt Hon David Willetts MP at the HPC and Big Data conference in February 2014. However there is a shortage of skills and available training in HPC to prepare and expand the workforce for the HPC and Big Data research and development. Currently, HPC skills are acquired mainly by students and staff taking part in HPC-related research projects, MSc courses, and at the dedicated training centres such as Edinburgh Universitys EPCC. There are few UK universities teaching the HPC, Clusters and Grid Computing courses at the undergraduate level. To address the issue of skills shortages in the HPC it is essential to provide teaching and training as part of both postgraduate and undergraduate courses. The design and development of such courses is challenging since the technologies and software in the fields of large scale distributed systems such as Cluster, Cloud and Grid computing are undergoing continuous change. The students completing the HPC courses should be proficient in these evolving technologies and equipped with practical and theoretical skills for future jobs in this fast developing area. In this paper we present our experience in developing the HPC, Cluster and Grid modules including a review of existing HPC courses offered at the UK universities. The topics covered in the modules are described, as well as the coursework project based on practical laboratory work. We conclude with an evaluation based on our experience over the last ten years in developing and delivering the HPC modules on the undergraduate courses, with suggestions for future work.

CDES: An Approach to HPC Workload Modelling

Computational science and complex system administration relies on being able to model user interactions. When it comes to managing HPC, HTC and grid systems user workloads - their job submission behaviour, is an important metric when designing systems or scheduling algorithms. Most simulators are either inflexible or tied in to proprietary scheduling systems. For system administrators being able to model how a scheduling algorithm behaves or how modifying system configurations can affect the job completion rates is critical. Within computer science research many algorithms are presented with no real description or verification of behaviour. In this paper we are presenting the Cluster Discrete Event Simulator (CDES) as an strong candidate for HPC workload simulation. Built around an open framework, CDES can take system definitions, multi-platform real usage logs and can be interfaced with any scheduling algorithm through the use of an API. CDES has been tested against 3 years of usage logs from a production level HPC system and verified to a greater than 95% accuracy.

Hybrid Computer Cluster with High Flexibility

In this paper we present a cluster middleware, designed to implement a Linux-Windows Hybrid HPC Cluster, which not only holds the characteristics of both operating system, but also accepts and schedules jobs in both environments. Beowulf Clusters have become an economical and practical choice for small-and-medium-sized institutions to provide High Performance Computing (HPC)resources. The HPC resources are required for running simulations, image rendering and other calculations, and to support the software requiring a specific operating system. To support the software, small-scale computer clusters would have to be divided in two or more clusters if they are to run on a single operating system. The x86 virtualisation technology would help running multiple operating systems on one computer, but only with the latest hardware which many legacy Beowulf clusters do not have. To aid the institutions, who rely on legacy non-virtualisation-supported facilities rather than high-end HPC resources, we have developed and deployed a bi-stable hybrid system built around Linux CentOS 5.5 with the improved OSCAR middleware, and Windows Server 2008 and Windows HPC 2008 R2. This hybrid cluster is utilised as part of the University of Hudders field campus grid.

An Algorithmic Approach for Signal Measurement Using Symbolic Dynamics of Tent Map

The symbolic time series generated by a unimodal chaotic map starting from any initial condition creates a binary sequence that contains information about the initial condition. A binary sequence of a given length generated this way has a one-to-one correspondence with a given range of the input signal. This can be used to construct analogue to digital converters (ADC). However, in actual circuit realizations, component imperfections and ambient noise result in deviations in the map function from the ideal, which, in turn, can cause significant error in signal measurement. In this paper, we propose the ways of circumventing these problems through an algorithmic procedure that takes into account the non-idealities. The most common form of non-ideality—reduction in the height of the map function—alters the partitions that correspond to each symbolic sequence. We show that it is possible to define the partitions correctly if the height of the map function is known. We also propose a method to estimate this height from the symbolic sequence obtained. We demonstrate the efficacy of the proposed algorithm with simulation as well as experiment. With this development, practical ADCs utilizing chaotic dynamics may become reality.