Determinants of the Optimal Network Configuration and the Implications forCoordination

2012 ◽  
Vol 2012 (1) ◽  
pp. 12291
Author(s):  
Patricia Deflorin ◽  
Helmut Dietl ◽  
Markus Lang ◽  
Eric Lucas
Keyword(s):  
Network Configuration ◽  
Optimal Network

Configuring a seismographic network for optimal monitoring of fault lines and multiple sources

1995 ◽  
Vol 85 (6) ◽  
pp. 1847-1857
Author(s):  
David M. Steinberg ◽  
Nitzan Rabinowitz ◽  
Yair Shimshoni ◽  
Daphna Mizrachi
Keyword(s):  
Statistical Theory ◽  
Experimental Design ◽  
Point Source ◽  
High Precision ◽  
Optimality Criterion ◽  
Network Configuration ◽  
Multiple Sources ◽  
Optimal Monitoring ◽  
Optimal Network ◽  
Seismographic Network

Abstract The geometric configuration of a seismographic network has important consequences for the ability to determine hypocenters with high precision. We present a method for optimal configuration when the network must monitor a system of faults. Our optimality criterion is drawn from the statistical theory of experimental design and can be effeciently maximized using an extension of the DETMAX algorithm. Our work generalizes that of Rabinowitz and Steinberg (1990), which treated the problem of optimal network configuration for monitoring a point source.

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.

scholarly journals A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sheetal N. Ghorpade ◽  
Marco Zennaro ◽  
Bharat S. Chaudhari ◽  
Rashid A. Saeed ◽  
Hesham Alhumyani ◽  
...  
Keyword(s):  
Network Configuration ◽  
Optimal Network ◽  
Industrial Iot ◽  
Quantum Pso

scholarly journals Optimal electric distribution network configuration using adaptive sunflower optimization

2021 ◽  
Vol 10 (4) ◽  
pp. 1777-1784
Author(s):  
Thuan Thanh Nguyen ◽  
Ngoc Thiem Nguyen ◽  
Trung Dung Nguyen
Keyword(s):  
Distribution System ◽  
Power Loss ◽  
Network Reconfiguration ◽  
Network Configuration ◽  
Solution Quality ◽  
Electric Distribution Network ◽  
Optimal Network ◽  
Comparison Results ◽  
Power Loss Reduction ◽  
Powerful Approach

Network reconfiguration (NR) is a powerful approach for power loss reduction in the distribution system. This paper presents a method of network reconfiguration using adaptive sunflower optimization (ASFO) to minimize power loss of the distribution system. ASFO is developed based on the original sunflower optimization (SFO) that is inspired from moving of sunflower to the sun. In ASFO, the mechanisms including pollination, survival and mortality mechanisms have been adjusted compared to the original SFO to fit with the network reconfiguration problem. The numerical results on the 14-node and 33-node systems have shown that ASFO outperforms to SFO for finding the optimal network configuration with greater success rate and better obtained solution quality. The comparison results with other previous approaches also indicate that ASFO has better performance than other methods in term of optimal network configuration. Thus, ASFO is a powerful method for the NR.

scholarly journals Determinants of the Optimal Network Configuration and the Implications for Coordination

2011 ◽  
Author(s):  
Patricia Deflorin ◽  
Helmut M. Dietl ◽  
Markus Lang ◽  
Eric Lucas
Keyword(s):  
Network Configuration ◽  
Optimal Network
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