scholarly journals Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques

2021 ◽  
Vol 11 (3) ◽  
pp. 1241
Author(s):  
Sergio D. Saldarriaga-Zuluaga ◽  
Jesús M. López-Lezama ◽  
Nicolás Muñoz-Galeano
Keyword(s):  
Unsupervised Learning ◽  
Distributed Generation ◽  
Network Topology ◽  
International Electrotechnical Commission ◽  
Number Of Clusters ◽  
Learning Techniques ◽  
Topology Changes ◽  
Network Topologies ◽  
Optimal Coordination ◽  
Operational Modes

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.

scholarly journals Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

2019 ◽  
Author(s):  
Paria Rezaeinia ◽  
Kim Fairley ◽  
Piya Pal ◽  
François G. Meyer ◽  
R. McKell Carter
Keyword(s):  
Psychiatric Disorders ◽  
Task Performance ◽  
Network Topology ◽  
Brain Network ◽  
Imaging Data ◽  
Hierarchical Processing ◽  
Functional Brain Network ◽  
Topology Changes ◽  
The World ◽  
Network Topologies

ABSTRACTA central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting-state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting-scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.AUTHOR SUMMARYOur mental lives are made up of a series of predictions about the world calculated by our brains. The calculations that produce these predictions are a result of how areas in our brain interact. Measures based on graph representations can make it clear what information can be combined and therefore help us better understand the computations the brain is performing. We make use of cutting-edge techniques that overcome a number of previous limitations to identify specific shapes in the functional brain network. These shapes are similar to hierarchical processing streams which play a fundamental role in cognitive neuroscience. The importance of these structures and the technique is highlighted by how they change under different task constraints and in individuals diagnosed with psychiatric disorders.

scholarly journals An Approach for Optimal Coordination of Over-Current Relays in Microgrids with Distributed Generation

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1740 ◽  
Author(s):  
Sergio D. Saldarriaga-Zuluaga ◽  
Jesús M. López-Lezama ◽  
Nicolás Muñoz-Galeano
Keyword(s):  
Network Security ◽  
Distributed Generation ◽  
Single Type ◽  
Renewable Distributed Generation ◽  
High Penetration ◽  
Decision Variables ◽  
Optimal Coordination ◽  
New Challenges ◽  
Operational Modes ◽  
Selection Of

The ever increasing presence of renewable distributed generation (DG) in microgrids is imposing new challenges in protection coordination. The high penetration of renewable DG enables microgrids to operate under different topologies, giving rise to bidirectional power flows and in consequence, rendering traditional coordination approaches inappropriate to guarantee network security. This paper proposes an approach for the optimal coordination of directional over-current relays (OCRs) in microgrids that integrate renewable DG and feature several operational modes. As a main contribution, the characteristic curves of directional OCRs are considered to be decision variables, instead of fixing a single type of curve for all relays as considered in previous works. The proposed approach allows for the selection of several IEC and IEEE curves which combination results in the best protection coordination. Several tests were carried out on an IEC benchmark microgrid in order to show the applicability of the proposed approach. Furthermore, a comparison with other coordination approaches evidenced that the proposed approach is able to find lower operation times and, at the same time, guarantee the suitable operation of protections under different condition faults and operational modes.

scholarly journals Identifying brain network topology changes in task processes and psychiatric disorders

2020 ◽  
Vol 4 (1) ◽  
pp. 257-273
Author(s):  
Paria Rezaeinia ◽  
Kim Fairley ◽  
Piya Pal ◽  
François G. Meyer ◽  
R. McKell Carter
Keyword(s):  
Psychiatric Disorders ◽  
Task Performance ◽  
Network Topology ◽  
Brain Function ◽  
Dynamic Networks ◽  
Brain Network ◽  
Imaging Data ◽  
Topology Changes ◽  
Network Topologies ◽  
Resting State Connectivity

A central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.

Islanding Detection in Distributed Generation using Unsupervised Learning Techniques

2018 ◽  
Vol 16 (1) ◽  
pp. 118-125 ◽  
Author(s):  
Bruno Martins Biaz ◽  
Vitor Hugo Ferreira ◽  
Marcio Zamboti Fortes ◽  
Thales Terrola Lopes ◽  
Gilson Brito Alves Lima
Keyword(s):  
Unsupervised Learning ◽  
Distributed Generation ◽  
Islanding Detection ◽  
Learning Techniques

scholarly journals Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-2
Author(s):  
Anke Meyer-Baese ◽  
Sylvain Lespinats ◽  
Juan Manuel Gorriz Saez ◽  
Olivier Bastien
Keyword(s):  
Unsupervised Learning ◽  
Learning Techniques

Analysis of Wireless Mesh Network Routing

2014 ◽  
Vol 989-994 ◽  
pp. 4629-4632
Author(s):  
Xiao Long Tan ◽  
Jia Zhou ◽  
Wen Bin Wang
Keyword(s):  
Network Topology ◽  
Routing Protocol ◽  
Wireless Mesh Network ◽  
Network Routing ◽  
Mesh Network ◽  
Mac Layer ◽  
Key Factors ◽  
Wireless Mesh ◽  
Topology Changes ◽  
Communication Links

Since the wireless mesh network topology dynamics and the radio channels instable, the design of wireless mesh network routing protocol become one of the key factors to determine the performance. This paper mainly studied the existing several kinds of typical three-layer mesh network routing protocol (DSDV and AODV), aimed at the defects of three-layer routing limited to the network topology changes, the paper proposed a network model based on two-layer routing. Forwarding the packet, establishing and maintaining the communication links are accomplished on the Mac layer. Simulation tests showed that two-layer routing has a big improvement on the efficiency of packet forwarding, and it effectively reduced the routing overhead and end-to-end delay simultaneously.

scholarly journals A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
J. Avilés ◽  
J. C. Mayo-Maldonado ◽  
O. Micheloud
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Optimization Technique ◽  
Optimal Number ◽  
Optimal Placement ◽  
Evolutionary Approach ◽  
Power Losses ◽  
Network Configuration ◽  
Optimal Network ◽  
Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

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