12,185 research outputs found
An Integrated Approach for Failure Mitigation & Localization in Power Systems
The transmission grid is often comprised of several control areas that are
connected by multiple tie lines in a mesh structure for reliability. It is also
well-known that line failures can propagate non-locally and redundancy can
exacerbate cascading. In this paper, we propose an integrated approach to grid
reliability that (i) judiciously switches off a small number of tie lines so
that the control areas are connected in a tree structure; and (ii) leverages a
unified frequency control paradigm to provide congestion management in real
time. Even though the proposed topology reduces redundancy, the integration of
tree structure at regional level and real-time congestion management can
provide stronger guarantees on failure localization and mitigation. We
illustrate our approach on the IEEE 39-bus network and evaluate its performance
on the IEEE 118-bus, 179-bus, 200-bus and 240-bus networks with various network
congestion conditions. Simulations show that, compared with the traditional
approach, our approach not only prevents load shedding in more failure
scenarios, but also incurs smaller amounts of load loss in scenarios where load
shedding is inevitable. Moreover, generators under our approach adjust their
operations more actively and efficiently in a local manner.Comment: Accepted to the 21st Power Systems Computation Conference (PSCC 2020
An integrated approach for failure mitigation & localization in power systems
The transmission grid is often comprised of several control areas that are connected by multiple tie lines in a mesh structure for reliability. It is also well-known that line failures can propagate non-locally and redundancy can exacerbate cascading. In this paper, we propose an integrated approach to grid reliability that (i) judiciously switches off a small number of tie lines so that the control areas are connected in a tree structure; and (ii) leverages a unified frequency control paradigm to provide congestion management in real time. Even though the proposed topology reduces redundancy, the integration of tree structure at regional level and real-time congestion management can provide stronger guarantees on failure localization and mitigation. We illustrate our approach on the IEEE 39-bus network and evaluate its performance on the IEEE 118-bus, 179-bus, 200-bus and 240-bus networks with various network congestion conditions. Simulations show that, compared with the traditional approach, our approach not only prevents load shedding in more failure scenarios, but also incurs smaller amounts of load loss in scenarios where load shedding is inevitable. Moreover, generators under our approach adjust their operations more actively and efficiently in a local manner
An integrated approach for failure mitigation & localization in power systems
The transmission grid is often comprised of several control areas that are connected by multiple tie lines in a mesh structure for reliability. It is also well-known that line failures can propagate non-locally and redundancy can exacerbate cascading. In this paper, we propose an integrated approach to grid reliability that (i) judiciously switches off a small number of tie lines so that the control areas are connected in a tree structure; and (ii) leverages a unified frequency control paradigm to provide congestion management in real time. Even though the proposed topology reduces redundancy, the integration of tree structure at regional level and real-time congestion management can provide stronger guarantees on failure localization and mitigation. We illustrate our approach on the IEEE 39-bus network and evaluate its performance on the IEEE 118-bus, 179-bus, 200-bus and 240-bus networks with various network congestion conditions. Simulations show that, compared with the traditional approach, our approach not only prevents load shedding in more failure scenarios, but also incurs smaller amounts of load loss in scenarios where load shedding is inevitable. Moreover, generators under our approach adjust their operations more actively and efficiently in a local manner
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Risk analysis in manufacturing footprint decisions
A key aspect in the manufacturing footprint analysis is the risk and sensitivity analysis of critical parameters. In order to contribute to efficient industrial methods and tools for making well-founded strategic decisions regarding manufacturing footprint this paper aims to describe the main risks that need to be considered while locating manufacturing activities, and what risk mitigation techniques and strategies that are proper in order to deal with these risks. It is also proposed how the risk analysis should be included in the manufacturing location decision process
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