51,514 research outputs found
Identification of Low-Voltage Distribution Network Attribution Relationship and Phase Information Based on Density Clustering
[Introduction] The correct topology information recorded by the power supply department can help the staff monitor the power grid information, analyze the faults, and optimize the operation of the power grid to meet the needs of lean and intelligent management of low-voltage distribution networks. At present, the addition of various new types of electricity-using equipment and users has caused the low-voltage distribution network structure to show a continuous change in characteristics, and the line maintenance cost is greatly increased. [Method] Therefore, the identification method of low-voltage distribution network attribution relationship based on density clustering was proposed. First, the effective voltage data collected by smart meters were extracted to generate a high-dimensional time-series voltage matrix. Then, the t-distributed Stochastic Neighbor Embedding algorithm (t-SNE) and Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) were applied to cluster the voltage data to achieve identification of low-voltage distribution network attribution relationship. Finally, the actual data of a low-voltage distribution network in Sanya City, Hainan Province were analyzed, and the proposed method is compared with other mainstream topology identification methods. [Result] The analysis results show that the proposed method can achieve more than 95% of identification accuracy, which is higher than other mainstream topology identification methods. [Conclusion] The proposed method is effective and advantageous in solving such problems, and can provide reference for practical engineering applications and offer a different research idea in the field of topology identification of low-voltage distribution network
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
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
Discovering the network topology: an efficient approach for SDN
Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS), among many others. Recent technologies like Software-Defined Networks (SDN) have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP) is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.Peer ReviewedPostprint (published version
On the Exploitation of Admittance Measurements for Wired Network Topology Derivation
The knowledge of the topology of a wired network is often of fundamental
importance. For instance, in the context of Power Line Communications (PLC)
networks it is helpful to implement data routing strategies, while in power
distribution networks and Smart Micro Grids (SMG) it is required for grid
monitoring and for power flow management. In this paper, we use the
transmission line theory to shed new light and to show how the topological
properties of a wired network can be found exploiting admittance measurements
at the nodes. An analytic proof is reported to show that the derivation of the
topology can be done in complex networks under certain assumptions. We also
analyze the effect of the network background noise on admittance measurements.
In this respect, we propose a topology derivation algorithm that works in the
presence of noise. We finally analyze the performance of the algorithm using
values that are typical of power line distribution networks.Comment: A version of this manuscript has been submitted to the IEEE
Transactions on Instrumentation and Measurement for possible publication. The
paper consists of 8 pages, 11 figures, 1 tabl
- …