23,070 research outputs found
European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0
The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag
Advanced Testing Chain Supporting the Validation of Smart Grid Systems and Technologies
New testing and development procedures and methods are needed to address
topics like power system stability, operation and control in the context of
grid integration of rapidly developing smart grid technologies. In this
context, individual testing of units and components has to be reconsidered and
appropriate testing procedures and methods need to be described and
implemented. This paper addresses these needs by proposing a holistic and
enhanced testing methodology that integrates simulation/software- and
hardware-based testing infrastructure. This approach presents the advantage of
a testing environment, which is very close to f i eld testing, includes the
grid dynamic behavior feedback and is risks-free for the power system, for the
equipment under test and for the personnel executing the tests. Furthermore,
this paper gives an overview of successful implementation of the proposed
testing approach within different testing infrastructure available at the
premises of different research institutes in Europe.Comment: 2018 IEEE Workshop on Complexity in Engineering (COMPENG
The Ultralight project: the network as an integrated and managed resource for data-intensive science
Looks at the UltraLight project which treats the network interconnecting globally distributed data sets as a dynamic, configurable, and closely monitored resource to construct a next-generation system that can meet the high-energy physics community's data-processing, distribution, access, and analysis needs
Advanced Message Routing for Scalable Distributed Simulations
The Joint Forces Command (JFCOM) Experimentation Directorate (J9)'s recent Joint Urban Operations (JUO)
experiments have demonstrated the viability of Forces Modeling and Simulation in a distributed environment. The
JSAF application suite, combined with the RTI-s communications system, provides the ability to run distributed
simulations with sites located across the United States, from Norfolk, Virginia to Maui, Hawaii. Interest-aware
routers are essential for communications in the large, distributed environments, and the current RTI-s framework
provides such routers connected in a straightforward tree topology. This approach is successful for small to medium
sized simulations, but faces a number of significant limitations for very large simulations over high-latency, wide
area networks. In particular, traffic is forced through a single site, drastically increasing distances messages must
travel to sites not near the top of the tree. Aggregate bandwidth is limited to the bandwidth of the site hosting the
top router, and failures in the upper levels of the router tree can result in widespread communications losses
throughout the system.
To resolve these issues, this work extends the RTI-s software router infrastructure to accommodate more
sophisticated, general router topologies, including both the existing tree framework and a new generalization of the
fully connected mesh topologies used in the SF Express ModSAF simulations of 100K fully interacting vehicles.
The new software router objects incorporate the scalable features of the SF Express design, while optionally using
low-level RTI-s objects to perform actual site-to-site communications. The (substantial) limitations of the original
mesh router formalism have been eliminated, allowing fully dynamic operations. The mesh topology capabilities
allow aggregate bandwidth and site-to-site latencies to match actual network performance. The heavy resource load at
the root node can now be distributed across routers at the participating sites
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
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