A survey on smart grid potential applications and communication requirements

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424hb2016Electrical, Electronic and Computer Engineerin

Smart grid technologies : communication technologies and standards

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=942

A Hybrid Network Processor with Support for High-Speed Execution of CPN and Upper Layer IP Protocols

The trend in network processing is towards layer 4-7 processing in the routers and switches. In this paper, we present a hybrid network processor (NPU) architecture that supports high-speed execution of higher layer TCP/IP protocol processing. The architecture achieves this by employing optimized logic blocks for specific tasks in the processing elements. This is a unique architecture such that it can realize both task-level and packet-level parallelism. We also present an example of implementation of a fast adaptive routing algorithm, Cognitive Packet Networks (CPNs), using the proposed NPU architecture. CPN uses a neural network model with a reinforcement learning algorithm to find routes. The applicability of the CPN concept has been demonstrated through several software implementations. Through hardware implementation, we show that this new network model can sustain similar line rates as the current IP protocols. 1

A Hybrid Network Processor With Support For High-Speed Execution Of Cpn And Upper Layer Ip Protocols

The trend in network processing is towards layer 4-7 processing in the routers and switches. In this paper, we present a hybrid network processor (NPU) architecture that supports high-speed execution of higher layer TCP/IP protocol processing. The architecture achieves this by employing optimized logic blocks for specific tasks in the processing elements. This is a unique architecture such that it can realize both task-level and packet-level parallelism. We also present an example of implementation of a fast adaptive routing algorithm, Cognitive Packet Networks (CPNs), using the proposed NPU architecture. CPN uses a neural network model with a reinforcement learning algorithm to find routes. The applicability of the CPN concept has been demonstrated through several software implementations. Through hardware implementation, we show that this new network model can sustain similar line rates as the current IP protocols. © 2006 Oxford University Press

Approximate Analysis Of Coupled Queueing For Self-Similar Video Traffic In Atm Networks

In this paper, we propose a dynamic scheduling method for self-similar variable bit rate video traffic in ATM networks. We analyze the proposed scheme in coupled queueing systems which have been used extensively in the modeling of computer and communication systems. Diffusion approximations methods are used to decouple a queueing system which represents an ATM network node into separate G/G/1 queues. Real MPEG video traces are used in discrete event simulation. Results are compared with the approximation, and are found to work very well under different traffic conditions

Hardware Implementation Of Random Neural Networks With Reinforcement Learning

In this paper, we present, a hardware implementation of a random neural network (RNN) model. The RNN, introduced by Gelenbe. is a spiked neural network model that possesses several mathematical properties such as the existence and uniqueness of the solution, and convergence of the learning algorithm. In particular, we discuss the implementation details for an RNN which uses a reinforcement learning algorithm. We also illustrate an example where this circuit implementation is used as a building block in a recently proposed novel network routing protocol called cognitive packet networks (CPN). CPN does not employ a routing table instead it relies on the RNN with a. reinforcement algorithm to route probing packets. © Springer-Verlag Berlin Heidelberg 2006

Performance Of Emi Based Mine Detection Using Back-Propagation Neural Networks

We propose and evaluate a neural network approach to mine detection using Electromagnetic Induction (EMI) sensors which provides a robust non-parametric approach. In our approach, a neural network with the well-known back-propagation learning algorithm combines the S-Statistic with the δ-Technique to discriminate between non-mine patterns and mines. Experimental results show that this approach reduces false alarms substantially over using just the δ-Technique or the energy detector

Performance Evaluation Of 3D-Interconnect Architectures For Network Line Cards

In this paper, we propose two off-chip interconnect architectures, called 3D-interconnects, to communicate between processing elements and memory modules located on network line cards. The goal of the 3D-interconnect architectures is to increase the throughput of the memory system since most currently deployed line card designs reach their maximum transfer rate. Moreover, line rates are constantly increasing while at the same time more data and functionality are embedded in each packet. The 3D-interconnect architectures allow multiple packet processing elements on a line card to access multiple memory modules. The novelty of the proposed interconnects is their application and implementation as off-chip interconnects on the line card board. Our interconnects includes multiple, highly efficient techniques to route, switch, and control packet flows in order to minimize congestion spots within the interconnects and packet loss. Performance results show that both interconnect designs achieve high throughput, low latency results surpassing other common interconnects currently deployed. Moreover, the interconnects were able to sustain high traffic load while keeping low failure rates and high bandwidth utilization levels

A Packet Processor For A Learning-Based Routing Protocol

As the Internet expands significantly in number of users, servers, routers and other networking products, the IP based network architecture must evolve and change. There are already proposed alternative packet-switched network models that would eliminate some of the problems of IP based networks. Recently proposed Cognitive Packet Networks (CPN) is one of them and it shows similarity with the discrete active networks. CPN uses a neural network model with a reinforcement learning algorithm to find routes. In this paper, we present a packet processor architecture which supports this fast adaptive routing algorithm. Copyright 2005 ACM