Multiplicative improvements in network reliability bounds

Networks ◽  
1989 ◽  
Vol 19 (5) ◽  
pp. 521-529 ◽  
Author(s):  
Timothy B. Brecht ◽  
Charles J. Colbourn
Keyword(s):  
Network Reliability ◽  
Reliability Bounds

scholarly journals Chip firing and all-terminal network reliability bounds

2009 ◽  
Vol 6 (4) ◽  
pp. 436-445 ◽  
Author(s):  
Jason I. Brown ◽  
Charles J. Colbourn ◽  
Richard J. Nowakowski
Keyword(s):  
Network Reliability ◽  
Reliability Bounds ◽  
Chip Firing

scholarly journals THE TREE CUT AND MERGE ALGORITHM FOR ESTIMATION OF NETWORK RELIABILITY

2003 ◽  
Vol 17 (1) ◽  
pp. 23-45 ◽  
Author(s):  
K.-P. Hui ◽  
N. Bean ◽  
M. Kraetzl ◽  
D. Kroese
Keyword(s):  
Monte Carlo ◽  
Network Reliability ◽  
Hybrid Scheme ◽  
Monte Carlo Techniques ◽  
Reliability Bounds ◽  
Time Space ◽  
Speed Up ◽  
Magnitude Improvement

This article presents Monte Carlo techniques for estimating network reliability. For highly reliable networks, techniques based on graph evolution models provide very good performance. However, they are known to have significant simulation cost. An existing hybrid scheme (based on partitioning the time space) is available to speed up the simulations; however, there are difficulties with optimizing the important parameter associated with this scheme. To overcome these difficulties, a new hybrid scheme (based on partitioning the edge set) is proposed in this article. The proposed scheme shows orders of magnitude improvement of performance over the existing techniques in certain classes of network. It also provides reliability bounds with little overhead.

An Algorithm for Network Reliability Bounds

1990 ◽  
Vol 2 (4) ◽  
pp. 336-345 ◽  
Author(s):  
Che-Liang Yang ◽  
Peter Kubat
Keyword(s):  
Network Reliability ◽  
Reliability Bounds

scholarly journals Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3242
Author(s):  
Hamid Mirshekali ◽  
Rahman Dashti ◽  
Karsten Handrup ◽  
Hamid Reza Shaker
Keyword(s):  
Fault Location ◽  
Network Reliability ◽  
Distribution Network ◽  
Estimation Method ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Active Power ◽  
Financial Loss ◽  
Smart Meters ◽  
The Real

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

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